Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of optically actuated MRI-compatible active needles for medical interventions

The development of a Magnetic Resonance Imaging (MRI) compatible optically-actuated active needle for guided percutaneous surgery and biopsy procedures is described. Electrically passive MRI-compatible actuation in the small diameter needle is provided by non-magnetic materials including a shape memory alloy (SMA) subject to precise fiber laser operation that can be from a remote (e.g., MRI control room) location. Characterization and optimization of the needle is facilitated using optical fiber Bragg grating (FBG) temperature sensors arrays. Active bending of the needle during insertion allows the needle to be accurately guided to even relatively small targets in an organ while avoiding obstacles and overcoming undesirable deviations away from the planned path due to unforeseen or unknowable tissue interactions. This feature makes the needle especially suitable for use in image-guided surgical procedures (ranging from MRI to CT and ultrasound) when accurate targeting is imperative for good treatment outcomes. Such interventions include reaching small tumors in biopsies, delineating freezing areas in, for example, cryosurgery and improving the accuracy of seed placement in brachytherapy. Particularly relevant are prostate procedures, which may be subject to pubic arch interference. Combining diagnostic imaging and actuation assisted biopsy into one treatment can obviate the need for a second exam for guided biopsy, shorten overall procedure times (thus increasing operating room efficiencies), address healthcare reimbursement constraints and, most importantly, improve patient comfort and clinical outcomes.
Nonlinear gain dynamics in quantum-dot optical amplifiers and its application to optical communication devices

Ultrafast gain dynamics in quantum-dot (QD) optical amplifiers has been studied. It was found that there are at least three nonlinear processes, which are attributed to carrier relaxation to the ground states, phonon scattering, and carrier capture from the wetting layers into the QDs. The relevant time constants were evaluated to be /spl sim/90 fs, /spl sim/260 fs, and /spl sim/3 ps, respectively, under a 50-mA bias condition. The dephasing time was evaluated to be /spl sim/85 fs. The third-order optical susceptibility (/spl chi//sup (3)/) has been evaluated by means of both nonlinear transmission and four-wave mixing experiments. The results show that the nonlinearity expressed by /spl chi//sup (3)//g/sub 0/ is quite similar to that of bulk and quantum wells, which can be explained by similar relaxation times. Applications to optical communication devices are also discussed.
Biosensors: Electrochemical Devices—General Concepts and Performance

This review provides a general overview of different biosensors, mostly concentrating on electrochemical analytical devices, while briefly explaining general approaches to various kinds of biosensors, their construction and performance. A discussion on how all required components of biosensors are brought together to perform analytical work is offered. Different signal-transducing mechanisms are discussed, particularly addressing the immobilization of biomolecular components in the vicinity of a transducer interface and their functional integration with electronic devices. The review is mostly addressing general concepts of the biosensing processes rather than specific modern achievements in the area.
Data Augmentation, Internal Representation, and Unsupervised Learning

I am grateful to the editor for inviting me to contribute this discussion. I have learned a great deal from this exceedingly clever article by Yu and Meng. Ever since the groundbreaking work of Meng and van Dyk (1997), there have been many interesting developments in the art of data augmentation for both EM and MCMC. This article is yet another significant contribution to this line of research. While I feel I can contribute little to the discussion of the proposed method, I would like to mention a different perspective of data augmentation, in the hope of broadening the scope of the discussion. Data augmentation is not only a useful tool for MCMC, but it is also an essential ingredient in the so-called unsupervised learning, which involves augmenting latent variables or hidden units to explain the observed or visible data. In the context of neural science, the observed data are collected by the sensors in the form of images or sounds, and the latent variables or hidden units form the internal representations of the sensory data. The learning of such internal representations often does not require class labels or detailed annotations of the training examples, thus the learning is said to be unsupervised. Latent variable models abound in statistical literature, such as factor analysis, mixture model, t -model, random effects model, probit regression, hidden Markov model, just to name a few. In what follows, I shall briefly review two popular latent variable models in neural science and unsupervised learning, as well as their hierarchical extensions. The first model is the sparse coding model of Olshausen and Field (1996). Let Y = (y1, . . . , yM) be the M-dimensional vector, such as an image (where M is the number of pixels). Let Z = (z1, . . . , zK) be the K-dimensional vector of hidden units for representing Y . The model is of the following form: zk ∼ p(z) independently, (0.1)
The Chinese Version of the Caregiver Difficulties Scale: Psychometric Evaluation.

BACKGROUND Evaluating caregiver burden and its health impact is an essential components of long-term care plan for children with disabilities, the Caregiver Difficulties Scale (CDS) have high conceptual sensitivity. The aim of this study was to adapt the CDS to Chinese and investigates the psychometric properties of this tool. METHODS The study was carried out among caregivers of children with Cerebral Palsy (n=194). The CDS, Caregivers Burden Inventory (CBI), World Health Organization Quality of Life- BREF (WHOQOL -BREF) were used for data collection. Twenty experts were consulted to evaluate the content validity of the scale. The confirmatory factor analysis was conducted to measure the construct validity of CDS. The Spearman correlation coefficients were calculated among CDS, CBI and WHOQOL -BREF to examine the convergent validity and discriminant validity. The reliability was evaluated by examining internal consistency and test-retest reliability. RESULTS The result of expert consultation showed that the S-CVI was 0.894, and the I-CVI ranged from 0.70 to 1.00. The fit indices showed that the original correlated four-factor model of CDS was adequate: χ2 =268.397; df=243; χ2 /df=1.105; RMSEA=0.023; CFI=0.985; NNFI=0.869; TLI=0.982; IFI=0.986. The score of CDS were positively strong associated with the scores of CBI (r=+0.764); negatively correlating with the scores of WHOQOL-BREF (r =-0.627). The Cronbach's alpha was 0.840, intraclass correlation coefficient (ICC) value was 0.843. CONCLUSIONS The Chinese version of the CDS is a valid and reliable tool to evaluate burden for caregivers of children with CP in China.
Streamer head structure: role of ionization and photoionization

Results from experiments and numerical modelling of streamer propagation are presented. The 2D hydrodynamic numerical description of the pulsed discharge based on the local ionization and photoionization models adequately describes the streamer shape and dynamics over a wide range of pressures and voltages. This work presents a method for imaging the instantaneous emission distribution in the streamer head. A method for restoring the electrodynamic radius of the streamer head was developed on the basis of the streamer head images that were obtained with subnanosecond exposure time. The electrodynamic radius has been determined as the distance between the maxima of the electric field at the position where the streamer head transforms into the streamer channel. The dependence of the electrodynamic radius on voltage and pressure has been determined. We show that a 2D numerical model using hydrodynamic approximation predicts the streamer characteristics with an accuracy of about 15% in the 0.5–1 atmosphere pressure range and up to 40% in the 0.2–0.3 atmosphere pressure range for a voltage of U from 20 kV up to 40 kV in the 30 and 40 mm discharge gap.
In-plane and cross-plane thermal conductivity of graphene: applications in thermal interface materials

Heat removal became crucial issue for continuing progress in electronic, optoelectronic and photonic industries. Carbon allotropes and derivatives occupy a unique place in terms of their ability to conduct heat. The room-temperature thermal conductivity of carbon materials spans an extraordinary large range from the lowest in amorphous carbons to the highest in graphene. I review thermal properties of graphene, discuss distinctions between intrinsic and extrinsic thermal conductivity and describe prospects of graphene applications for thermal management in electronics and optoelectronics. ý
Smoothing and forecasting mortality rates

The prediction of future mortality rates is a problem of fundamental importance for the insurance and pensions industry. We show how the method of P-splines can be extended to the smoothing and forecasting of two-dimensional mortality tables. We use a penalized generalized linear model with Poisson errors and show how to construct regression and penalty matrices appropriate for two-dimensional modelling. An important feature of our method is that forecasting is a natural consequence of the smoothing process. We illustrate our methods with two data sets provided by the Continuous Mortality Investigation Bureau, a central body for the collection and processing of UK insurance and pensions data.
Bias and variance of angular correlation functions

We present a general method for calculating the bias and variance of estimators for w(θ) based on galaxy-galaxy (DD), random-random (RR), and galaxy-random (DR) pair counts and describe a procedure for quickly estimating these quantities given an arbitrary two-point correlation function and sampling geometry. These results, based conditionally upon the number counts, are accurate for both high and low number counts. We show explicit analytical results for the variances in the estimators DD/RR, DD/DR, which turn out to be considerably larger than the common wisdom Poisson estimate and report a small bias in DD/DR in addition to that due to the integral constraint. Further, we introduce and recommend an improved estimator (DD−2DR+RR)/RR, whose variance is nearly Poisson
Association of Leopard Cat Occurrence with Environmental Factors in Chungnam Province, South Korea

Simple Summary Understanding how environmental factors influence wildlife species is important for effective management. This study was conducted to address leopard cats’ distribution according to various environmental factors across Chungnam Province, South Korea, using two analytical approaches: classical statistical (i.e., logistic regression) and machine learning (i.e., boosted regression trees) methods. Results identified that higher leopard cat distribution was observed in the areas with lower elevation, closer to roads and water sources, and lower human population densities. The results also show that two methods can be used in a complementary manner for effective wildlife management. Abstract This study was conducted to investigate the association of leopard cat (Prionailurus bengalensis) occurrences and environmental factors in Chungnam Province, South Korea, using two different analytical approaches for binomial responses: boosted regression trees and logistic regression. The extensive field survey data collected through the Chungnam Biotope Project were used to model construction and analysis. Five major influential factors identified by the boosted regression tree analysis were elevation, distance to road, distance to water channel/body, slope and population density. Logistic regression analysis indicated that distance to forest, population density, distance to water, and diameter class of the forest were the significant explanatory variables. The results showed that the leopard cats prefer the areas with higher accessibility of food resources (e.g., abundance and catchability) and avoid the areas adjacent to human-populated areas. The results also implied that boosted regression and logistic regression models could be used in a complementary manner for evaluating wildlife distribution and management.
Learning-Aided Evolution for Optimization

Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this article proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on EC competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.
HOMOTOPY TYPE COMPARISON OF A SPACE WITH COMPLEXES ASSOCIATED WITH ITS OPEN COVERS

1. Introduction. This paper deals with the homotopy type comparison of a space with certain complexes (such as the nerve) associated with suitably "well pieced together" open covers of the space by homotopically trivial sets. The main theorem (Theorem 1) implies a slightly different version (Theorem 2) of a theorem of A. Weil [7]. The method of proof is quite different from Weil's. A simple use is made of a theorem in [6] of Alexandroff's [l] "discrete spaces" (spaces in which the intersection of any collection of open sets is open). Also use is made of a modification (Theorem 3) of a theorem
Studies on linear matrix inequality relaxations for fuzzy control systems via homogeneous polynomials

In this study, the authors investigate a relaxed condition characterised by parameter-dependent linear matrix inequality (PD-LMI) in terms of firing strength belonging to the unit simplex, exploiting the algebraic property of Polyas theorem to construct a family of finite-dimensional LMI relaxations. The main contribution of this study is that sets of relaxed LMI are parameterised in terms of the polynomial degree d . As d increases, progressively less conservative LMI conditions are generated, being easier satisfied owing to more freedom provided by new variables involved. Another protruding feature is that a verifiable necessary condition is derived. Furthermore, the new relaxation results for PD-LMI is shown to include and generalise all previous results on quadratic (common P ) stability approach. Lastly, numerical experiments for illustrating the advantage of relaxation, being less conservative and effective, are provided.
Porous parachute modelling with an Euler-Lagrange coupling

A newly developed approach for tridimensional fluid-structure interaction with a deformable thin porous media is presented. The method presented couples a Arbitrary Lagrange Euler formulation for the fluid dynamics and a updated Lagrangian finite element formulation for the thin porous medium dynamics. The interaction between the fluid and porous medium are handled by a Euler-Lagrange coupling, for which the fluid and structure meshes are superimposed without matching. The coupling force is computed with an Ergun porous flow model. As test case, the method is applied to an anchored air parachute placed in an air stream.
Characterization and analysis of thermoelectric transport in n-type Ba_(8)Ga_(16−x)Ge_(30+x)

The thermoelectric transport properties of polycrystalline, Ba_(8)Ga_(16−x)Ge_(30+x) were characterized from 300 to 1000 K. The carrier density was found to vary precisely with the experimental x as expected from simple electron counting. The experimental data are analyzed within the framework of a single parabolic band model, which is found to accurately describe transport for the compositions of interest for thermoelectric application. The lattice thermal conductivity, calculated with a degeneracy adjusted Lorenz number, does not show a trend with composition and a value of ~1 Wm^(−1) K^(−1) is observed at 300 K. A maximum figure of merit zT = 0.86 is obtained at 950 K, and the optimal doping level for thermoelectric application is predicted to be ~2 × 10^(20) cm^(−3), which corresponds to Ba_(8)Ga_(15.75)Ge_(30. 25_ by electron counting. An unexpected transition event is observed near 650 K, which results in a significant increase in the heat capacity.
Data-Mining Model-Based Enhanced Differential Relaying Scheme for Microgrids

The article presents a data-mining model-based enhanced differential relaying scheme for fault detection and classification task in the microgrids. The proposed research work aims to provide a fixed setting, fast, and reliable scheme for accurately detecting and classifying faults in the microgrid. The presented scheme fetches the three-phase current signals from both ends of the distribution lines and computes the differential current phasor, including the distributed generation (DG) in the fault model. The deep neural network (DNN)-based data-mining models for fault detection and classification are built using differential current phasor datasets generated by wide variations in fault and operating conditions of the microgrid, including different types of faults with the change in fault location, fault resistance, DG penetration in grid-connected and islanding mode of operation with types of DG units. The developed DNN models are validated for their ability to detect the faults in a low-voltage CERTS microgrid and subsequently for a medium-voltage IEEE-34 bus microgrid on a MATLAB/SIMULINK platform. Furthermore, the proposed intelligent scheme is also tested on the Typhoon hardware-in-loop platform for real-time performance evaluation. The accuracy and speed in detecting and classifying faults indicate that the proposed DNN-based intelligent differential relaying scheme can be a potential candidate for providing reliable protection measures for the microgrid.
Surface plasmon resonance-based gas sensor with chalcogenide glass and bimetallic alloy nanoparticle layer

A theoretical model for a gas sensor based on chalcogenide prism and surface plasmon resonance is proposed. The metal layer consists of Ag–Au alloy nanoparticles. The chalcogenide prism is made of sulfide glass Ge20Ga5Sb10S65, commonly known as 2S2G. The angular interrogation method is used to analyze the sensor’s performance in terms of its intrinsic sensitivity that takes into account both the angular shift as well as the angular width of plasmon resonance curve. The effect of operating wavelength along with the size and composition ratio of alloy nanoparticle is studied on sensor's performance. The sensor’s performance improves with an increase in nanoparticle size and silver fraction. Large operating wavelength is found to be useful for accurate gas sensing. Therefore, proposed sensor can be very useful for gaseous sensing in infrared due to large operating window of chalcogenide materials.A theoretical model for a gas sensor based on chalcogenide prism and surface plasmon resonance is proposed. The metal layer consists of Ag–Au alloy nanoparticles. The chalcogenide prism is made of sulfide glass Ge20Ga5Sb10S65, commonly known as 2S2G. The angular interrogation method is used to analyze the sensor’s performance in terms of its intrinsic sensitivity that takes into account both the angular shift as well as the angular width of plasmon resonance curve. The effect of operating wavelength along with the size and composition ratio of alloy nanoparticle is studied on sensor's performance. The sensor’s performance improves with an increase in nanoparticle size and silver fraction. Large operating wavelength is found to be useful for accurate gas sensing. Therefore, proposed sensor can be very useful for gaseous sensing in infrared due to large operating window of chalcogenide materials.
An a Priori Study for the Modelling of Subgrid-Scale Phenomena in the Interaction Between a Liquid Sheet and a Decaying Turbulence

Large Eddy Simulation (LES) of one-phase flow is widely used for industrial applications. However, LES of two-phase interfacial flows, i.e. two-phase flows where an interface separates liquid and gas phases, still remains a challenging task. The main issue is development of subgrid-scale models well suited for two-phase interfacial flows. Explicit filtering of 3D Direct Numerical Simulations has been employed to evaluate the order of magnitude of subgrid contributions. Two-phase interfacial flow simulations are carried out with an incompressible flow solver coupled to a ghost-fluid level-set interface capturing method. The originality of this work is the filtering of jump conditions at the interface. Subgrid terms of jump conditions are shown to derive from the filtering of both the pressure gradient term and the viscous term. Conclusions are drawn on the relative importance of the different subgrid scale contributions. These conclusions will contribute to develop subgrid-scale models which take into account the interface-turbulence coupling when LES are performed.Copyright © 2008 by ASME
The Satellite DNA Catalogues of Two Serrasalmidae (Teleostei, Characiformes): Conservation of General satDNA Features over 30 Million Years

Satellite DNAs (satDNAs) are tandemly repeated sequences that are usually located on the heterochromatin, and the entire collection of satDNAs within a genome is called satellitome. Primarily, these sequences are not under selective pressure and evolve by concerted evolution, resulting in elevated rates of divergence between the satDNA profiles of reproductive isolated species/populations. Here, we characterized two additional satellitomes of Characiformes fish (Colossoma macropomum and Piaractus mesopotamicus) that diverged approximately 30 million years ago, while still retaining conserved karyotype features. The results we obtained indicated that several satDNAs (50% of satellite sequences in P. mesopotamicus and 43% in C. macropomum) show levels of conservation between the analyzed species, in the nucleotide and chromosomal levels. We propose that long-life cycles and few genomic changes could slow down rates of satDNA differentiation.