Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the accuracy of three classical density functional theories of the electrical double layer.

Classical density functional theory (DFT) is a useful tool to compute the structure of the electrical double layer because it includes ion-ion correlations due to excluded-volume effects (i.e., steric correlations) and ion screening effects (i.e., electrostatic correlations beyond the electrostatic mean-field potential). This paper systematically analyzes the accuracies of three different electrostatic excess free-energy functionals, as compared to Monte Carlo (MC) simulations of the planar electrical double layer, over a large parameter space. Specifically, we tested the reference fluid density (RFD) [Gillespie et al., J. Phys.: Condens. Matter 14, 12129 (2002)10.1088/0953-8984/14/46/317], functionalized mean spherical approximation (fMSA) [Roth and Gillespie, J. Phys.: Condens. Matter 28, 244006 (2016)10.1088/0953-8984/28/24/244006], and bulk fluid (BF) [Kierlik and Rosinberg, Phys. Rev. A 44, 5025 (1991)10.1103/PhysRevA.44. 5025; Y. Rosenfeld, J. Chem. Phys. 98, 8126 (1993)10.1063/1.464569] functionals. Previous work compared these DFT methods to MC simulations only for a small set of parameters. Here, a total of twelve different cations were studied, with valences of +1, +2, and +3 and ion diameters of 0.15, 0.30, 0.60, and 0.90 nm at bulk concentrations between 1 μM and 1 M. The anion always had valence -1 and diameter 0.30 nm. The structure of the double layer of these charged, hard-sphere ions was computed for surface charges ranging from 0 to -0.50C/m^{2}. All the DFTs were compared against each other for all the parameters, as well as to 378 MC simulations. Overall, RFD was the best of the three functionals, while BF was the least accurate. fMSA performed significantly better than BF, making it a reasonable choice that is less computationally expensive than RFD. For monovalent cations, all three functionals worked reasonably well, except BF was qualitatively different from MC at very low surface charges. For multivalent cations, BF underestimated charge inversion while fMSA overestimated it. All DFTs performed poorly for small multivalent ions.
Reduction of spike generation frequency by cooling in brain slices from rats and from patients with epilepsy

This study aimed to understand the mechanism by which brain cooling terminates epileptic discharge. Cortical slices were prepared from rat brains (n = 19) and samples from patients with intractable epilepsy that had undergone temporal lobectomy (n = 7). We performed whole cell current clamp recordings at approximately physiological brain temperature (35℃) and at cooler temperatures (25℃ and 15℃). The firing threshold in human neurons was lower at 25℃ (−32.6 mV) than at 35℃ (−27.0 mV). The resting potential and spike frequency were similar at 25℃ and 35℃. Cooling from 25℃ to 15℃ did not change the firing threshold, but the resting potential increased from −65.5 to −54.0 mV and the waveform broadened from 1.85 to 6.55 ms, due to delayed repolarization. These changes enhanced the initial spike appearance and reduced spike frequency; moreover, spike frequency was insensitive to increased levels of current injections. Similar results were obtained in rat brain studies. We concluded that the reduction in spike frequency at 15℃, due to delayed repolarization, might be a key mechanism by which brain cooling terminates epileptic discharge. On the other hand, spike frequency was not influenced by the reduced firing threshold or the elevated resting potential caused by cooling.
Preparation and quantitative characterization of polydimethylsiloxane optical phantoms with zinc-phthalocyanine dye absorbers.

We described a method for the preparation of polydimethylsiloxane (PDMS) phantoms to mimic the optical properties of biologic tissues at distinct wavelengths ranging from the visible to the near-infrared spectra. The present method for fabricating solid optical tissue phantoms using zinc-phthalocyanine chromophores has demonstrated high photostability with optical absorption coefficients up to 1.0 mm-1, making this phantom proper with absorption bands ranging from 600 to 850 nm. It also happens that the chromophore absorption coefficient is linear as a function of its concentration inside the previous optical window. The optical scattering properties were quantitatively selected by adding TiO2 particle concentrations to the PDMS phantom. Thus, the quantitative optical properties of absorption and scattering for a large-batch fabrication were demonstrated, making the zinc-phthalocyanine phantoms suitable for use as a reference standard.
False Secret Keys to Disturb Power Analysis

Protecting against power analysis on block cipher implementations is no longer optional but mandatory. One of the most well-known techniques to prevent power analysis is higherorder masking which randomizes sensitive intermediate values by using masks picked randomly for each execution of cryptographic algorithms. Because of its slow performance however, we need to find a more simple way. In this paper, we present a new method for achieving this goal. In the presence of deceiving runs of encryption using several false keys, we aim to disturb power analysis. Our experiment finds out how many false keys are required to disturb power analysis.
Privacy Preserving Average Consensus with Different Privacy Guarantee

Privacy preserving average consensus problem concerns protecting the initial states of participants not to be disclosed while achieving average consensus. In existing literatures, most of works consider providing privacy guarantee for agents when all the other participants are viewed as the same type of privacy attackers. However, for an agent, not all the other participants are untrustworthy. The agent can set weaker privacy demand against these credible participants to obtain potential utility improvement. In this paper, we consider that network is composed of several groups. Within each group, agents treat participants within and outside the group as two types of attackers. Then, a private average consensus algorithm (PACA) is proposed to provide different privacy protection against participants within and outside the group, by perturbing agents' initial states with random noises. Based on the definition of $\varepsilon$-KL privacy, we prove that PACA preserves distinct degrees of privacy guarantee against agents within and outside the group. Moreover, the convergence accuracy is analyzed according to the deviation between the final value and the true average one. Finally, simulations are conducted to evaluate the performance of PACA.
Chasing the addicted engram: identifying functional alterations in Fos-expressing neuronal ensembles that mediate drug-related learned behavior

Given that addiction has been characterized as a disorder of maladaptive learning and memory, one critical question is whether there are unique physical adaptations within neuronal ensembles that support addiction-related learned behavior. The search for the physical mechanisms of encoding these and other memories in the brain, often called the engram as a whole, continues despite decades of research. As we develop new technologies and tools that allow us to study cue- and behavior-activated Fos-expressing neuronal ensembles, the possibility of identifying the engrams of learning and memory is moving into the realm of reality rather than speculation. It has become clear from recent studies that there are specific functional, electrophysiological alterations unique to Fos-expressing ensemble neurons that may participate in encoding memories. The ultimate goal is to identify the addicted engram and reverse the physical changes that support this maladaptive form of learning.
State and Parameter Estimation for a Class of Nonlinearly Parameterized Systems Using Sliding Mode Techniques

In this study, a class of nonlinear parameterized systems is considered where the unknown parameters are parameterized nonlinearly. A stability criteria for time-varying systems is developed based on Perron-Frobenius theorem, and used for designing observers. A particular sliding mode observer with an update law, which can ensure that the sliding motion converges to zero asymptotically, is designed to estimate states and unknown parameters. The developed result is applied to a three-phase inverter system used by China high-speed trains to verify the effectiveness.
Software Defined Radio Controller Using Bluetooth

We present a Bluetooth-based mobile application to support and enhance internal and external interfaces to software defined radio platforms. For this application, a control message protocol used between software defined radio platform and smart devices is designed and implemented with examples of various control flows. With enhanced controllability and smart device connectivity through Bluetooth, versatile applications such as connected audio applications and in-vehicle infotainment systems can be realized.
An Ensemble Extreme Learning Machine for Data Stream Classification

Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time.
Input-to-State Stability for the Control of Stefan Problem with Respect to Heat Loss at the Interface

This paper develops an input-to-state stability (ISS) analysis of the one-phase Stefan problem with a prescribed heat loss at the liquid-solid interface. We focus on the closed-loop system under the control law proposed in [7] which is designed to stabilize the interface position at a desired position for the one-phase Stefan problem without the heat loss. The problem is modeled by a 1-D diffusion Partial Differential Equation (PDE) defined on a time-varying spatial domain described by an ordinary differential equation (ODE) with a time-varying disturbance. The well-posedness and some positivity conditions of the closed-loop system are proved based on an open-loop analysis. The closed-loop system with the designed control law satisfies an estimate of $L_{2}$ norm in a sense of ISS with respect to the unknown heat loss.
Zero Step Capturability for Legged Robots in Multicontact

The ability to anticipate a fall is fundamental for any robot that has to balance. Currently, fast fall-prediction algorithms only exist for simple models, such as the linear inverted pendulum model (LIPM), whose validity breaks down in multicontact scenarios (i.e., when contacts are not limited to a flat ground). This paper presents a fast fall-prediction algorithm based on the point-mass model, which remains valid in multicontact scenarios. The key assumption of our algorithm is that, in order to come to a stop without changing its contacts, a robot only needs to accelerate its center of mass in the direction opposite to its velocity. This assumption allows us to predict the fall by means of a convex optimal control problem, which we solve with a fast custom algorithm (less than 11 ms of computation time). We validated the approach through extensive simulations with the humanoid robot HRP-2 in randomly-sampled scenarios. Comparisons with standard LIPM-based methods demonstrate the superiority of our algorithm in predicting the fall of the robot, when controlled with a state-of-the-art balance controller. This paper lays the foundations for the solution of the challenging problem of push recovery in multicontact scenarios.
Towards Natural Language Empowered Interactive Data Analysis

The recent advances in natural language based interaction methodologies offer promising avenues to enhance the interactive processes within the human-machine dialogue of visual analytics. We envisage \textit{Multimodal Data Analytics} as a novel approach for conducting data analysis that builds on the strengths of visual analytics and natural language as an expressive interaction channel. We investigate the potential enhancements from such a multimodal approach and discusses the preliminary outline for a structured methodology to study the role of natural language in data analytics. Our approach builds on a simple model of human machine dialogue for interactive data analysis which we then propose to instantiate as visual analytics workflows -- representations to study and operationalise interactive data analysis routines empowered by natural language interaction.
Closed-loop guidance of mobile sensors for the estimation of spatially distributed processes

The paper provides a computationally efficient scheme for the spatial repositioning of mobile sensors used in the real-time estimation of spatially distributed processes. Instead of considering the computationally expensive option of propagating the covariance operator forward in time with the guidance propagated backward in time, the proposed closed-loop guidance scheme repositions the mobile sensors according to the positive direction of the spatial gradient of the resulting state estimation error. The proposed coupled state estimation and sensor guidance is applied to advection-diffusion PDEs in one and two spatial dimensions and compared to the case of a Kalman filter with fixed-in-space sensors.
Diatom Silica for Biomedical Applications: Recent Progress and Advances

Diatoms are unicellular photosynthetic algae enclosed in porous 3D nanopatterned silica enclosures called “frustules. ” The diatom frustules are made from biosilica self‐assembled into intricate porous shells that feature unique properties including high specific surface area, biocompatibility, tailorable surface chemistry, thermal stability, and high mechanical and chemical resistance. The ability to cultivate diatoms in artificial environments and their abundant availability of diatom frustules as mineable fossilized mineral deposits (diatomite or diatomaceous earth; DE) make diatom silica a promising natural alternative to synthetic porous silica for a broad range of biomedical, environmental, agricultural, and energy applications. This review article provides a comprehensive and current account of the use of natural DE silica materials in biomedical applications focused mainly on drug delivery with some highlights on biosensing, tissue engineering, and clotting agents. The article also covers some basic physical and chemical aspects of DE material such as purification, surface chemical functionalization, biocompatibility, and cellular uptake that are critical for the development of an efficient drug carrier.
Klimawandel in der thorakalen Onkologie

Zusammenfassung In den letzten Jahren haben sich die Diagnose und die medizinische Behandlung von Lungenkarzinompatienten erheblich gewandelt. Auch wenn die Mehrzahl der Patienten weiterhin nicht von der Erkrankung geheilt wird, so können durch verschiedene, klug abgestimmte Therapieansätze eine deutlich längere Krankheitskontrolle und stark verlängerte Überlebenszeiten als noch vor wenigen Jahren erreicht werden. Diese Entwicklung ist nicht nur durch medizinische, sondern auch zahlreiche politische und organisatorische Aspekte charakterisiert. Diese verschiedenen Ebenen, die klinischen Konsequenzen und die resultierenden Herausforderungen werden im Folgenden kurz skizziert.
Validation of MPTCP Performance Enhancement Algorithm in Real PS-LTE Environment

Reliability is very important factor in disaster service than any others. Connection delays or interruptions in providing disaster services can lead to catastrophic consequences. However, in the event of a disaster, there may be a temporary or permanent transmission path break in data communication due to various reasons. Therefore, in order to improve the reliability and performance of the disaster service in the disaster communication network, our study is conducted to examine the Constraint-based Proactive Scheduling(CP-Scheduling) algorithm [1] and the feedback-based path failure detection(FPF) algorithm [2] which are applied for MPTCP in PS-LTE Environment.
A Matlab Software Tool for Semi-Automatic Detection of R Peak in Noisy Electrocardiogram Signal

This paper presents a Matlab software tool for detecting R peaks in noisy electrocardiogram signal (ECG). The software is designed to facilitate both automated and manual R peak detection. Automated R peak detection can be done in batch processing mode using any preferred R peak detection algorithm. Due to a well-designed graphic user interface, viewing the ECG signal and R peak detection results of a large ECG dataset is effortless. Deleting faulty detected or adding some missing R peaks manually is also simplified. This software is appropriate for researchers who require low R peak detection error rate without inventing or utilizing a complex R peak detection algorithm.
Current Significance of IT-Infrastructure Enabling Industry 4.0 in Large Companies

An appropriate IT-Infrastructure is the basic condition for using Big Data processes in the context of Industry 4.0. Therefore, the objective of this study is to describe the current state of implementation of Big Data relating IT-Infrastructure in large companies. Through three pilot case studies the realization of Industry 4.0 regarding the topics Data Exchange, Cloud Solutions, Data Analysis, Production supported by IT, IT-Security and ERP-Integration in companies is analysed. The sources of information for the case studies were interviews and desktop research. The different cases are compared to the state of the art as described in the literature. Generally, it can be detected, that all three cases are competitive in their industry sectors with regards to IT-Infrastructure. Facts as described in literature can be transferred to the cases. For instance, the horizontal integration of data exchange can be reflected in practice, where an advanced implementation level can be identified. However, there are contradictions with regard to standardized transmission standards as e.g. OPC-UA, a standard for vertical data exchange, as well. The cases can be seen as a sample for enhancing the individual implementation of Big Data processes by improving the IT-Infrastructure.
Improved Chord Recognition by Combining Duration and Harmonic Language Models

Chord recognition systems typically comprise an acoustic model that predicts chords for each audio frame, and a temporal model that casts these predictions into labelled chord segments. However, temporal models have been shown to only smooth predictions, without being able to incorporate musical information about chord progressions. Recent research discovered that it might be the low hierarchical level such models have been applied to (directly on audio frames) which prevents learning musical relationships, even for expressive models such as recurrent neural networks (RNNs). However, if applied on the level of chord sequences, RNNs indeed can become powerful chord predictors. In this paper, we disentangle temporal models into a harmonic language model---to be applied on chord sequences---and a chord duration model that connects the chord-level predictions of the language model to the frame-level predictions of the acoustic model. In our experiments, we explore the impact of each model on the chord recognition score, and show that using harmonic language and duration models improves the results.
Flatness-based constrained optimal control of reaction-diffusion systems

In recent years flatness-based techniques for trajectory planning have been extended to partial differential equations (PDEs). Herein a state and input parametrization is determined by means of a so-called flat or basic output and its derivatives. While this enables a systematic solution of the trajectory planning problem it does not allow to immediately incorporate constraints or the inclusion of optimization criteria such as energy or time optimality. In this contribution, we extend the flatness approach for PDEs by providing a technique to efficiently address input and state constraints as well as the minimization of cost functionals. For this, a constrained dynamic optimization problem is formulated based on an integrator chain representing the flat output trajectory and its derivatives. Herein, the highest derivative serves as the decision variable and input to the integrator chain. By using a discrete time formulation the dynamic optimization problem is transferred to a static problem, which can be efficiently solved using, e.g., interior-point methods. This results in a flatness-based constrained optimal control approach, that does not require to solve any partial differential equation. Simulation results are presented for a scalar reaction-diffusion system to illustrate the performance of this approach.
Liposomal Curcumin Attenuates the Incidence of Oxidative Stress, Inflammation, and DNA Damage Induced by Copper Sulfate in Rat Liver

Background: Copper is an essential element that is used widely in agriculture as fungicides and insecticides; for example, it is used to control schistosomiasis and as an antiseptic and germicide. Copper sulfate (CuSO4) induces multiorgan dysfunction through the stimulation of reactive oxygen species and oxidative stress. Despite the numerous pharmacological effects of curcumin (CUR), its pharmacokinetic properties are less promising. Hence, there is an urgent need for novel, effective strategies to attenuate heavy metal toxicity and consequently improve the treatment efficiency. Liposomal curcumin (L-CUR) improves the dissolution, stability, and bioavailability of treatment agents. This study compared the efficacy of CUR and L-CUR with that of desferrioxamine (DES), which is a heavy metal chelator against CuSO4 hepatotoxicity. Methods: All treatments with the aforementioned antioxidants were administered for 7 days along with CuSO4. Serum levels of alanine aminotransferase, aspartate transaminase, lactate dehydrogenase, and C-reactive protein, hepatic nitric oxide (NO), and lipid peroxides (malondialdehyde) were measured; protein expression of cyclooxygenase 2 and DNA fragmentation were evaluated. Histopathological examinations were also conducted. Results: A toxic dose of CuSO4 induced elevations in the previously measured parameters; these increases were reduced by the tested antioxidants, whereas glutathione (GSH) and superoxide dismutase (SOD) levels were decreased. Treatment with the antioxidants in question modulated these levels. Liposomal CUR has more hepatoprotective efficiency than CUR, and its efficacy was similar to that of DES. The histopathological examinations confirmed these results. Conclusions: Liposomal CUR may be useful for the prevention of CuSO4-induced liver injury. Cyclooxygenase 2 protein expression and DNA fragmentation were involved in CuSO4 toxicity and treatment.