Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Interferometry for Different Energy Landscapes in a Tuneable Josephson Junction Circuit

This paper presents a simple Josephson-junction circuit with two parameters (inductance and capacitance) which can be tuned to represent different energy landscapes with different physical properties. By tuning this quantum circuit through external accessible elements we can move from two to three and more energy levels depending on the parameter setting. The inductance, the capacitance as well as the external voltage (driving terms) condition the number of relevant energy levels as well as the model to be used. We show that the quantized circuit represents a multi-state system with tunneling induced by the Landau-Zener and Landau-Zener-Stuckelberg transition. The special cases of single crossing and multi-crossing models are thoroughly studied and the transition probability is obtained in each case. It is proven that, the crossing time as well as the relaxation time affect drastically the transition probability; the system mimics a single passage for short relaxation and a multiple passage problem for large relaxation. The nonlinearity of energy levels modifies the transition probability and the derived adiabatic parameters help to redefine the Landau-Zener probability. The observed constructive and destructive interferences are parametrically conditioned by the initial condition set by the inductive branch. Moreover, the total population transfers as well as the complete blockage of the system are obtained in a permissible range of parameters only by changing the values of the inductance. Therefore, the system models a controllable level-crossing where the additional branches (inductive and capacitive) help in designing the number of states, the type of interferometry as well as the control of states occupation.
A Review of Nudges: Definitions, Justifications, Effectiveness

In an influential book published in 2008, Thaler and Sunstein suggested a novel approach to policy making based on the notion of a ‘nudge. ’ Roughly speaking, a nudge is defined as an aspect of the decisional context that steers people’s decisions by acting on their cognitive biases. The book generated an intense debate, over the course of which concerns were raised about: (1) the exact definition of nudges, (2) their ethical justifiability, and (3) their effectiveness. In this paper, we review the nudge literature by focusing on these three concerns.
Maternal Gut Microbiota Transplantation Undermined the Lipid and Glucose Metabolism of Newborns in a Piglet Model

BackgroundThe present study was conducted to explore the maternal gut microbiota transplantation on the lipid and glucose metabolism of newborns in a piglet model. Six hysterectomy-derived germ-free (GF) Bama piglets were reared in three sterile isolators and were orally inoculated with healthy sow fecal suspension on day 7 after birth, which considers as fecal microbiota transplanted (FMT) group. Another six piglets from a natural birth and reared in conventional (CV) environments was regarded as CV group. The FMT piglets were hand-fed Co60-γ-irradiated sterile milk powder, CV piglets were reared by lactating Bama sows and both were weaned at day 21. Then, all piglets were housed separately and fed sterile feed for another 21 days. ResultsWe observed that transplanted with sow fecal microbiota increased the content of lipid in liver (P < 0.05), and upregulated the mRNA abundances of genes related to lipid anabolism and catabolism in liver and longissimus dorsi of newborn piglets (P < 0.05). Meanwhile, the concentrations of adiponectin, GLP-1, and insulin in serum and the activity of CPT-1 in liver were lower in FMT piglets (P < 0.05). Besides, transplanted with sow fecal microbiota enhanced the concentration of glycogen in liver (P < 0.05), while upregulated the mRNA expressions of genes related to glycogenesis and glycogenolysis in liver and longissimus dorsi of newborn piglets (P < 0.05). Moreover, the pathway of AMPK was stimulated by sow fecal microbiota transplantation (P < 0.10). In addition, the microbial structure between FMT and CV piglets was marked differently (P < 0.05). Furthermore, transplanted with sow fecal microbiota markedly activated the metabolic pathway of bile secretion in newborn piglets (P < 0.05). ConclusionsCollectively, healthy sow gut microbiota transplanted to newborn germ-free piglets might undermine the homeostasis of glucose and lipid metabolism, and increased the content of lipid, and decreased the concentration of glycogen in liver. It is concluded that transplanted with maternal gut microbiota might induce diseases related to glucose and lipid metabolism in newborns.
Learning multiscale spatial context for three-dimensional point cloud semantic segmentation

Abstract. Semantic segmentation of three-dimensional (3D) scenes is a challenging task in 3D scene understanding. Recently, deep learning-based segmentation approaches have made significant progress. A multiscale spatial context feature learning is used in an end-to-end approach for 3D point cloud semantic segmentation. Furthermore, a local feature fusion learning block is then introduced to the hidden layers in the network to improve its feature learning capability. In addition, features learned in several different layers are fused for further improvement. Based on these strategies, end-to-end architectures are finally designed for 3D point cloud semantic segmentation. Several experiments conducted on three publicly available datasets have clearly shown the effectiveness of the proposed network.
A real-world comparison of outcomes between fractional flow reserve-guided versus angiography-guided percutaneous coronary intervention

Background Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) has been shown to be superior to angiography-guided PCI in randomized controlled studies. However, real-world data on the use and outcomes of FFR-guided PCI remain limited. Thus, we investigated the outcomes of patients undergoing FFR-guided PCI compared to angiography-guided PCI in a large, state-wide unselected cohort. Methods and results All patients undergoing PCI between June 2017 and June 2018 in New South Wales, Australia, were included. The cohort was stratified into the FFR-guided group when concomitant FFR was performed, and the angiography-guided group when no FFR was performed. The primary outcome was a combined endpoint of death or myocardial infarction (MI). Secondary outcomes included all-cause death, cardiovascular (CVS) death, and MI. The cohort comprised 10,304 patients, of which 542 (5%) underwent FFR-guided PCI. During a mean follow-up of 12±4 months, the FFR-guided PCI group had reduced occurrence of the primary outcome (hazard ratio [HR] 0.34, 95% confidence intervals [CI] 0.20–0.56, P<0.001), all-cause death (HR 0.18, 95% CI 0.07–0.47, P = 0.001), CVS death (HR 0.21, 95% CI 0.07–0.66, P = 0.01), and MI (HR 0.46, 95% CI 0.25–0.84, P = 0.01) compared to the angiography-guided PCI group. Multivariable Cox regression analysis showed FFR-guidance to be an independent predictor of the primary outcome (HR 0.45, 95% CI 0.27–0.75, P = 0.002), all-cause death (HR 0.22, 95% CI 0.08–0.59, P = 0.003), and CVS death (HR 0.27, 95% CI 0.09–0.83, P = 0.02). Conclusions In this real-world study of patients undergoing PCI, FFR-guidance was associated with lower rates of the primary outcome of death or MI, as well as the secondary outcomes of all-cause death and CVS death.
Android application to retrieve car details from car plate numbers

Computer vision is a field in computer science that has had great success due to the increasing popularity of machine learning. Instead of having a human look at images and decide what they depict, we are able to teach computers to recognize patterns of previous images and see the resemblance in new images. In this paper we will get car all information from his number plates through an Android Application which use image preprocessing and YOLO (You only look once) technique to detect the plates and then optimal character recognition to read the plates and Arabic numbers of the cars in Iraq and we have trained it using darkflow. Every Car has its unique Licensed number plate which will be scanned and give the details about the car. Computer vision can also be used to read alphanumeric characters in images and turn them into text. We have implemented API to detect the text and number in the plates. The purpose of this project is to develop a system for our Government and Police So that If police need details about any car he would easily access it. We have achieved the accuracy of 92.23% which means out of 3500 number plates 3220 read correctly.
Twenty Years of Unconventional Monetary Policies: Lessons and Way Forward for the Bank of Japan

The Bank of Japan has used unconventional monetary policies to fight deflation and stabilize the financial system since the late 1990s. While the Bank of Japan’s reflation efforts have evolved over time, inflation and inflation expectations have remained stubbornly low. This paper examines the evolution of monetary policy in Japan over the past twenty years, in order to draw relevant lessons and propose ways to strengthen the Bank of Japan’s policy framework. In doing so the analysis focuses on three aspects of monetary policy: objectives and goals; policy strategies; and the communication framework. Moreover, the paper discusses coordination between monetary, fiscal, and financial policies, and how the corresponding institutional design could be strengthened.
Violet-light stimulated synaptic and learning functions in a zinc–tin oxide photoelectric transistor for neuromorphic computation

In contrast to the commonly present UV light-stimulated synaptic oxide thin-film transistors, this study demonstrates a violet light (wavelength of 405 nm) stimulated zinc–tin oxide (ZTO) photoelectric transistor for potential application in optical neuromorphic computation. Owing to the light-induced oxygen vacancy ionization and persistent photoconductivity effect in ZTO, this device well imitates prominent synaptic functions, including photonic potentiation, electric depression, and short-term memory (STM) to long-term memory (LTM) transition. A highly linear and broad dynamic range of photonic potentiation can be achieved by modulating the light power density, while electric depression is realized by gate voltage pulsing. In addition, the brain-like re-learning experience with extended forgetting time (200 s) is well mimicked by the ZTO photoelectric transistor. As a result, the ZTO photoelectric transistor provides excessive synaptic function with multi-series of synaptic weight levels (90 levels for each given light power density), which makes it prevalent in the neuromorphic computation of massive data as well as in learning-driven artificial intelligence computation.
Multi-View Deep Clustering based on AutoEncoder

In recent years, with the development of deep learning, replacing traditional clustering methods with subspaces extracted by deep neural networks will help better clustering performance. However, due to the instability of unsupervised learning, the features extracted each time are different even if the same data is processed. In order to improve the stability and performance of clustering, we propose a novel unsupervised deep embedding clustering multi-view method, which treats multiple different subspaces as different views through some data expansion methods for the same data. Specifically, our method uses a variety of different deep autoencoders to learn the latent representation of the original data and constrain them to learn different features. Our experimental evaluations on several natural image datasets show that this method has a significant improvement compared to existing methods.
Robust adaptive beamforming based on the direct biconvex optimization modeling

In general, robust adaptive beamforming (RAB) is modeled as a nonconvex optimization problem. The most of existing methods solve it indirectly by approximating the nonconvex problem to the convex optimization problem, which will cause the approximation error. Different from the existing methods, a novel method, which reformulates RAB as the biconvex form directly by use of an auxiliary variable, is proposed. Thus, the errors caused by the approximation can be avoided and the performance will be improved. Then the alternating direction methods of multiplier (ADMM) algorithm is applied to solve it. Compared with the existing methods, the results of the simulation experiments indicated that the proposed method has better performance encountered with types of signal steering vector mismatches.
Deep Learning for Network Intrusion Detection: An Empirical Assessment

The detection of security-related events using machine learning approaches has been extensively investigated in the past. Particularly, machine learning-based network intrusion detection has attracted a lot of attention due to its potential to detect unknown attacks. A number of classiﬁcation techniques have been used for that purpose, but they were mostly classical schemes like decision trees. In this paper we go one step further and explore the use of a set of machine learning techniques denominated generically as “deep learning” that have been generating excellent results in other areas. We compare three recent techniques – generalized linear models, gradient boosting machines, and deep learning – with classical classiﬁers. The comparison is performed using a recent data set of network communication traces designed carefully for evaluating intrusion detection schemes. We show that deep learning techniques have an undeniable value over older algorithms, since better model ﬁtting indicators can be achieved.
Multi-Walled Carbon Nanotubes Enhance Methanogenesis from Diverse Organic Compounds in Anaerobic Sludge and River Sediments

Conductive nanomaterials affect anaerobic digestion (AD) processes usually by improving methane production. Nevertheless, their effect on anaerobic communities, and particularly on specific trophic groups such as syntrophic bacteria or methanogens, is not extensively reported. In this work, we evaluate the effect of multi-walled carbon nanotubes (MWCNT) on the activity of two different anaerobic microbial communities: an anaerobic sludge and a river sediment. Methane production by anaerobic sludge was assessed in the presence of different MWCNT concentrations, with direct methanogenic substrates (acetate, hydrogen) and with typical syntrophic substrates (ethanol, butyrate). MWCNT accelerated the initial specific methane production rate (SMPR) from all compounds, with a more pronounced effect on the assays with acetate and butyrate, i.e., 2.1 and 2.6 times, respectively. In the incubations with hydrogen and ethanol, SMPR increased 1.1 and 1.2 times. Experiments with the river sediment were performed in the presence of MWCNT and MWCNT impregnated with 2% iron (MWCNT-Fe). Cumulative methane production was 10.2 and 4.5 times higher in the assays with MWCNT-Fe and MWCNT, respectively, than in the assays without MWCNT. This shows the high potential of MWCNT toward bioenergy production, in waste/wastewater treatment or ex situ bioremediation in anaerobic digesters.
The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm

Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached the stimulators on the subject's cheeks. Two paradigms were designed as follows: a paradigm with no visual attention and another paradigm with visual attention to the target position. Eleven subjects were invited to perform the two paradigms. We also recorded and analyzed the eyeball movement data during the paradigm with visual attention to explore whether the eyeball movement would have an effect on the BCI classification. The average online accuracy was 89.09% for the paradigm with visual attention, which was significantly higher than that of the paradigm with no visual attention (70.45%). Significant difference in ITR was also found between the two paradigms ([Formula: see text]). The results demonstrated that visual attention was an effective method to improve the performance of tactile P300 BCI. Our findings suggested that it may be feasible to complete an efficient tactile BCI system by adding visual attention.
Soil behaviour of sulfur natural fumigants used as methyl bromide substitutes

Methyl bromide is the most widely used and most effective fumigant and is used extensively for soil fumigation. According to the Montreal Protocol of 1991, methyl bromide is categorised as an ozone-depleting chemical and its use is prohibited from 2005. Many substitutes, such as methyl isothiocyanate and methyl iodide, are not applied as widely as methyl bromide. Moreover, crushed Alliumspp. plants (garlic, leek and onion) produce thiosulfinates (Ti, R–S–SO–R′) and related compounds like disulfides, which have the same pesticide activity as methyl bromide. Therefore Allium tissues or extracts can be used in biological control and Integrated Pest Management in agriculture. The successful application of these compounds, and Allium tissues and extracts, for biological soil disinfection requires more specific knowledge regarding their subsequent fate in the soil. To obtain this, appropriate analytical methods using IR spectroscopy, SPME (solid-phase micro-extraction) and GC-MS techniques were developed and applied in the laboratory on pure compounds and on Allium tissues or extracts. The experiments revealed that thiosulfintes are stable in the atmosphere but in soil they are rapidly degraded into disulfides, which are very stable in soil. For that reason and for their general pesticide effect, disulfides are a promising alternative to methyl bromide.
Electrochemical paper-based biosensors for bacteria detection in water

Paper substrates have shown a high potential for development of cost-effective and efficient point-of-care biosensors, essential for public healthcare and environmental diagnostics. Most paper-based biosensors rely on qualitative colorimetric detection schemes with high limits of detection. To overcome this limitation, technologies that combine paper-based substrates and electrochemical detection are being developed to allow for quantification and achieve better performances. In this work, we explore the potential of dielectric measurements towards electrical detection of whole-cell bacteria in nitrocellulose membranes, a paper-derivative. Impedance spectroscopy was considered to characterize the membranes with and without Bacillus thuringiensis cells, used as model microorganism. To specifically target this bacterial strain, phage endolysin cell-wall binding domain (CBD) encoded by a bacteriophage targeting B. thuringiensis were prepared and integrated into the membranes as recognition biointerface. The fluid sample containing the bacteria is conducted in the membrane through passive capillarity, and the bacteria are specifically immobilized in the test zone. Resulting changes of the dielectric properties of the membrane are sensed through impedance changes, highlighting the contribution of ions in the bacterial detection mechanism. This experimental proof-of-concept illustrates the electrical detection of 108 CFU/mL bacteria in low-salinity buffers within 5 min.
Determination of Silver‐Modified Titanium Phosphate Nanoparticles by Voltammetric and Electrocatalytic Methods

The electrochemical determination of silver-modified titanium phosphate nanoparticles (Ag-TiPNPs) was performed using two electrochemical features of this novel kind of nanoparticles. First, a determination using the voltammetric activity of the silver from the Ag-TiPNPs was carried out. Secondly, the electrocatalytic effect of Ag-TiPNPs was shown for the first time to the hydrogen evolution reaction (HER) and the determination of these nanoparticles was performed by chronoamperometry using this electrocatalysis. Moreover, it was verified that the catalytic effect was due to the electroreduced silver since the unmodified titanium phosphate nanoparticles (TiPNPs) did not exhibit this effect. Detection limits as low as 0.1 and 0.75 ng µL−1 of Ag-TiPNPs were obtained with the voltammetric and chronoamperometric methods, respectively. 8-channel screen-printed electrochemical arrays (8xSPCEs) were employed as transducers to carry out these electrochemical studies, due to its low cost and time saving.
SAXS Study of the Nucleation of Glycine Crystals from a Supersaturated Solution

Nucleation of crystalline solids, the first stage of crystallization from solution, is not yet fully understood. This is true for both small molecules of low molecular weight and more complicated large molecules. To obtain direct structural information about the process of nucleation and crystallization of small molecules, small-angle X-ray scattering (SAXS) has been used to study the crystallization of the amino acid glycine from its supersaturated aqueous solution. The scattering data was analyzed using the unified fit model, which is well-suited for studying complex systems that may contain multiple levels of related structural features. The results suggest that glycine molecules exist as dimers in supersaturated solution. The system obeys power-law behavior that indicates the presence of fractals in the solution. A transformation from mass fractal structure to surface fractal structure is observed during the crystallization process, which could be the signature of a two-step nucleation process.
A Review of Privacy-Preserving Aggregation Schemes for Smart Grid

Smart Grid aims to make the use of electricity reliable, sustainable, scalable, fault-tolerant, and efficient. For this purpose, it uses information and communication technologies. However, there are issues related to information security threats. For example, an adversary monitoring data metering from consumers could trace their habits. Traditional encryption techniques resolve this question against external attackers. However, the operation center still could trace the consumers habits. Thus, to preserve consumers privacy, even against internal threats, the data must be aggregated by groups of consumers, representing a geographic area. A usual way to make it possible is through the use of homomorphic cryptography, since it allows making arithmetic operation over encrypted data. Thus, this article reviews the literature on privacy-preserving data aggregation schemes against internal and external attackers. It also presents the information security requirements for Advanced Metering Infrastructure (AMI) and its operation.
First-Principles Study on the Corrosion Mechanism of Degradable Medical Mg Alloys in the Medium Containing Cl-

A first-principles plane-wave pseudopotential method based on Density Functional Theory (DFT) has been used to perform a comparative theoretical study of the adsorption H2O and Cl- on a degradable medical Mg alloy (0001) surface. It is found that H2O molecules bind preferentially at atop sites with a large tilt angle away from the surface normal, Cl- ions are energetically favoured adsorbed on hcp hollow sites. In the aqueous solution containing Cl-, the preferential adsorption of Cl- over H2O on Mg surface prevents the Mg surface passivation and induces the corrosion of degradable medical Mg alloys. For H2O adsorption, the ineraction between H2O molecule and Mg surface makes Mg hydration, and thereby Mg alloy surface passivates and the formation of the protective Mg(OH)2. For Cl- ion adsorption, a 0.44e charge translation from Cl- ion to the Mg alloy surface makes the corrosion potential of Mg alloy surface negativly shifted. Thereby, the activity and the corrosion of magnesium alloy are enhanced. This can explain why degradable medical magnesium alloys corrode quickly in physiological media containing high concentrations of chloride ions.