Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum hacking on a practical continuous-variable quantum cryptosystem by inserting an external light

We report here a new side channel attack on a practical continuous-variable (CV) quantum key distribution (QKD) system. Inspired by blinding attack in discrete-variable QKD, we formalize an attack strategy by inserting an external light into a CV QKD system implemented Gaussian-modulated coherent state protocol and show that our attack can compromise its practical security. In this attack, we concern imperfections of a balanced homodyne detector used in CV QKD. According to our analysis, if one inserts an external light into Bob’s signal port, due to the imperfect subtraction from the homodyne detector, the leakage of the external light contributes a displacement on the homodyne signal which causes detector electronics saturation. In consequence, Bob’s quadrature measurement is not linear with the quadrature sent by Alice. By considering such vulnerability, a potential Eve can launch a full intercept-resend attack meanwhile she inserts an external light into Bob’s signal port. By selecting proper properties of the external light, Eve actively controls the induced displacement value from the inserted light which results saturation of homodyne detection. In consequence, Eve can bias the excess noise due to the intercept-resend attack and the external light, such that Alice and Bob believe their excess noise estimation is below the null key threshold and they can still share a secret key. Our attack shows that the detector loopholes also exist in CV QKD, and it seems influence all the CV QKD systems using homodyne detection, since all the practical detectors have finite detection range.
Wound photography for evaluation of surgical site infection and wound healing after lower limb trauma.

AIMS Deep surgical site infection (SSI) is common after lower limb fracture. We compared the diagnosis of deep SSI using alternative methods of data collection and examined the agreement of clinical photography and in-person clinical assessment by the Centers for Disease Control and Prevention (CDC) criteria after lower limb fracture surgery. METHODS Data from two large, UK-based multicentre randomized controlled major trauma trials investigating SSI and wound healing after surgical repair of open lower limb fractures that could not be primarily closed (UK WOLLF), and surgical incisions for fractures that were primarily closed (UK WHiST), were examined. Trial interventions were standard wound care management and negative pressure wound therapy after initial surgical debridement. Wound outcomes were collected from 30 days to six weeks. We compared the level of agreement between wound photography and clinical assessment of CDC-defined SSI. We are also assessed the level of agreement between blinded independent assessors of the photographs. RESULTS Rates of CDC-defined deep SSI were 7.6% (35/460) after open fracture and 6.3% (95/1519) after closed incisional repair. Photographs were obtained for 77% and 73% of WOLLF and WHiST cohorts respectively (all participants n = 1,478). Agreement between photographic-SSI and CDC-SSI was fair for open fracture wounds (83%; k = 0.27 (95% confidence interval (CI) 0.14 to 0.42)) and for closed incisional wounds (88%; k = 0.29 (95% CI 0.20 to 0.37)) although the rate of photographically detected deep SSIs was twice as high as CDC-SSI (12% vs 6%). Agreement between different assessors for photographic-SSI (WOLLF 88%, k = 0.63 (95% CI 0.52 to 0.72); WHiST 89%; k = 0.61 (95% CI 0.54 to 0.69)); and wound healing was good (WOLLF 90%; k = 0.80 (95% CI 0.73 to 0.86); WHiST 87%; k = 0.57 (95% CI 0.50 to 0.64)). CONCLUSION Although wound photography was feasible within the research context and inter-rater assessor agreement substantial, digital photographs used in isolation overestimated deep SSI rates, when compared to CDC criteria. Wound photography should not replace clinical assessment in pragmatic trials but may be useful for screening purposes where surgical infection outcomes are paramount. Cite this article: Bone Joint J 2021;103-B(12):1802-1808.
HBT on LEO GaN

Dramatic progress in GaN electronics has led to increased interest in bipolar transistors. Although there have been reports of GaN bipolars from several groups, the development of the GaN bipolar transistor is still in its fundamental stages. In the case of GaN, the usual correlation between common base, Gummel, and common emitter characteristics does not exist due to significant collector-emitter leakage, leaving only the common emitter characteristic as a reliable measure of DC device performance. We identify the source of this leakage as threading dislocations and clarify the effect of this leakage on the transistor DC characteristics. Furthermore, we conclude from various growth structures and methods of device fabrication that the electron lifetime in the neutral base is currently the limiting factor in GaN NPN transistor performance. Typical GaN material has high threading dislocation densities, 10/sup 7/-10/sup 9/ cm/sup -2/, due to lattice mismatch with the substrate, typically sapphire or SiC. To study the effects of threading dislocations on GaN bipolar transistors, we have fabricated devices on material grown using the lateral epitaxial overgrowth technique, LEO. To the authors' knowledge, this is the first demonstration of GaN bipolar transistors grown on nondislocated material. The LEO substrate allows us to compare devices grown on material with a negligible dislocation density with those grown on a standard template.
Equation of State’s Crossover Enhancement of Pseudopotential Lattice Boltzmann Modeling of CO2 Flow in Homogeneous Porous Media

The original Shan-Chen’s pseudopotential Lattice Boltzmann Model (LBM) has continuously evolved during the past two decades. However, despite its capability to simulate multiphase flows, the model still faces challenges when applied to multicomponent-multiphase flows in complex geometries with a moderately high-density ratio. Furthermore, classical cubic equations of state usually incorporated into the model cannot accurately predict fluid thermodynamics in the near-critical region. This paper addresses these issues by incorporating a crossover Peng–Robinson equation of state into LBM and further improving the model to consider the density and the critical temperature differences between the CO2 and water during the injection of the CO2 in a water-saturated 2D homogeneous porous medium. The numerical model is first validated by analyzing the supercritical CO2 penetration into a single narrow channel initially filled with H2O, depicting the fundamental role of the driving pressure gradient to overcome the capillary resistance in near one and higher density ratios. Significant differences are observed by extending the model to the injection of CO2 into a 2D homogeneous porous medium when using a flat versus a curved inlet velocity profile.
Development of an immunomagnetic bead clean-up ELISA method for detection of Maduramicin using single-chain antibody in chicken muscle

ABSTRACT An indirect competitive enzyme-linked immunosorbent assay (icELISA) based on immunomagnetic bead clean-up for detection of Maduramicin (MAD) was developed using a single-chain antibody (scFv). The single-chain antibody synthesized by genetic engineering technology was immobilized on the surface of carboxylic acid magnetic beads with a diameter of 2.8 μm. After simple extraction, the residue in the sample extract was specifically adsorbed, and the supernatant was removed by magnetic separation. The half-maximal inhibitory concentration (IC50) of 15.43 ng·ml−1 with a limit of detection of 6.31 ng·ml−1 for MAD in chicken muscle. The recovery ranged from 72.93 to 89.51% with variation coefficients in intraday and interday less than 15%. We improved the inhibition efficiency and sensitivity of this method compared with traditional hydrophilic-lipophilic balance column clean up icELISA. The convenience and repeatability of the immunomagnetic clean-up render the new method for the analysis of drug residues in complex matrices. GRAPHICAL ABSTRACT
Sustainable Smart Cities: Convergence of Artificial Intelligence and Blockchain

Recently, 6G-enabled Internet of Things (IoT) is gaining attention and addressing various challenges of real time application. The artificial intelligence plays a significant role for big data analytics and presents accurate data analysis in real time. However, designing big data analysis through artificial intelligence faces some issues in terms of security, privacy, training data, and centralized architecture. In this article, blockchain-based IoT framework with artificial intelligence is proposed which presents the integration of artificial intelligence and blockchain for IoT applications. The performance of the proposed architecture is evaluated in terms of qualitative and quantitative measurement. For qualitative measurement, how the integration of blockchain and artificial intelligence addresses various issues are described with the description of AI oriented BC and BC oriented AI. The performance evaluation of proposed AI-BC architecture is evaluated and compared with existing techniques in qualitative measurement. The experimental analysis shows that the proposed framework performs better in comparison with the existing state of art techniques.
Association between Fruit and Vegetable Intake and Physical Activity among Breast Cancer Survivors: A Longitudinal Study

This study examines the association between rates of change in daily fruit and vegetable intake and in weekly levels of moderate-to-vigorous intensity physical activity (MVPA) over a 15-month period in women following primary treatment completion for breast cancer. Breast cancer survivors (N = 199) self-reported fruit and vegetable intake and wore an accelerometer for 7 consecutive days to measure levels of MVPA on five occasions every 3 months. Multivariate latent growth modeling revealed that the rate of change in fruit and vegetable intake was not associated with the rate of change in levels of MVPA. Baseline (Mean = 3.46 months post-treatment) levels of MVPA were not associated with the rate of change of daily fruit and vegetable intake; likewise, baseline fruit and vegetable intake was not associated with the rate of change in levels of MVPA. Behavioral interventions promoting fruit and vegetable intake should not be assumed to yield concomitant effects in promoting MVPA or vice versa.
Spectrophotometric method for simultaneous estimation of atazanavir sulfate and ritonavir in tablet dosage form

Background: Ritonavir (RTV) and atazanavir sulfate (ATV) are protease inhibitor and RTV mostly used as a booster for increasing the bioavailability of other protease inhibitors like ATV. Aims: Quality assessment of the new dosage form of RTV and ATV i.e., tablets is very essential and hence this work deals with to develop sensitive, simple and precise method for simultaneous estimation of ATV and RTV in tablet dosage form by absorbance correction method. Materials and Methods: The present work was carried out on Shimadzu Ultraviolate(UV)-1700 double beam spectrophotometer with 1 cm path length supported by S Shimadzu, model-1700(Japan), UV-Probe software, version 2.31 was used for spectral measurements with 10 mm matched quartz cells. Standard ATV and RTV were supplied by Cipla Pharmaceutical Ltd. Methanol was purchased from Finar Chemicals Pvt. Ltd. Results and Conclusion: The λmax or the absorption maxima for ATV and RTV were found to be 279 and 240 nm, respectively in methanol as solvent. The drugs follow Beer-Lambert′s law in the concentration range 30-90 and 10-30 μg/mL for ATV and RTV, respectively. The percentage recovery was found to be 100-100.33% and 100-101.5% for ATV and RTV, respectively. The method was validated for different parameters as per the International Conference for Harmonization Guidelines.
Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies—A TMT-Based LC-MSMS Proteome Investigation

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.
A hybrid neural network strategy for identification of structural parameters

This paper presents a multistage identification scheme for structural damage detection using modal data. Previous studies of damage assessment using neural networks mostly involved training a backpropagation neural network (BPN) to learn damage patterns that were obtained either experimentally or by simulation for different damage cases. Damage identification for large structures, especially those involving multiple member damage, could result in large training data sets that require a large BPN and consequently greater computational effort. The proposed scheme involves using a counterpropagation neural network (CPN) in the first stage for sorting the training data into clusters and giving an approximate guess of the damage extent within a very short time. After an approximate estimate of the damage is obtained, a new set of training patterns of reduced size is generated using the CPN prediction. In the second stage, a BPN trained with the Levenberg–Marquardt algorithm is used to learn the new training data and predict a more accurate result. A superior convergence and a substantial decrease in central processing unit (CPU) time are observed for three numerical examples.
Efficacy and Safety of Video-Laryngoscopy versus Direct Laryngoscopy for Double-Lumen Endotracheal Intubation: A Systematic Review and Meta-Analysis

The available meta-analyses have inconclusively indicated the advantages of video-laryngoscopy (VL) in different clinical situations; therefore, we conducted a systematic review and meta-analysis to determine efficacy outcomes such as successful first attempt or time to perform endotracheal intubation as well as adverse events of VL vs. direct laryngoscopes (DL) for double-lumen intubation. First intubation attempt success rate was 87.9% for VL and 84.5% for DL (OR = 1.64; 95% CI: 0.95 to 2.86; I2 = 61%; p = 0.08). Overall success rate was 99.8% for VL and 98.8% for DL, respectively (OR = 3.89; 95%CI: 0.95 to 15.93; I2 = 0; p = 0.06). Intubation time for VL was 43.4 ± 30.4 s compared to 54.0 ± 56.3 s for DL (MD = −11.87; 95%CI: −17.06 to −6.68; I2 = 99%; p < 0.001). Glottic view based on Cormack–Lehane grades 1 or 2 equaled 93.1% and 88.1% in the VL and DL groups, respectively (OR = 3.33; 95% CI: 1.18 to 9.41; I2 = 63%; p = 0.02). External laryngeal manipulation was needed in 18.4% cases of VL compared with 42.8% for DL (OR = 0.28; 95% CI: 0.20 to 0.40; I2 = 69%; p < 0.001). For double-lumen intubation, VL offers shorter intubation time, better glottic view based on Cormack–Lehane grade, and a lower need for ELM, but comparable first intubation attempt success rate and overall intubation success rate compared with DL.
Nanoparticles and polyaniline electrical conductivity

Polyaniline is a conductive polymer that attracts the attention of many researchers around the world. The history of this polymer begins in 1862 when Letheby first reported this material. Since then, a myriad of studies has been conducted on this material, and new works continue to investigate the potential of this material. Polyaniline has been improved with the help of Nanotechnology. The use of nanofillers has been seen as a quick and economical way to modify materials, driving innovations based on new physical and chemical properties from the conductive polymer materials and nanoparticles joining. Several works address the use of different nanoparticles, which leads to the practical impossibility of sifting through all this information. Thus, this work proposes to systematically collect data in the literature and investigate which nanoparticles can increase the electrical conductivity of Polyaniline (PAni). The results obtained demonstrate that among the possible nanofillers, graphene and carbon nanotubes have great prominence. Furthermore, the results of the meta-analysis prove that PAni's conductivity increases when this polymer is modified with the aforementioned nanofillers.
Crusader Automated Docking System Phase 3 report

The US Army is developing the next generation of battlefield artillery vehicles, including an advanced, self-propelled howitzer and a companion resupply vehicle. The resupply vehicle is intended to rendezvous with the howitzer near the battlefront and to upload ammunition to the howitzer. The Army has recommended that the vehicles incorporate robotics to increase safety, by allowing the crew to remain inside their vehicles during resupply operations. Oak Ridge National Laboratory has developed an autonomous docking system for a 6-D.F. robotic, ammunition transfer arm. The docking system augments the operator`s abilities by determining the position and orientation (pose) of a docking port. The pose is the location of the x, y, and z reference axes in 3-D space; and the orientation is the rotations--roll, pitch, and yaw--about those axes. Bye precisely determining the pose of the docking port, the robot can be instructed to move to the docking position without operator intervention. The system uses a video camera and frame grabber to digitize images of the special docking port. Custom algorithms were developed to recognize the port in the camera image, to determine the pose from its image features, and to distribute the results to the robot control computer. The system is loosely coupled to the robot and can be easily adapted to different mechanical configurations. The system has successfully demonstrated autonomous docking on a 24-in. tabletop robot and a 12-ft ammunition resupply robot. The update rate, measurement accuracy, continuous operation, and accuracy with obstructed view have been determined experimentally.
Wavelet Packet‐Autocorrelation Function Method for Traffic Flow Pattern Analysis

Abstract: Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet‐ACF method is proposed for analysis of traffic flow time series and determining its self‐similar, singular, and fractal properties. A DWPT‐based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic‐forecasting models.
Spatial-Temporal Neural Network for P300 Detection

P300 spellers are common brain-computer interface (BCI) systems designed to transfer information between human brains and computers. In most P300 detections, the P300 signals are collected by averaging multiple electroencephalographic (EEG) changes to the same target stimuli, so the participants are obliged to endure multiple repeated stimuli. In this study, a spatial-temporal neural network (STNN) based on deep learning (DL) is proposed for P300 detection. It detects P300 signals by combining the outputs from a temporal unit and a spatial unit. The temporal unit is a flexible framework consisting of several temporal modules designed for analyzing brain potential changes in the time domain. The spatial unit combines one-dimensional convolutions (Conv1Ds) and linear layers to generalize P300 features from the space domain, and it can decode EEG signals recorded using different numbers of electrodes. Both amyotrophic lateral sclerosis (ALS) patients and healthy subjects can benefit from this study. In the within-subject P300 detection and the cross-subject P300 detection, our approach gained higher performance with fewer repeated stimuli than other comparative approaches. Furthermore, we applied the proposed STNN in the P300 detection challenge of BCI Competition III. The accuracy score was 89% in the fifth round of repeated stimuli, outperforming the best result in the literature (accuracy = 80%) to the best of our knowledge. The results demonstrate that the proposed STNN performs well with limited stimuli and is robust enough for various P300 detections.
Femtosecond Laser Precision Engineering: From Micron, Submicron, to Nanoscale

As a noncontact strategy with flexible tools and high efficiency, laser precision engineering is a significant advanced processing way for high-quality micro-/nanostructure fabrication, especially to achieve novel functional photoelectric structures and devices. For the microscale creation, several femtosecond laser fabrication methods, including multiphoton absorption, laser-induced plasma-assisted ablation, and incubation effect have been developed. Meanwhile, the femtosecond laser can be combined with microlens arrays and interference lithography techniques to achieve the structures in submicron scales. Down to nanoscale feature sizes, advanced processing strategies, such as near-field scanning optical microscope, atomic force microscope, and microsphere, are applied in femtosecond laser processing and the minimum nanostructure creation has been pushed down to ~25 nm due to near-field effect. The most fascinating femtosecond laser precision engineering is the possibility of large-area, high-throughput, and far-field nanofabrication. In combination with special strategies, including dual femtosecond laser beam irradiation, ~15 nm nanostructuring can be achieved directly on silicon surfaces in far field and in ambient air. The challenges and perspectives in the femtosecond laser precision engineering are also discussed.