Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutrino scattering off the stable cadmium isotopes: II. Charged-current processes

The present study is a continuation of our previous work (2015 J. Phys. G: Nucl. Part. Phys. 42 025106) on neutral-current supernova-neutrino processes in the stable cadmium isotopes. Here we concentrate on the charged-current scattering processes of neutrinos off the stable cadmium isotopes covering an energy range characteristic of supernova neutrinos. As a theory framework we adopt the quasiparticle random-phase approximation in its charge-changing mode (pnQRPA) and the microscopic quasiparticle-phonon model to construct the required nuclear wave functions of the odd–odd and odd–even isobars of the stable even–even reference cadmium isotopes, respectively. The cross sections of the charged-current neutrino and antineutrino scatterings off the stable cadmium isotopes are subsequently computed. The nuclear responses to supernova neutrinos are also estimated by folding the cross sections with neutrino-energy spectra including the effects of matter oscillations within an effective two-neutrino framework. The calculations indicate that the folded cross sections have mass-number dependencies that are different for neutrinos and antineutrinos, and that odd-mass nuclei have relatively large cross sections. The matter oscillations affect the cross sections strongly and it is found that the computed averaged cross sections for antineutrinos differ notably for the normal/inverted mass hierarchy. This could have interesting consequences for the experimental solution of the mass-hierarchy problem in future large-scale neutrino telescopes.
Validation of Aerodynamic and Optical Computations for the Unsteady Flow Field About a Hemisphere-on-Cylinder Turret

An evaluation of the aerodynamic and aero-optic predictive capability based on the Boeing Computational Fluid Dynamics (CFD) program BCFD is presented. The Boeing Enterprise Aero-optic Modeling (BEAM) process is presented which combines state-of-the-art CFD simulations with a procedure that computes the optical path integrations and the associated Optical Path Distortions (OPD) which characterize the magnitude of the wavefront deviation due to the aerodynamic disturbances over the device. This capability will also allow Active Flow Control (AFC) devices to be evaluated in terms of both aerodynamic and aerooptic (AO) benefits. This type of simulation capability is a large step toward providing a method to screen potential wind tunnel models and to provide a cost effective alternative to the high cost of wind tunnel model design, manufacturing, instrumentation, and testing all together.
Temperatures of Λ Hyperons, K 0 and π - -Mesons Produced in C-C and Mg-Mg Collisions at 4.2 ÷ 4.3 AGeV/c

Particle spectral temperatures for Λ hyperons, K 0 and π - -mesons produced either in the absence or presence of the Λ hyperons and K 0 mesons in the C-C and Mg-Mg collisions at incident momentum of 4.2÷4.3 AGeV/c have been determined focussing independently on either center-of-mass energy and transverse momentum distributions. For negative pions, the temperatures were found to be approximately the same, no matter whether the emission of those particles was associated with Λ and K 0 production or not. Results of the measurements have been compared to the results of simulations within the Quark-Gluon String Model. © 2010 Bull. Georg. Natl. Acad. Sci.
Role of CD5 molecular-like on hepatocellular carcinoma

Abstract Background: CD5L (CD5 molecular-like) plays an important role in lipid metabolism and immune regulation. This study aimed to investigate the roles of CD5L on liver hepatocellular carcinoma (LIHC). Methods: We analyzed the CD5L mRNA expression and its potential prognostic value based on The Cancer Genome Atlas and Gene Expression Omnibus databases. Immunohistochemical analysis was used to investigate the CD5L levels in LIHC tissues. Serum CD5L levels in LIHC were detected by enzyme-linked immunosorbent assay. Cell Counting Kit-8 (CCK-8) assay was used to investigate the effect of CD5L treatment on HepG2 and QSG-7701 cell proliferation. CD5L expression correlated genes were exhumed based on the LinkedOmics. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses for CD5L associated genes were performed. The correlation between CD5L and tumor immune infiltration was analyzed by using Tumor Immune Estimation Resource (TIMER) 2.0. Results: CD5L mRNA and protein levels were significantly decreased in LIHC tumor tissue compared with non-tumor control tissues. Moreover, serum CD5L levels were significantly lower in LIHC patients than that in healthy subjects. Gene Expression Profiling Interactive Analysis 2 and Kaplan-Meier plotter analysis showed that a high-CD5L expression was correlated with favorable overall survival in LIHC patients, except the LIHC patients with hepatitis virus. CCK-8 results showed that CD5L treatment significantly decreased HepG2 cell proliferation in a concentration-dependent manner, and CD5L treatment had no effect on the proliferation of non-tumor hepatocyte line QSG-7701. CD5L associated genes were enriched in the immune response biological process, and CD5L expression levels were positively correlated with the immune infiltrates of CD8+ T cell and M1 macrophage cells but negatively correlated with CD4+ T cells and M0 macrophage cell infiltration. Conclusions: Exogenous CD5L inhibits cell proliferation of hepatocellular carcinoma. CD5L may act as a role of prognostic marker.
Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

The current methods of classifying plant disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%.
Transforming quantum circuits into negator circuits

In this work we present a method of decomposition of arbitrary unitary matrix $U\in\mathbf U(2^k)$ into a product of single-qubit negator and controlled-$\sqrt{NOT}$ gates. Since the product results with negator matrix, which can be treated as complex analogue if bistochastic matrix, our method can be seen as complex analogue of Sinkhorn-Knopp algorithm, where diagonal matrices are replaced by adding and removing an one-qubit ancilla. The decomposition can be found constructively and resulting circuit consists of $O(4^k)$ entangling gates, which is proved to be optimal. An example of such transformation is presented.
WebGripper: Bioinspired Cobweb Soft Gripper for Adaptable and Stable Grasping

Web structure is a flexible structure widely used in animal predation behavior and human hunting work, which exhibits an excellent capability of gripping objects stably and securely. Inspired by the predation behavior of spiders, in this article, we present a bioinspired cobweb soft gripper, named WebGripper, which takes on a form like a spider web before grasping an object and presents an entangling state like a snake after grasping the object. These characteristics result in broad adaptability to various objects and enhanced grasping stability. The design concept, grasping principle, and fabrication process of the WebGripper are presented in this work, and the influences of cable layout and soft finger installation angle are analyzed to provide a reference for the design of the WebGripper. To evaluate the performance of the WebGripper, we carried out experiments on grasping objects with different shapes and sizes. Experimental results illustrated the universal adaptable grasping ability of the WebGripper, which exhibits excellent application prospects in many fields, such as goods sorting, fruit picking, and underwater fishing. This work is expected to promote the development of a high-performance soft gripper that may be used in a number of applications based on the understanding of the bionic web structure.
Parallel Arrayed Waveguide Grating for Multi-plane Light Conversion Mode Multiplexer

For the multi-plane light conversion (MPLC) linear polarization (LP) mode multiplexer, we design and fabricate a 2×8 polymer parallel arrayed waveguide grating (AWG) with the working wavelengths of 1271 nm, 1291 nm, 1311 nm and 1331 nm. This device can implement the multiplexing of 8 input ports with wavelength spacing of 20 nm into 2 output ports, with 4 multiplexed wavelength channels in each port, using a single AWG structure.
Query Management for a Decentralised Enterprise Knowledge Graph

Organisations manage large amount of data scattered in multiple data sources disseminated within the organisation. In this context, decision-making having a complete vision of the available data to make the right decisions is a challenge. Choosing the right strategy to bypass this issue may be impactful on the enterprise data management policy. A good alternative to limit data integration issues can be found in Knowledge Graphs. These Knowledge Graphs allow decision makers to understand which data of interest are existing in the enterprise. They are integrating the semantic of data rather than data itself. In a previous research, we proposed a decentralised enterprise knowledge graph (DEKG) architecture to facilitate decision making and consider the scattered data sources of interest. Due to the decentralised dimension of this architecture and the separation of the data that is scattered in remote sources, but also to the "centralised" aspect of knowledge graphs, one of the remaining challenges is the decentralised query management in order to answer a decision-maker needs. As a result we define in this paper a four step approach to manage queries and to build results based on a DEKG. We illustrate the proposed approach through examples and detail its implementation. We also show the results of preliminary experiments before giving some future work.
Fractional Fourier Transform Based Channel Estimation in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is important for the 5G communications nowadays, which can improve the spectral efficiency through beamforming or multiplexing. However, with the increasement of antenna numbers, a lot of users are very near to the BS, which means the change in the electromagnetic structure. Spherical wave must be considered in during the channel estimation. Traditional channel estimation schemes assume the planar wave. The channel is sparse in angles. This characteristic can be used to estimate the parameters of the system. However, in a spherical wave system, the sparsity in angle domain no longer exists. In this paper, we propose a fractional fourier transform (FrFT) based channel estimation scheme, which can accurately extract the sparsity in near-field communication systems and thus estimating the whole channel. Specifically, the channel estimation scheme is divided into two steps. Firstly, we estimate the parameters of the multiple paths. Secondly, the whole channel are recovered based on the estimated parameters in the first step. Simulation results reveal that the proposed FrFT based channel estimation schemes outperforms the existing schemes.
Machine Learning Models to Predict Cardiovascular Events from Heart Rate Variability Data

Among the diseases known to mankind, cardiovascular diseases remain the deadliest and most expensive. However, Artificial Intelligence offers new solutions that can help diagnose these diseases and even predict their occurrence with high accuracy. In this study, we present several AI models that attempt to predict cardiovascular diseases. The models created are Support Vector Machines, AdaBoost, TabNet and TabTransformers. They were trained using Heart Rate Variability features extracted from the PhysioNet Smart Health for Assessing the Risk of Events via ECG Database. The models achieved high accuracies, which were 91.80%, 89.50%, 76.00% and 90.38% for the Support Vector Machines, AdaBoost, TabNet, and TabTransformers models, respectively.
Depressive Symptoms Mediate Associations between Subjective Memory Complaints, Cognitive Ability, and Instrumental Activities of Daily Living

Abstract Objectives: Cognitive ability (CA) and instrumental activities of daily living (IADL) performance are essential for productive aging. Therefore, it is important to understand if other common issues such as subjective memory complaints (SMCs) and depressive symptoms are related to CA and IADLs, and explore the nature of those relationships. This study aims to explore 1) the associations between SMCs, depressive symptoms, CA, and IADL performance and 2) the role of depressive symptoms in these associations. Method: The study sample included 1,621 community-dwelling oldest-old from the Health and Retirement Study. Results: SMCs were significantly and positively associated with cognitive decline and limitations in IADL performance. Additionally, depressive symptoms mediated the associations between SMCs and cognitive decline and between SMCs and limitations in IADL performance. Conclusion: Depressive symptoms associated with SMCs negatively affect CA and IADL performance. Therefore, healthcare providers should consider and assess SMCs and depressive symptoms in the oldest-old.
The First Swedish Outbreak with VIM-2-Producing Pseudomonas aeruginosa, Occurring between 2006 and 2007, Was Probably Due to Contaminated Hospital Sinks

Multidrug-resistant Pseudomonas aeruginosa is an increasing clinical problem worldwide. The aim of this study was to describe the first outbreak of a Verona integron-borne metallo-ß-lactamase (VIM)-2-producing P. aeruginosa strain in Sweden and its expansion in the region. A cluster of multidrug-resistant P. aeruginosa appeared at two neighbouring hospitals in 2006. The isolates were characterized by PCR, pulsed-field gel electrophoresis (PFGE), and whole-genome sequencing. Patient charts, laboratory records, and hygiene routines were reviewed, and patients, staff, and the environment were screened. The investigation revealed a clonal outbreak of a VIM-2-producing P. aeruginosa strain belonging to the high-risk clonal complex 111, susceptible only to gentamicin and colistin. No direct contact between patients could be established, but most of them had stayed in certain rooms/wards weeks to months apart. Cultures from two sinks yielded growth of the same strain. The outbreak ended when control measures against the sinks were taken, but new cases occurred in a tertiary care hospital in the region. In conclusion, when facing prolonged outbreaks with this bacterium, sinks and other water sources in the hospital environment should be considered. By implementing proactive control measures to limit the bacterial load in sinks, the waterborne transmission of P. aeruginosa may be reduced.
A new alternating minimization algorithm for image segmentation

A new alternating minimization algorithm for image segmentation is proposed. Based on the similarities between Ambrosio-Tortorelli model and Perona-Malik model, a new explicit alternating minimization algorithm is developed. The Euler-Lagrange equations of Ambrosio-Tortorelli model is modified to produce sharper edges. The effect of parameters is studied. Numerical results are presented to illustrate the effectiveness of the proposed model.
Malware Detection across Clusters

Due to diverse software vulnerabilities and hardware attacks, user credentials are vulnerable or could land in demilitarized zones. An attempt is made to explore and finally proposed a trust based malware detection based on optimal data classification. An improved glowworm swarm optimization (IGWSO) algorithm is proposed for clustering in the input dataset into different groups. After clustering, recurrent deep neural network based computation is used for deriving different trust levels for cloud information that classify the suspected intrusion. The proposed TMD system implemented in java with cloudsim tool evidently discovers the effectiveness of the algorithm over existing state of art systems on the parameters of high detection, precision, recall and F-measures.
3D full-waveform inversion in ultrasound computed tomography employing a ring-array

Ultrasound computed tomography (USCT) is an emerging medical imaging modality that holds great promise for breast cancer diagnosis. Full-waveform inversion (FWI)-based image reconstruction methods for USCT can produce high spatial resolution and accurate images of the acoustic properties of soft tissues. A common USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers. Volumetric imaging can be achieved by translating the ring-array orthogonally to the imaging plane. Slice-by-slice two-dimensional (2D) reconstruction methods have been implemented to form a composite three-dimensional (3D) volumes by stacking together reconstructed cross-sectional images at each ring-array position. However, this 2D approach does not account for the 3D wave propagation physics and the focusing properties of the transducers, and can result in out-of-plane scattering-based artifacts and inaccuracies. To overcome this, a new 3D time-domain FWI method is proposed for ring-array-based USCT that concurrently utilizes measurement data acquired from multiple positions of the ring-array. A virtual imaging study of ring-array-based USCT that employs a realistic 3D numerical breast phantom was conducted to assess the impact of the number of ring-array measurements on image quality.
Cosmological results from the Planck space mission and their comparison with data from the WMAP and BICEP2 experiments

We review basic results from the European Space Agency's Planck space mission, which are of crucial significance to understanding the origin and evolution of the Universe. The main stages of astrophysical and cosmological data processing pipelines are considered. The Planck results are compared with the data from the NASA WMAP (Wilkinson Microwave Anisotropy Probe) space mission and the BICEP2 (Background Imaging of Cosmic Extragalactic Polarization 2) experiment.
Automatic Helmet And License Plate Recognization using YOLOv5

Now a days the traffic has been increasing abnormally, and also increase in the accident's ratio. One of the main reasons is that most of the motorcyclists do not wear helmets and also the affordability of the vehicles. The existing solutions for capturing the riders without helmet are built based on OCR, CNN, OpenCV -python technologies, and the video surveillance-based system. These methodologies are effective but require significant human assistance, in which the efficiency decreases with time. Therefore, this process has to be automated. Our goal is to fabricate a model that can instantly capture motorcycle riders without wearing helmets from a video of ”. mov” extension using YOLOv5 technology. The captured images are classified in various ranges and also the model uses non_max _suppression to opt the best bounding box and then stores the images of the extracted riders and number plates. And as a further progress, E-challans are sent to the drivers to remind them to be cautious when making future fine payments.
Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints

In various permanent magnet synchronous motor (PMSM) drive applications the torque dynamics are an important performance criterion. Here, time-optimal control (TOC) methods can be utilized to achieve highest control dynamics. Applying the state-of-the-art TOC methods leads to unintended overcurrents and torque over- and undershoots during transient operation. To prevent these unintended control characteristics while still achieving TOC performance the time-optimal model predictive control (TO-MPC) is proposed in this work. The TO-MPC contains a reference prerotation (RPR) and a continuous control set model predictive flux control (CCS-MPFC). By applying Pontryagin's maximum principle, the TOC solution trajectories for states and inputs of the PMSM are determined neglecting current and torque limits. With the TOC solution, a flux linkage reference for the CCS-MPFC is calculated that corresponds to a prerotation of the operating point in the stator-fixed coordinate system. This prerotated flux linkage reference is reached in minimum time without overcurrents and torque over- as well as undershoots by incorporating current and torque limits as time-varying softened state constraints into the CCS-MPFC. Simulative and experimental investigations for linearly and non-LM-PMSMs in the whole speed and torque range show that, compared to state-of-the-art TOC methods, overcurrents and torque over- as well as undershoots are prevented by the proposed TO-MPC.
Automated detection of ribs in chest CT scans and assessment of changes in their morphology between Total Lung Capacity (TLC) and Residual Volume (RV)

Aging of the thoracic musculoskeletal system can result in adverse impacts on lung function. Measurement of rib morphology in chest CT scans and assessment of their changes between full inspiratory, or total lung capacity (TLC), and full expiratory, or residual volume (RV), help examine the impacts of rib cage-related changes on lung function. We present new and automated methods using deep learning, multi-parametric thresholding, and topological analysis to segment and label individual ribs in chest CT scans, compute static morphological features at individual rib locations, and assess their lung volume related changes (ΔLV) between TLC and RV scans. The method was applied on TLC and RV scans from the COPDGene Iowa cohort at baseline visits, and accuracy of rib segmentation and computed metrics were examined by comparing with manually outlined results on TLC and RV scans (n=2×20). An average Dice score of 0.93 was observed in all TLC and RV rib segmentations, and root-mean-square errors for different static and ΔLV metrics were found between 0.7 and 4.9%. Application on a larger population (n=200) revealed a five-year loss of 6.2% (p<.001) in the trendline for ΔLV in the anterior-posterior diameter of the 5th rib with losses of 8.4 and 4.0% for males and females, respectively. Automation of CT-based static and ΔLV metrics of rib morphology and significant evidence of age-related changes and sex-bias establish a novel and effective tool to investigate the influence of different risk factors and comorbidities in patients with chronic lung disease and their impacts on disease progression and clinical outcomes.