Miracle-dz/UniFAR-FTUs huggingface dataset:
Capsule Network With Multiscale Feature Fusion for Hidden Human Activity Classification

This article considers the problem of human activity classification behind the walls using ultrawideband (UWB) radar. The complex-valued multiscale feature fusion capsule network (CV-MCNet) is proposed, which consists of a feature extractor, a multiscale feature fusion (MFF) block, and a capsule block. Specifically, the feature extractor with two complex-valued convolutional layers is designed to extract the deep features from the range profiles. Then, the MFF block is developed to enrich the feature representation of the activity. Finally, a capsule block is applied to implicitly encode the spatial relationship among the features in vector form and aggregate the vectors to get accurate classification results. The proposed CV-MCNet is evaluated by real data, and the results show that it achieves better classification performance compared with the deep convolutional neural network (DCNN), convolutional autoencoder (CAE), and complex-valued convolutional neural network (CV-CNN).
Tumor Microenvironment-Responsive Magnetic Nanofluid for Enhanced Tumor MRI and Tumor multi-treatments

We prepared a tumor microenvironment-responsive magnetic nanofluid (MNF) for improving tumor targeting, imaging and treatment simultaneously. For this purpose, we synthesized sulfonamide-based amphiphilic copolymers with a suitable pKa at 7.0; then, we utilized them to prepare the tumor microenvironment-responsive MNF by self-assembly of the sulfonamide-based amphiphilic copolymers and hydrophobic monodispersed Fe3O4 nanoparticles at approximately 8 nm. After a series of characterizations, the MNF showed excellent application potential due to the fact of its high stability under physiological conditions and its hypersensitivity toward tumor stroma by forming aggregations within neutral or weak acidic environments. Due to the fact of its tumor microenvironment-responsiveness, the MNF showed great potential for accumulation in tumors, which could enhance MNF-mediated magnetic resonance imaging (MRI), magnetic hyperthermia (MH) and Fenton reaction (FR) in tumor. Moreover, in vitro cell experiment did not only show high biocompatibility of tumor microenvironment-responsive MNF in physiological environment, but also exhibit high efficacy on inhibiting cell proliferation by MH-dependent chemodynamic therapy (CDT), because CDT was triggered and promoted efficiently by MH with increasing strength of alternating magnetic field. Although the current research is limited to in vitro study, these positive results still suggest the great potential of the MNF on effective targeting, diagnosis, and therapy of tumor.
THE UPCOMING THERAPY FROM CAMEL'S URINE

Cancer presents a serious problem in Egypt with high incidence. Intensive research work. was done to control the disease. The ideal treatment that selectively kill the malignant cell and keep other normal ones safe from hazards of cancer therapy has not yet been achieved. Camel was mentioned in the book of Muslims as special creature. The profit Mohamed advised people to use camel's urine and milk for treatment of some ailments. in the current study camel urine samples were collected from camels reared in wadi Elnotron, El Alameen road in north west desert of Egypt. Urine samples were fractionated using HPLC. Freshly collected urine samples were diluted in different proportions 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64, 1:128, 1:256, 1:512, 1:1024 and 1;2048 with sterile saline solution (0.85% NaCl). Cytotoxic effect of camel urine to cancer cell lines were done. Colon (HCT), lung (A549 ), breast (MCF-7 ) and liver (HepG2) cancers were used in the study. the antiviral effect of camel urine was examined using sterile adenovirus inoculated into pre-cultured Vero cells tissue culture flask. results revealed that camel urine has anticancer effect and antiviral effect up to 8-fold of dilution. the main component defined in fractionated urine was dimethylamine and formamide. The study showed that camel urine had cytotoxic effect for different cancer cell lines and antiviral effect up to 8 folds of dilution. The dimethylamine was a major component in camel urine was known as a component of anticancer chemotherapy.
A Recursive Formula for Computing Central Moments of a Multivariate Lognormal Distribution

In this note, we develop a simple recursive formula for the efficient computation of all central moments of a multivariate lognormal distribution up to a given total order. This recursive scheme is found to speed up the computation by a factor of up to 75 compared to an existing explicit formula in the cases we consider. We also present an application to multi-asset portfolio choice.
Image Classification by Neural Network on Crossbars with Memristor Defects

Memristor devices are promising in terms of speeding up and improving the power efficiency of deep learning systems. Crossbar architectures built using memristors can be used to efficiently implement various in-memory computational operations such as multiply-accumulate and convolution, which are widely used in deep and convolutional neural networks. The modeling of image processing by neural networks built on the basis of memristor crossbars is performed. The influence of memristor crossbar defects on the quality of image recognition is studied. The proportions of defective memristors for which memristor neural networks are able to correctly classify images are determined. Dependences of the performance of memristor neural networks on their architectures are revealed: the more layers in the network, the stronger the influence of defects in memristors on the network performance. The time of software simulation of a memristor crossbar operation is estimated. The high resistance of memristor crossbars to defects with a large size of crossbars is noted. It has been established that various models of memristors, both linear and non-linear, behave almost identically within convolutional neural networks.
One-dimensional edge transport on the surface of cylindrical BixTe3−ySey nanowires in transverse magnetic fields

The geometry of topological insulators (TIs) has a major impact on the magnetoelectric band structure of their surface states. Here, we investigate the surface states of cylindrical TI bismuth telluride selenide nanowires with three different diameters, by parallel and transverse magnetoresistance (MR) measurements. In parallel configuration, we observe Aharonov-Bohm oscillations as well as weak antilocalization, indicating two-dimensional TI surface states. In transverse magnetic fields, we observed MR oscillations that are non-linear against the reciprocal of the magnetic field and thus cannot be explained by two- or three-dimensional states. Instead, our transport data analysis reveals that these MR oscillations are the consequence of one-dimensional edge channels at the nanowire surface that form due to the projection of the external magnetic field on the cylindrically curved surface plane in high magnetic fields. Our observation provides an exotic class of surface states that might be used for electronic and spintronic devices.
Splitting of C IV lines in a QSO absorption-line system

The C IV doublet in the z = 1.795 absorption-line system in the QSO B2 1225 + 317 has been observed with the Multiple Mirror Telescope echelle spectrograph at a resolution of 10 km/s. The profile of C IV, previously known to contain at least three velocity components, is shown to consist of at least eleven. Individual components have Doppler width upper limits of 6.4-13.2 km/s. These low upper limits, plus a study of published profile shapes for several other species, suggest that the observed gas is not in an equilibrium state of collisional ionization, and that electron temperatures are 35,000 K or lower. The width of the aggregate profile (approximately 500 km/s) is larger by a factor of 5 than any C IV or Si IV profile observed through the halo of the Galaxy, while individual components are narrower than those in the Galaxy. Thus, intervening galaxies with halos similar to that of the Galaxy, even if occurring in groups, may not explain this QSO absorption-line system.
Exploring Syrian Refugees’ Access to Medical and Social Support Services Using a Trauma-Informed Analytic Framework

Even after arrival in new countries, refugees may be exposed to traumatic events. This state is exacerbated by contextual stressors, including the resettlement process, asylum proceedings and threats of deportation. This paper is rooted in a trauma-informed framework. We interviewed 16 male Syrian refugee migrant workers employed on a Jordanian farm during crop harvesting season to explore the quality and level of medical care and mental health services they received in light of the framework’s principal dimensions (e.g., safety, trust, intersectionality). We found that this vulnerable group of individuals is living a marginal and marginalized existence and depends on the goodwill of the growers for whom they work to treat them with a modicum of dignity and respect. Second, their itinerancy makes it difficult for this population to take advantage of available medical and mental health services at the nation’s major refugee camps. Finally, our interlocutors preferred their current lives, as isolating and limiting as they are, as superior to full-time residence in the camps, because they perceive their present way of life as according a measure of dignity, self-direction and autonomy they could not enjoy in the camps.
A vorticity dynamics theory of three-dimensional flow separation

A theory of three-dimensional incompressible flow separation is presented in terms of the on-wall signatures of the flow. Some long-standing controversial issues are revisited and answers given, such as the inconsistency of the separation criteria based on the topological theory and “open separation,” and whether a separation line is an asymptote or envelope of neighboring skin-friction lines. General criteria for identifying an “open” or “closed” flow separation zone and separation line (including the initial point of the latter), steady and unsteady, are obtained, which apply to a generic smooth curved wall at any Reynolds numbers. The criteria are found to be most clearly given in terms of on-wall signatures of vorticity dynamics. These are then specified to steady boundary layer separation at large Reynolds numbers. A scale analysis under mild assumptions leads to a three-dimensional triple-deck structure near a generic boundary layer separation line. Criteria are presented for “separation watch,” whi.
A Novel Planetary Gearbox Fault Diagnosis Method for Nuclear Circulating Water Pump With Class Imbalance and Data Distribution Shift

Deep learning (DL) has demonstrated splendid performance in fault diagnosis with sufficient samples and ideal operating environments. However, in practice, it is hard to acquire adequate fault samples from high-reliability equipment, thus inducing challenges in applying DL for intelligent diagnosis. Moreover, data distribution shift occurs due to working load variation or environment noise interference, severely degrading DL models’ performance. Aiming at the above problems, this article proposes a method called Q networks calibrated ensemble (QCE). Specifically, two improved deep Q networks are employed to enhance the attention to critical feature samples and fault samples by quantifying feature distribution and utilizing fault prior knowledge. Afterward, the calibrated Choquet integral is adopted to achieve the models’ ensemble, further enhancing the generalization ability and robustness of the diagnosis results. The proposed method has been verified on the nuclear circulating water pump (NCWP) test bench. Results indicate the superiority and reliability of the QCE method. In addition, results when working load changes demonstrate the outstanding generalization ability of the proposed method for cross-domain fault diagnosis with class imbalance. Furthermore, gradient-weighted class activation mapping (Grad-CAM) visualization verifies that the proposed method effectively learns fault features instead of noise.
Enhanced performance of Nd laser glass by double doping with Cr

Cr and Cu offer the best possibilities of increasing the efficiency of Nd glass lasers. Unfortunately Cu must be discounted as it is pumped in the part of the UV which has to be blocked from the glass to prevent solarisation. Cr to Nd transfer is investigated by various methods. The classical method involving measuring lifetimes is shown to be misleading, at least for this pair of ions. A 45% efficient transfer from Cr to Nd was achieved, resulting in a glass of lower threshold and higher slope efficiency than pure Nd glass. A 35% increase in pumping efficiency was achieved.
Method of Building Detection in Optical Remote Sensing Images Based on SegFormer

An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections.
Stress–dilatancy and force chain evolution

The evolution of internal structure plays a pivotal role in the macroscopic response of granular materials to applied loads. A case in point is the so‐called ‘stress–dilatancy relation’, a cornerstone of Soil Mechanics. Numerous attempts have been made to unravel the connection between stress–dilatancy and the evolution of fabric and contact forces in a deforming granular medium. We re‐examine this connection in light of the recent findings on force chain evolution, in particular, that of collective force chain failure by buckling. This study is focussed on two‐dimensional deformation of dense granular assemblies. Analysis of individual and collective force chain bucklings is undertaken using data from a discrete element simulation. It is shown that the kinematics of force chain buckling lead to significant levels of local dilatation being developed in the buckling force chain particles and their confining first‐ring neighbors. Findings from the simulation are used to guide the development of a lattice model of collective, localized force chain buckling. Consideration of the statics and kinematics of this process yields a new stress–dilatancy relation. The physics of buckling, even at its simplest form, introduces a richness into the stress–dilatancy formulation in a way that preserves the essential aspects of fabric evolution, specifically the buckling mode. Copyright © 2010 John Wiley & Sons, Ltd.
Morphological and Genetic Clonal Diversity within the ‘Greco Bianco’ Grapevine (Vitis vinifera L.) Variety

Grapevine (Vitis vinifera L.) has been propagated vegetatively for hundreds of years. Therefore, plants tend to accumulate somatic mutations that can result in an intra-varietal diversity capable of generating distinct clones. Although it is common that winemakers request specific clones or selections for planting new vineyards, relatively limited information is available on the extent, degree, and morphological impact of the clonal diversity in traditional, highly valued grapevine varieties within production areas protected by geographical denomination of origin. Here, we present a morphological and genetic investigation of the intra-varietal diversity in ‘Greco Bianco’, the grapevine variety used to produce the DOCG and PDO “Greco di Tufo” wine. Seventeen clones from different farms (all within the allowed production area) were phenotypically characterized using ampelographic and ampelometric traits. The clones were also genotyped with Simple Sequence Repeats (SSR) and retrotransposon-based DNA markers (REMAP). The morphological analysis indicated a uniformity in the qualitatively scored traits, and a limited variability for the quantitative traits of the bunch and of the berry composition. The molecular markers also depicted variability among clones, which was more evident with the use of REMAPs. The comparison of the discriminatory information of the three analyses indicated that they provided different estimates of the level of diversity. The evaluation described herein of the clonal variability has implications for the management and protection of clonal selections in ‘Greco Bianco’ and prompts for further multidisciplinary investigations on its possible role in winemaking.
Near-wall streak modification by spanwise oscillatory wall motion and drag-reduction mechanisms

Abstract Direct numerical simulations for fully developed channel flow, subjected to oscillatory spanwise wall motion, have been performed and analysed in an effort to illuminate the fundamental mechanisms responsible for the reduction in turbulent friction drag, observed to result from the spanwise wall motion. A range of statistical data are discussed, including second-moment budgets, joint-probability-density functions, enstrophy and energy-spectra maps. Structural features are also investigated by reference to the response of streak properties to the oscillatory forcing. The unsteady cross-flow straining is shown to cause major spanwise distortions in the streak near-wall structures, leading to a pronounced reduction in the wall-normal momentum exchange in the viscous sublayer, hence disrupting the turbulence contribution to the wall shear stress. The response of the streaks, in terms of their periodic reorientation in wall-parallel planes, the decline and recovery of their intensity during the cyclic actuation, and their wall-normal coherence, is shown to be closely correlated with the temporal variation of the shear-strain vector. Furthermore, a modulating ‘top-to-bottom’ effect, associated with large-scale outer-layer structures, is highlighted and deemed responsible for the observed reduction in the actuation efficiency as the Reynolds number is increased.
Classification and Visualization of Lyric Collections Using Guided LDA

Lyrics are one of the most important components of music, and they have a great impact on the appreciation of songs such as J-POP. Therefore, it is useful to classify and search songs based on lyrics. However, the impression of lyrics is subjective and may be influenced by musical elements other than lyrics, so the criteria for searching for lyrics required by users may vary from person to person. To address this issue, we are working on a research project to support active lyric search by visualizing the distribution of lyrics. Here, it is often difficult to appropriately calculate the distribution of the lyrics because lyrics have a higher degree of lexical freedom than articles and papers. In this study, we propose a method to visualize the distribution of lyrics calculated applying guided LDA (Latent Dirichlet Allocation) that interactively consumes guided words. This method facilitates the iterative visualization of lyric classification results based on the users' viewpoints. It also makes it possible to search for songs by focusing only on lyrics without taking other musical elements into account. Users can observe the differences in individuality and tendency of songs and artists, and the diversity of lyrics, by using the visualization results.
Assessment of Clinical Indicators Registered on Admission to the Hospital Related to Mortality Risk in Cancer Patients with COVID-19

Background: Oncology patients are a particularly vulnerable group to the severe course of COVID-19 due to, e.g., the suppression of the immune system. The study aimed to find links between parameters registered on admission to the hospital and the risk of later death in cancer patients with COVID-19. Methods: The study included patients with a reported history of malignant tumor (n = 151) and a control group with no history of cancer (n = 151) hospitalized due to COVID-19 between March 2020 and August 2021. The variables registered on admission were divided into categories for which we calculated the multivariate Cox proportional hazards models. Results: Multivariate Cox proportional hazards models were successfully obtained for the following categories: Patient data, Comorbidities, Signs recorded on admission, Medications used before hospitalization and Laboratory results recorded on admission. With the models developed for oncology patients, we identified the following variables that registered on patients’ admission were linked to significantly increased risk of death. They are: male sex, presence of metastases in neoplastic disease, impaired consciousness (somnolence or confusion), wheezes/rhonchi, the levels of white blood cells and neutrophils. Conclusion: Early identification of the indicators of a poorer prognosis may serve clinicians in better tailoring surveillance or treatment among cancer patients with COVID-19.
PASTO: Enabling Secure and Efficient Task Offloading in TrustZone-Enabled Edge Clouds

Offloading tasks to edge servers has been regarded as a potential way to solve the computation resource poverty problem of the end devices like autonomous vehicles. However, due to the sharing and openness features of edge computing, it raises severe security problems. In order to combat such issues, Trust Execution Environment (TEE), such as TrustZone, is advocated to strength the security of task offloading to edge computing in the hardware level. For the exploration of TrustZone, the inevitable involvement of cryptographic operations make existing offloading strategies not applicable, or not efficient enough, any more. In addition, TrustZone does not allow multiple tasks to coexist and execute on one CPU core at the same time. Taking the above issues into consideration, we investigate a secure task offloading problem for minimizing the total task completion time under the constraint of energy budget. To address this problem, we propose a Priority-aware Secure Task Offloading (PASTO) algorithm and evaluate the performance of PASTO by both numerical analysis and prototype based experiments. All the experiment results show that PASTO can effectively reduce the total task completion time in comparison with other state-of-the-art offloading approaches in TrustZone-enabled edge clouds.