Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-temporal PolSAR Image Classification Using F-SAE-CNN

Crop classification using polarimetric SAR data is one of the most important applications in Polarimetric Synthetic Aperture Radar (PolSAR) imagery. Obviously, for crop classification, multi-temporal PolSAR data can provide more information than single-temporal PolSAR data, but the processing method of the matching image data is relatively backward. Aiming at the high-dimensional data composed of multi-temporal PolSAR, this paper proposes a method to integrate the stacked auto-encoder network and convolutional neural network, making full use of the dimension reduction advantages of the stacked auto-encoder network and the superior classification performance of the convolutional neural network. By constructing a fusion network, the multi-temporal PolSAR images can be processed once, the classification accuracy can be improved, and the processing steps can be simplified. The experimental results show that, compared with the traditional Stacked Auto-encoder and Convolutional Neural Network (SAE-CNN) classification method, the multitemporal PolSAR image classification method based on Fusion of Stacked Auto-encoder and Convolutional Neural Network (F-SAE-CNN) proposed in this paper has the highest classification accuracy, which effectively combines the advantages of the self-encoding network and the CNN network, and provides a new idea for PolSAR image classification work.
The Impact of Risk Perception on Customer Purchase Intention in e-Commerce Platform

Purpose – This study aims to investigate the impact of risk perception on online purchase intention in e-commerce platforms. Design/Methodology/Approach – This research uses secondary data analysis and an archival study approach to investigate the impact of risk perception on online purchase intention in e-commerce platforms. The data was gathered from 385 customers using e-commerce platforms. Moreover, this research adopted four frameworks from previous research to construct a new conceptual framework. Findings – This research examined the factors that impact risk perception on online purchase intention in e-commerce platforms. The research's findings revealed that perceived CKM tool risk, social risk, product risk, and delivery risk of customers impact e-commerce platforms. Research Limitations/Implications – There are many limitations to researching the factors influencing customer purchase intention on e-commerce platforms. This research is based in China and uses e-commerce platforms, so this study may not completely apply to people from different cultural backgrounds in other nations. Originality/value - This research is about the important influencing variables that customers perceived CKM tool risk, social risk, product risk, delivery risk, and purchase intention.
An Intrusion Detection System (IDS) using Dimensional Reduction Based on Statistical and SDAE to Enhance SVM in Classification Task

The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It is capable of screening an internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity in accordance with the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a preprocessing using statistical approach, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of SVM work in IDS detection. In this research, we tried our model in two experiment scenarios include binary class and multi class classification experiment. We evaluated the performance of our model using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness up to more than 2% in NSL-KDD Dataset. In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection.
Research on Path Planning for Satellite Formation Configuration Changes

For different observation tasks, synthetic aperture radar(SAR) satellite formation needs to adopt different configurations. In this case, how to realize the path planning is an important research problem for configuration transition. In this paper, a sequential convex programming(SCP) is proposed for the swarm reconfiguration that involve avoiding collisions and minimizing fuel. The quadratic performance index of the control variable represents the optimization index of path planning to ensure the optimal fuel consumption. Then, a linear discrete relative orbital dynamic model under the J2 perturbation is constructed. Regarding the dynamic constraints and the prevention of inter-satellite collisions as basic constraints, the path planning problem of configuration transformation is solved as a discretized convex programming problem. The SCP is introduced to realize iterated optimal planning. This algorithm reduces the computational complexity while developing the high control precision. Through experimental analysis, this method can ensure that there is no collision risk between satellites, and the planning converges to complete configuration reconstruction.
On the solution of renewal-type integral equations

A simple method (here called the RS-method) established for solving renewal-type integral equations based on direct numerical Riemann-Stieltjes integration is presented and evaluated. In almost all situations it has shown surprisingly good results in terms of simplicity, convergence and applicability compared with the other known methods. The RS-method is particularly useful when the probability density function has singularities.
Importance of Identifying Cognitive Impairment in Multiple Sclerosis

ABSTRACT: This article aims to highlight the impact of cognitive impairment on outcomes and quality of life for people with multiple sclerosis (MS) and to review current evidence for the efficacy of disease-modifying therapies (DMTs) and other interventions. In addition, we provide clinical practice insights regarding screening and management of cognitive impairment in people with MS. Evidence suggests that cognitive deterioration often accompanies magnetic resonance imaging changes. Neocortical volume and deep grey matter atrophy correlate with cognitive impairment. Similarly, cognitive decline is predictive of a higher lesion burden. Cognitive impairment is an important clinical measure of disability and negatively impacts quality of life. Phase 3 studies suggest that DMTs such as natalizumab, ozanimod and fingolimod may provide long-lasting, clinically meaningful effects on cognition in people with MS. Further data are needed to support the use of adjunct cognitive behavioural and exercise interventions for people with MS who have cognitive impairment. More data are needed to define appropriate management strategies for cognitive impairment in people with MS. Baseline and periodic screening for cognitive impairment and inclusion of cognitive impairment as a clinical trial endpoint will help to inform efforts to manage this important aspect of MS.
Prototyping of 40 GHz Band Orbital Angular Momentum Multiplexing System and Evaluation of Field Wireless Transmission Experiments

We are developing a spatial multiplexing technique based on orbital angular momentum (OAM) that is capable of 1 Tbit/s point-to-point wireless transmissions for the sixth generation mobile communication system. In this paper, we describe a demonstration of 100 Gbit/s wireless transmission over a range of 100 m that used OAM multiplexing of 15 streams with a 1.5 GHz bandwidth (<inline-formula> <tex-math notation="LaTeX">$39.5-41$ </tex-math></inline-formula> GHz). The OAM modes of this system were generated using a Butler matrix that allows discrete Fourier transform (DFT) operations to be performed in analog circuits. We designed <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> Butler matrices to generate OAM modes by combining hybrid couplers and phase shifters. Since this Butler matrix was connected to 16 element antennas, two <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> Butler matrices were connected to make an <inline-formula> <tex-math notation="LaTeX">$8\times 16$ </tex-math></inline-formula> matrix. Furthermore, since these inputs were in 7 OAM mode, one port was terminated to create a <inline-formula> <tex-math notation="LaTeX">$7\times 16$ </tex-math></inline-formula> Butler matrix. It was confirmed that the mode isolation was more than 15 dB in the 1.5 GHz bandwidth. Next, we designed microstrip antennas for a horizontal and vertical polarization uniform circular array (UCA) to radiate the OAM modes. Then, we implemented radio frequency (RF) chains and digital signal processing, including single carrier-frequency domain equalization and adaptive modulation and coding. A transmission experiment conducted in a field line-of-sight environment showed that the system could transmit at 119.45 Gbit/s at a distance of 100 meters, thereby demonstrating the feasibility of wideband OAM transmission in the millimeter-wave band.
The Bioavailability of Glycyrrhizinic Acid Was Enhanced by Probiotic Lactobacillus rhamnosus R0011 Supplementation in Liver Fibrosis Rats

Glycyrrhizinic acid (GL) is clinically applied to treat liver injury, and the bioavailability of orally administered GL is closely related to the gut microbiota. Therefore, the dysbiosis of gut flora in liver injury could significantly influence GL bioavailability. Still, less is known about the impact of probiotic supplementation on the bio-absorption process of oral medication, especially under a pathological state. Herein, probiotic L. rhamnosus R0011 (R0011) with a high viability in the harsh gastrointestinal environment was selected, and the effect of R0011 on the GL bioavailability in rats was investigated. Four groups of rats (n = 6 per group) were included: the normal group (N group), the normal group supplemented with R0011 (NLGG group), CCl4-induced chronic liver injury model (M group), and the model group supplemented with R0011 (MLGG group). Our results showed that liver injury was successfully induced in the M and MLGG groups via an intraperitoneal injection of 50% (v/v) CCl4 solution. Healthy rats supplemented with R0011 could increase the bioavailability of GL by 1.4-fold compared with the normal group by plasma pharmacokinetic analysis. Moreover, the GL bioavailability of MLGG group was significantly increased by 4.5-fold compared with the model group. R0011 directly improved gut microbial glucuronidase and downregulated the host intestinal drug transporter gene expression of multidrug resistance protein 2 (MRP2). More critically, R0011 restored the gut microbiota composition and regulated the metabolic function, significantly enhancing the microbial tryptophan metabolic pathway compared with the pathological state, which may indirectly promote the bioavailability of GL. Overall, these data may provide possible strategies by which to address the low bioavailability of traditional medicine through probiotic intervention.
Ontogenetic and spatial variability in parasite communities of white shrimp Penaeus setiferus (Decapoda: Penaeidae)

Abstract Abstract Understanding the combined effects of multi-parasite infections on their hosts is necessary for documenting parasite impacts and is particularly important for developing effective management strategies for economically important organisms. The white shrimp Penaeus setiferus supports important recreational and commercial fisheries along the southeastern and Gulf coasts of the United States and occupies an important ecological niche in estuarine and offshore habitats throughout these regions. The goal of this study was to identify and assess ontogenetic and spatial variation in white shrimp parasite communities and their relation to shrimp health. We used a series of trawl surveys in tidal creek and open water habitats of an estuary in the southeastern USA to collect and identify parasites of white shrimp using morphological and DNA sequencing techniques. Parasite communities in white shrimp were composed of organisms belonging to 6 classes: Conoidasida (gregarines), Oligohymenophorea (apostome and sessilid ciliates), Microsporea (meiodihaplophasids), Chromadorea (rhabditids), Cestoda (cyclophyllideans, lecanocephalideans and trypanorhynchs) and Trematoda (plagiorchiids). Parasite communities differed significantly among white shrimp life stages and localities. Furthermore, the health condition known as black gill occurred in some shrimp and was significantly related to parasite community structure. Infection metrics for the apostome ciliate Hyalophysa lynni, the trypanorhynch larvae Prochristianella sp. and the rhabditid larvae Hysterothylacium sp. were significantly different between shrimp exhibiting and not exhibiting black gill. These results highlight the importance of understanding parasite communities and the potential interactive effects of multiple parasite infections on shrimp health.
Digital avatars as a resource for weight loss

Avatar based digital triggers are promising tools for initiating weight management. This paper describes the effects of a digital avatar trigger on weight (Study 1) and motivation for weight loss (Study 2) in overweight individuals who were interested in losing weight. Use of a digital avatar to provide particpants with an image of a future self based on projected changes to diet and exercise resulted in ambitious goals and positive change, but a modest degree of weight loss. Timing of exposure to the future-self image and gender were found to influence the degree of weight loss acheived. While digital avatar triggers can effect positive weight management action, a significant gap exists between weight loss goal and weight loss achievements. Individuals need to be adequately resourced to achieve their weight loss goal and targets set need to be realistic and achievable. Consideration also needs to be given to how digital avatar triggers are best utilized to maximise weight loss outcomes.
Hydrogenated/Fluorinated Phase Borophene Nanoribbons as Nano-Interconnects

Density functional theory (DFT) together with non-equilibrium Green's function (NEGF) is used for the theoretical investigation of structural, electronic, and transport properties of zigzag <inline-formula><tex-math notation="LaTeX">$v_{1/6}-30^{\circ }$</tex-math></inline-formula> phase borophene nanoribbons (BNRs). Pristine and fluorinated <inline-formula><tex-math notation="LaTeX">$v_{1/6}-30^{\circ }$</tex-math></inline-formula> phase BNRs are considered for the nanoscale interconnect design. The binding energy computations indicate the improvement in the structural stability of the BNRs with increase in fluorine concentration. The E-k relations reveal the hydrogen (H) and fluorine (F) passivated BNRs are metallic. The quantum transport properties are evaluated using the two-probe geometry method. The current-voltage (I-V) behavior of the considered BNR devices are linear with applied bias. Further, interconnect distributed model is explored and the dynamical parameters are evaluated. The Fermi velocity (<inline-formula><tex-math notation="LaTeX">$v_{f}$</tex-math></inline-formula>) is measured to be highest for the H and F passivated BNR (F-BNR-H) of about 3.784 <inline-formula><tex-math notation="LaTeX">$\times 10^{4}$</tex-math></inline-formula> m/s. The dynamic parameters for the nanoscale interconnects are improved upon fluorinating the BNRs. The calculated values of quantum resistance (<inline-formula><tex-math notation="LaTeX">$R_{Q}$</tex-math></inline-formula>), quantum capacitance (<inline-formula><tex-math notation="LaTeX">$C_{Q}$</tex-math></inline-formula>) and kinetic inductance (<inline-formula><tex-math notation="LaTeX">$L_{K}$</tex-math></inline-formula>) for F-BNR-H are 1.845 K<inline-formula><tex-math notation="LaTeX">$\Omega$</tex-math></inline-formula>, 28.61 <inline-formula><tex-math notation="LaTeX">$nF/m$</tex-math></inline-formula>, and 24.38 <inline-formula><tex-math notation="LaTeX">$nH/\mu m$</tex-math></inline-formula>, respectively. Further, F-BNR-H device is found to have relatively lower delay from delay calculations. The stability and frequency analysis determine that the F-BNR-H has superior stability and bandwidth as compared to Cu interconnects. The interconnect performance is found to reduce with edge roughness and line parasitic effects. These theoretical findings suggest the fluorinated BNRs can be potentially used as the metal interconnect at nanoscale dimensions.
Empirical evidence on the robustness of the weighted symmetric unit root test

Recent research has shown the Dickey–Fuller (Econometrica, 49, pp. 1057–72, 1979) test to suffer severe size distortion in the presence of structural breaks under the unit root null hypothesis. In contrast, further theoretical results have suggested the weighted symmetric Dickey–Fuller test to be robust to such breaks. In this study, the empirical results of the seminal study of Leybourne et al. (Journal of Econometrics, 87, pp. 191–203, 1998) are revisited to provide an empirical illustration of the robustness of the weighted symmetric Dickey–Fuller test to structural breaks.
A 1.8–GΩ Input-Impedance 0.15–μV Input-Referred–Ripple Chopper Amplifier With Local Positive Feedback and SAR-Assisted Ripple Reduction

This article presents a chopper-stabilized multipath current-feedback instrumentation amplifier (CFIA) that can also be reconfigured to a general-purpose operational amplifier (OPA). By utilizing a local positive feedback loop (LPFL), both the CFIA and the OPA benefit from gain-independent impedance boosting while retaining the merits of chopping, i.e., low noise and low offset. In addition, the chopping-induced ripple, one of the main issues of conventional chopper amplifiers, is suppressed by a passive high-pass filter with the SAR-assisted offset calibration. The prototype IC is implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and draws 1.07-mA current from a 5-V supply. The chopper CFIA exhibits the minimal input impedance of 1.8 <inline-formula> <tex-math notation="LaTeX">$\text{G}\Omega $ </tex-math></inline-formula> and the maximum input referred ripple of 0.15 <inline-formula> <tex-math notation="LaTeX">$\mu \text{V}$ </tex-math></inline-formula> (ten samples for both tests). These correspond to at least 1.13<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and 1.3<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvements on the state-of-the-art IAs, respectively. The CFIA and the OPA also achieve excellent power efficiency (the highest GBW/<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm {SUPPLY}}$ </tex-math></inline-formula>), competitive input offset (4 and 2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{V}$ </tex-math></inline-formula>), and input noise density (39 and 20 nV/ <inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz).
Discriminative Nonnegative Tucker Decomposition for Tensor Data Representation

Nonnegative Tucker decomposition (NTD) is an unsupervised method and has been extended in many applied fields. However, NTD does not make use of the label information of sample data, even though such label information is available. To remedy the defect, in this paper, we propose a label constraint NTD method, namely Discriminative NTD (DNTD), which considers a fraction of the label information of the sample data as a discriminative constraint. Differing from other label-based methods, the proposed method enforces the sample data, with the same label to be aligned on the same axis or line. Combining the NTD and the label-discriminative constraint term, DNTD can not only extract the part-based representation of the data tensor but also boost the discriminative ability of the NTD. An iterative updating algorithm is provided to solve the objective function of DNTD. Finally, the proposed DNTD method is applied to image clustering. Experimental results on ORL, COIL20, Yale datasets show the clustering accuracy of DNTD is improved by 8.47–32.17% and the normalized mutual information is improved by 10.43–29.64% compared with the state-of-the-art approaches.
Novel method for fabrication of metal- or oxide-nanoparticle doped silica-based specialty optical fibers

Nanoparticle-doped optical fibers are causing significant scientific interest in different application fields. Nanoparticle-doping of silica glass layers during optical fiber preform fabrication was so far reported by sol-gel and solution doping processes, by flame hydrolysis spraying and by pulling hollow cylinders from nanoparticle suspensions. A new method for fabrication of high quality nanoparticle-doped fibers is suggested. Proposed method is based on "flash vaporization" deposition process, previously reported as method to fabricate rare earth- and metal ion-doped specialty optical fibers. Experiments were made where SiO2 layers were deposited using "flash vaporization"-equipped MCVD system, adding vapors carrying metal or oxide nanoparticles into deposition zone. Analysis of produced preforms confirms presence of nanoparticles in deposited layers, albeit with low deposition rate due to weak thermophoretic forces acting on very small particles or agglomerations. Based on results, a number of improvements were suggested and implemented in fabrication process, device design and choice of precursor materials. "Flash vaporization" method was demonstrated as suitable method for deposition of nanoparticles in silica layers, permitting in-situ fabrication of complete preforms, providing easy upgrade path for existing MCVD and OVD deposition systems and allowing simultaneous co-doping by a wide range of other co-dopants.
Single-Cell RNA Sequencing for the Detection of Clonotypic V(D)J Rearrangements in Multiple Myeloma

Multiple myeloma (MM) has a highly heterogeneous genetic background, which complicates its molecular tracking over time. Nevertheless, each MM patient’s malignant plasma cells (PCs) share unique V(D)J rearranged sequences at immunoglobulin loci, which represent ideal disease biomarkers. Because the tumor-specific V(D)J sequence is highly expressed in bulk RNA in MM patients, we wondered whether it can be identified by single-cell RNA sequencing (scRNA-seq). To this end we analyzed CD138+ cells purified from bone marrow aspirates of 19 samples with PC dyscrasias by both a standard method based on bulk DNA and by an implementation of the standard 10x Genomics protocol to detect expressed V(D)J sequences. A dominant clonotype was easily identified in each sample, accounting on average for 83.65% of V(D)J-rearranged cells. Compared with standard methods, scRNA-seq analysis proved highly concordant and even more effective in identifying clonal productive rearrangements, by-passing limitations related to the misannealing of consensus primers in hypermutated regions. We next validated its accuracy to track 5 clonal cells with absolute sensitivity in a virtual sample containing 3180 polyclonal cells. This shows that single-cell V(D)J analysis may be used to find rare clonal cells, laying the foundations for functional single-cell dissection of minimal residual disease.
Vernier fringe‐counting device for laser wavelength measurements

A vernier fringe‐counting circuit designed for use with a Kowalski‐type wavemeter is described. Wavelength measurements made with this device have a high reproducibility of 5×10−9 (or 1/50 of a fringe) and measured accuracy of 4×10−9. The results of accuracy tests are presented along with systematic errors incurred when the wavelengths of two He–Ne lasers (632.9 nm) are compared by the vernier method.