Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping the 2021 October Flood Event in the Subsiding Taiyuan Basin by Multitemporal SAR Data

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multitemporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multitemporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (preflood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and a statistically homogeneous pixels selection based multitemporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.
Neisseria gonorrhoeae Change in Antibiotic Resistance Caused by a Single Base Pair A Novel Mechanism of High-Level , Broad-Spectrum 2011

The MtrC-MtrD-MtrE multidrug efflux pump of Neisseria gonorrhoeae confers resistance to a diverse array of antimicrobial agents by transporting these toxic compounds out of the gonococcus. Frequently in gonococcal strains, the expression of the mtrCDE operon is differentially regulated by both a repressor, MtrR, and an activator, MtrA. The mtrR gene lies 250 bp upstream of and is transcribed divergently from the mtrCDE operon. Previous research has shown that mutations in the mtrR coding region and in the mtrR-mtrCDE intergenic region increase levels of gonococcal antibiotic resistance and in vivo fitness. Recently, a C-to-T transition mutation 120 bp upstream of the mtrC start codon, termed mtr120, was identified in strain MS11 and shown to be sufficient to confer high levels of antimicrobial resistance when introduced into strain FA19. Here we report that this mutation results in a consensus 10 element and that its presence generates a novel promoter for mtrCDE transcription. This newly generated promoter was found to be stronger than the wild-type promoter and does not appear to be subject to MtrR repression or MtrA activation. Although rare, the mtr120 mutation was identified in an additional clinical isolate during sequence analysis of antibiotic-resistant strains cultured from patients with gonococcal infections. We propose that cis-acting mutations can develop in gonococci that significantly alter the regulation of the mtrCDE operon and result in increased resistance to anti-
Mixed-Effects Models in S and S-PLUS

Hockman concluded (p. 426). “A more truthful statement of purpose served by this book: : : is to promote Shainin’s consulting business by discrediting just about all existing quality technologies,” Hockman stated earlier, “and to provide a brief overview of tools that can be helpful in suf ciently sparse detail to warrant the need to hire Shainin” (p. 425). Criticism in these pages has never been any more pointed or damning. Contrasting to the brutal treatment from the reviewer is the statement that the authors make about the success of the 1E. “The book has sold more than 100,000 copies, has been translated into four European languages, and has made the list of best-selling business books in a few cities,” the authors proclaim in the Preface (p. xx). With the aforementioned Dorian Shainin now deceased, the authors here appear just to be promoting themselves. “A mentor is an important factor in a company’s success,” they note. “The authors have provided this coaching, this hands-on help to more than 100 companies, following introductory seminars” (Preface, p. xx). On the positive side for this second edition (2E) are “more examples of problem solving. ” More questionable in utility is “a chapter that summarizes related techniques,” which are QFD and a whole bunch of mnemonics new to me, including TPM, NOAC, and VE. The authors further claim to “have developed additional DOE techniques” and “a technique called Multiple Environment Over Stress Testing (MEOST), that achieves a breakthrough in reliability. ” One might think that the authors would try to connect their tools to the popularity of Six Sigma. Quite the opposite, the authors claim that Six Sigma gains are accomplished with “creative accounting” (p. 13). The senior author, a former Motorola employee, claims his “ultimate Six Sigma: : : has already become the bible in a few enlightened companies. ” Use this book at your own risk. It is more than a little extreme.
Wrapping Brownian motion and heat kernels II: symmetric spaces

In this paper we extend our previous results on wrapping Brownian motion and heat kernels onto compact Lie groups to various symmetric spaces, where a global generalisation of Rouvi\`ere's formula and the $e$-function are considered. Additionally, we extend some of our results to complex Lie groups, and certain non-compact symmetric spaces.
Adaptive Force Control for Small Celestial Body Sampling

An adaptive force control algorithm for small celestial body sampling for a variety of surface properties is developed. The control algorithm consists of an adaptive controller combined with feedback linearization. When a spacecraft makes contact with the surface, it must maintain a desired contact force in order to capture a sample. The properties of the surface are unknown or uncertain before contact with the surface is made. The adaptive controller performs system identification online to create an input-output model of the feedback linearized system. From the input-output model a block observable canonical form is realized and the control input is determined by model predictive control (MPC) to maintain a desired contact force in spite of the unknown surface properties. The approach is applied to a variety of surface properties with linear and nonlinear contact models.
A learning observer approach for fouling detection and localization in direct contact membrane distillation systems

As the need for freshwater is continuously growing, seawater desalination technologies are increasingly used to meet these demands. The high solutes’ rejection factor and the low energy consumption of Membrane Distillation (MD) technologies make them high potential desalination techniques. However, they suffer from membrane fouling which deteriorates the system’s performance and causes high maintenance costs. This paper presents a novel approach based on a learning observer for detecting and localizing fouling in Direct Contact Membrane Distillation (DCMD) systems which allows to significantly decrease the maintenance costs. This approach has the advantage of being flexible and computationally inexpensive. Before the architecture and the design steps of the proposed observer are presented, the DCMD module layout in the fault free scheme and in presence of fouling is explained. To illustrate the effectiveness of the proposed fouling monitoring approach, several tests were simulated and two scenarios were considered: homogeneous and non-homogeneous fouling evolution along the membrane. The various simulations demonstrated very encouraging results in both fouling estimation and localization.
Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction

—Now-a-days, a digital image can be found almost everywhere, and digital image processing plays a huge role in analyzing and enhancing the image so that it can be delivered in a good condition. Color distortion and loss of image details are the common problems that were faced by low-light image enhancement methods. This paper introduces a low-light image enhancement method that applied the concept of homomorphic filtering, unsharp masking, and gamma correction. The aim of the proposed method is to minimize the two problems stated while producing images of better quality when compared to the other low-light image enhancement methods. An objective evaluation was done on the proposed method, comparing the results produced by the enhanced method with other two existing low-light image enhancement methods. The results obtained showed the proposed method outshines the other two existing low-light image enhancement method in maintaining the image details and producing a natural looking image, achieving the lowest Mean Square Error (MSE) and Lightness Order Error (LOE) scores, and has the highest Features Similarity Index color (FSIMc), Features Similarity Index (FSIM), Structure Similarity Index (SSIM), and Visual information fidelity (VIF) scores. Future studies that should be made on this research are to implement dehaze and denoise functionality into the low-light image as well as enabling it to be applicable in real-time scenarios.
Optimized Sequence Dataset Generation for a Two-Stage Pipelined Microcontroller Instruction-Level Electromagnetic Information Leakage Analysis

With the prevalence of the Internet of Things (IoT) and microcontrollers (MCUs), the security issues of IoT and MCUs have become increasingly important. Side-channel analysis (SCA) is a major threat to such problems. One of the non-invasive SCAs is through electromagnetic information leakage (EM-leak) analysis. The author has developed a machine-instruction-level EM-leak analysis by neural network (NN) model. The NN model analysis needs a large dataset for training and validation. And the dataset should be complete and sufficient. The dataset sufficiency can be achieved by acquiring more data from the already proposed EM-leak measurement platform. However, the completeness issue becomes a challenge due to the pipelined architecture in the target MCU. In this paper, the completeness issue of a NN model dataset for the EM-leak analysis is addressed. Experiments show that the proposed algorithm can find an optimal solution. The contributions of this paper include: it successfully reduces a complex and practical SCA problem into a two-stage pipelined MCU individual EM-leak analysis sequence (2-EMAseq) description, proves that the 2-EMAseq problem has at least one optimal solution, uses this proof to develop an algorithm, and uses this algorithm to find a complete dataset is the target two-stage pipelined MCU dataset for NN model training/validation. Currently, the algorithm can only generate the optimal EM-dataset for two-stage pipelined MCUs. However, there are many MCUs with 4~8 pipeline stages. Whether there is an optimal solution when the stages are more than two is still an open question. And it needs to find proofs or derive heuristics in the future for such MCUs.
A Novel Soft Actuator Based on Mini DC Motor and Supercoiled Polymer Artificial Muscle

This paper describes a novel design of a soft actuator that combines two types of actuation methods: DC motor actuation and supercoiled polymer (SCP) artificial muscle. SCP artificial muscle is used to substitute the traditional cable and attached to the gearmotor. The hybrid actuator intends to alleviate the drawbacks of both actuation methods. This novel actuator features a fast actuation speed and the addition of SCP artificial muscles endow it with a certain degree of compliance. At the same time, the thermal contraction property of SCP artificial muscle can also increase its output force. A soft robotic finger is designed to conFigure the actuator in it. In the experiment section, finger’s stiffness and output force tests are performed under three different actuation methods (single SCP/gearmotor & kite line/gearmotor & SCP). Experiment results demonstrate the superiority of this novel actuator in terms of compliance (compared with gearmotor & kite line actuation), actuation speed, and output force (compared with single SCP actuation). This actuator has the potential to be used for grasping and manipulation of soft robots in the future.
Children’s independent mobility and activity spaces during COVID-19 in Finland

ABSTRACT Children’s Independent Mobility (CIM) is usually higher in Nordic countries, although like in many other countries it is declining over time. COVID-19 has created new large-scale restrictions into the patterns of children’s mobility. We explore children’s and parents’ perceptions of CIM during the pandemic. To better understand how to promote CIM, we use COVID-19 restrictions as a model to investigate how acute changes in play and sports are associated with the level of CIM and activity space. In this mixed-methods study, a total of 22 children (10–12-years-old) and their parents were interviewed, and a public-participatory GIS (PPGIS) questionnaire was completed by 427 children and 177 parents in two small cities in Southern Finland. CIM was measured as the independently traveled distance to and from all places visited during the previous week and as CIM licenses. Activity space was obtained by using a home range model. Based on the interviews, the most typically mentioned rules related to CIM were informing parents of destination and company, and curfews. COVID-19 mainly affected organized sports participation requiring a ride from parent but had little influence on CIM. According to questionnaire data, COVID-19 decreased organized sports in 23% of children but increased outdoor games and play in 17% of children. Increased play was associated with a greater CIM, while decreased sports was associated with a smaller activity space. The findings suggest that in comparison to organized sports, outdoor games and play should be promoted to support CIM.
A Multi-Dimensional Graph Convolution Network for EEG Emotion Recognition

Due to the changeable, high-dimensional, nonstationary, and other characteristics of electroencephalography (EEG) signals, the recognition of EEG signals is mostly limited to independent individuals. To deal with these issues, we propose a multidimensional graph convolution network (MD-GCN), which integrates EEG signals’ temporal and spatial characteristics and can classify emotions more accurately. First, we use that the asymmetry of neuron activity in the left and right hemispheres is very important for emotion prediction to initialize the adjacency matrix and perform preliminary edge prediction without considering node features. Then, we perform the feature fusion on the inception network and then input it into the GCN to learn the interrelationship between channels. Finally, we visually analyze the adjacency matrix. To evaluate the performance of the model, we conduct experiments on the SEED dataset and the SEED-IV dataset. The results show that the predefined adjacency matrix method can improve the accuracy of emotion recognition, and the graph convolution has a better performance than the same type of convolution. It also theoretically shows that the emotional state is mainly by the interaction of important brain regions.
Distributed Invariant Extended Kalman Filter for 3-D Dynamic State Estimation Using Lie Groups

Distributed Kalman filters have been widely studied in vector space and been applied to 2-D target state estimation using sensor networks. In this paper, we introduce a novel distributed invariant extended Kalman filer (DIEKF) that exploits matrix Lie groups and is suitable to track the target’s 6-DOF motion in a 3-D environment. The DIEKF is based on the proposed extended Covariance Intersection (CI) algorithm that guarantees consistency in matrix Lie groups. The DIEKF is fully distributed as each agent only uses the information from itself and the one-hop communication neighbors, and it is robust to a time-varying communication topology and changing blind agents. To evaluate the performance, we apply the algorithm in a camera network to track a target pose. Extensive Monte-Carlo simulations have been performed to analyze the performance. Overall, the proposed algorithm is more accurate and more consistent in comparison with our recent work on the quaternion-based distributed EKF (QDEKF).
Dual-Polarized SIW Leaky-Wave Antenna Based on Mode-Multiplexed Feeding Structure

This letter presents a novel dual-polarized leaky-wave antenna (LWA) based on mode-multiplexed feeding structure (FS). Substrate integrated waveguide with groove arrays is exploited to achieve mode-multiplexed FS and two working modes are excited at the same frequency range utilizing different feeding methods. Thirteen dumbbell-shaped patches are placed above the proposed FS and fed through coupling, achieving leaky-wave radiation. When the FS is excited by the microstrip line port, even-mode is obtained and the LWA works in horizontal polarization. With the slotline port excitation, odd-mode is excited and the LWA works in vertical polarization. Therefore, a dual-polarized LWA is realized by using one single transmission line with one type of radiating elements, leading to mode multiplexing in feeding structure and polarization multiplexing in radiating part. A prototype was fabricated and tested for verification. Measured results reveal that the beams can scan from backward to forward under two polarizations. Within the working bandwidth, the cross polarization below −20 dB is realized in the beam-scanning plane.
A Detuning-Repeater-Based Dynamic Wireless Charging System With Quasi-Constant Output Power and Reduced Inverter Count

High-density coil installation to achieve a smooth power transfer inevitably increases construction costs, reducing the acceptance of dynamic wireless power transfer (DWPT). In this article, a detuning-repeater-based DWPT system is proposed to provide a relatively constant power at a reduced count of inverters. Half of the inverter-connected transmitter coils in conventional DWPT systems are served as passive repeater coils. The repeater relays the power from the transmitters on both sides to take the role of the main power transfer channel when the receiver is moving towards it. A detuning method is proposed to suppress the standby currents inside the idle repeater coils by utilizing the variation of receiver reflecting impedance during the receiver moving towards/away. An optimization method to achieve less power fluctuation is designed based on the detuning rate. The tradeoff between the power fluctuation and current suppression is analyzed. The system performance against parameter variations is also performed. A 100 W down-sized prototype is developed, and the experimental results show that the proposed DWPT system can achieve a quasi-constant output power with a power fluctuation of 8.5% and a system efficiency of around 82% during the entire dynamic process.
An Energy-Efficient Load Balancing Scheme in Heterogeneous Clusters by Linear Programming

The growing power consumption of heterogeneous clusters has attracted great interests from both academia and industry. Although there have been extensive studies on energy-efficient task scheduling algorithms to address this problem, most of them are task-centric. However, as a task is fine-grained enough, the scheduling algorithm can put more efforts on utilizing different power characteristics of heterogeneous servers, which has not been fully explored. This paper presents a Linear Programming-based Energy-efficient Load Balancing (LP-ELB) scheme for heteroge-neous clusters. The power model of each sever in the cluster is obtained in advance, and the overall optimization problem is formulated as a mixed integer linear programming problem. LP-ELB tackles this problem through a two-phase heuristic: a server selection phase for choosing a minimal set of servers, and a load partition phase for calculating load distribution. An optimal solution to the relaxed problem in the second phase can be provided finally. Experiments on both simulated and real-world testbeds validate the effectiveness of the proposed method, and reveal that LP-ELB is superior to competitor algorithms, such as Round-Robin and power-aware Weighted Round-Robin.
Deepfakes on Retinal Images using GAN

—In Deep Learning (DL), Generative Adversarial Networks (GAN) are a popular technique for generating synthetic images, which require extensive and balanced datasets to train. These Artificial Intelligence systems can produce synthetic images that seem authentic, known as Deep Fakes. At present, data- driven approaches to classifying medical images are prevalent. However, most medical data is inaccessible to general researchers due to standard consent forms that restrict research to medical journals or education. Our study focuses on GANs, which can create artificial fundus images that can be indistinguishable from actual fundus images. Before using these fake images, it is essential to investigate privacy concerns and hallucinations thoroughly. As well as, reviewing the current applications and limitations of GANs is very important. In this work, we present the Cycle-GAN framework, a new GAN network for medical imaging that focuses on the generation and segmentation of retinal fundus images.DRIVE retinal fundus image dataset is used to evaluate the proposed model’s performance and achieved an accuracy of 98.19%. imag-ing),
Extracellular Vesicles: The Next Generation Theranostic Nanomedicine for Inflammatory Bowel Disease

Abstract The recent rapid development in the field of extracellular vesicles (EVs) based nanotechnology has provided unprecedented opportunities for nanomedicine platforms. As natural nanocarriers, EVs such as exosomes, exosome-like nanoparticles and outer membrane vesicles (OMVs), have unique structure/composition/morphology characteristics, and show excellent physical and chemical/biochemical properties, making them a new generation of theranostic nanomedicine. Here, we reviewed the characteristics of EVs from the perspective of their formation and biological function in inflammatory bowel disease (IBD). Moreover, EVs can crucially participate in the interaction and communication of intestinal epithelial cells (IECs)-immune cells-gut microbiota to regulate immune response, intestinal inflammation and intestinal homeostasis. Interestingly, based on current representative examples in the field of exosomes and exosome-like nanoparticles for IBD treatment, it is shown that plant, milk, and cells-derived exosomes and exosome-like nanoparticles can exert a therapeutic effect through their components, such as proteins, nucleic acid, and lipids. Moreover, several drug loading methods and target modification of exosomes are used to improve their therapeutic capability. We also discussed the application of exosomes and exosome-like nanoparticles in the treatment of IBD. In this review, we aim to better and more clearly clarify the underlying mechanisms of the EVs in the pathogenesis of IBD, and provide directions of exosomes and exosome-like nanoparticles mediated for IBD treatment.