Controller Synthesis of Multi-Axial Robotic System Used for Wearable Devices

Wearable devices are commonly used in different fields to help improving performance of movements for different groups of users. The long-term goal of this study is to develop a low-cost assistive robotic device that allows patients to perform rehabilitation activities independently and reproduces natural movement to help stroke patients and elderly adults in their daily activities while moving their arms. In the past few decades, various types of wearable robotic devices have been developed to assist different physical movements. Among different types of actuators, the twisted-string actuation system is one of those that has advantages of light-weight, low cost, and great portability. In this study, a dual twisted-string actuator is used to drive the joints of the prototype assistive robotic device. To compensate the asynchronous movement caused by nonlinear factors, a hybrid controller that combines fuzzy logic rules and linear PID control algorithm was adopted to compensate for both tracking and synchronization of the two actuators.;In order to validate the performance of proposed controllers, the robotic device was driven by an xPC Target machine with additional embedded controllers for different data acquisition tasks. The controllers were fine tuned to eliminate the inaccuracy of tracking and synchronization caused by disturbance and asynchronous movements of both actuators. As a result, the synthesized controller can provide a high precision when tracking simple actual human movements

A multichannel thiacalix[4]arene-based fluorescent chemosensor for Zn²⁺, F⁻ ions and imaging of living cells

The fluorescent sensor (3) based on the 1,3-alternate conformation of the thiacalix[4]arene bearing the coumarin fluorophore, appended via an imino group, has been synthesised. Sensing properties were evaluated in terms of a colorimetric and fluorescence sensor for Zn 2+ and F - . High selectivity and excellent sensitivity were exhibited, and off-on optical behaviour in different media was observed. All changes were visible to the naked eye, whilst the presence of the Zn 2+ and F - induces fluorescence enhancement and the formation of a 1:1 complex with 3. In addition, 3 exhibits low cytotoxicity and good cell permeability and can readily be employed for assessing the change of intracellular levels of Zn 2+ and F -

Geometrical Model for Non-Zero theta_13

Based on Friedberg and Lee's geometric picture by which the tribimaximal Pontecorvo-Maki-Nakawaga-Sakata leptonic mixing matrix is constructed, namely, corresponding mixing angles correspond to the geometric angles among the sides of a cube. We suggest that the three realistic mixing angles, which slightly deviate from the values determined for the cube, are due to a viable deformation from the perfectly cubic shape. Taking the best-fitted results of $\theta_{12}$ and $\theta_{23}$ as inputs, we determine the central value of $\sin^22\theta_{13}$ should be 0.0238, with a relatively large error tolerance; this value lies in the range of measurement precision of the Daya Bay experiment and is consistent with recent results from the T2K Collaboration.Comment: 14 pages, 7 figures. Published version in Phys. Rev.

Bis(4-fluoro­benz­yl-κC)bis­(3-methyl­sulfanyl-1,2,4-thia­diazole-5-thiol­ato-κ2 N 4,S 5)tin(IV)

The mononuclear title molecule, [Sn(C7H6F)2(C3H3N2S3)2], has 2 symmetry. The SnIV atom, located on a twofold rotation axis, is in a skew trapezoidal–bipyramidal geometry, with the basal plane defined by two S,N-chelating 3-methyl­sulfanyl-1,2,4-thia­diazole-5-thiol­ate ligands. The apical positions are occupied by the C atoms of two 4-fluoro­benzyl groups

Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment

With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has a better detection of FDIA compared to the method based on auto-regressive (AR) model

Deep Multimodal Fusion for Generalizable Person Re-identification

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, in this paper, we propose DMF, a Deep Multimodal Fusion network for the general scenarios on person re-identification task, where rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. On top of it, a multimodal fusion strategy is introduced to translate the data of different modalities into the same feature space, which can significantly boost generalization capability of Re-ID model. In the fine-tuning stage, a realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world. Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform previous domain generalization or meta-learning methods. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF

High-Sensitivity C-Reactive Protein: An Independent Risk Factor for Left Ventricular Hypertrophy in Patients with Lupus Nephritis

Objective. To determine the prevalence of left ventricular hypertrophy (LVH) and its associated risk factors in lupus nephritis (LN) patients. Methods. 287 LN patients (age: 38.54 ± 13.31, 262 female) were recruited. Echocardiography and serum high-sensitivity C-reactive protein (hs-CRP) were measured. Their relationship was evaluated by univariate correlation analysis and multivariate regression analysis. Results. The prevalence of LVH in this cohort was 21.25% (n = 61). Serum hs-CRP level was significantly elevated in patients with LVH compared to those without (8.03 (3.22–30.95) versus 3.93 (1.48–9.48) mg/L, P < .01), and correlated with left ventricular mass index (LVMI) (r = 0.314, P = .001). Multivariate regression analysis further confirmed that hs-CRP was an independent risk factor (β = 0.338, P = .002) for LVH in patients with LN. Conclusions. Our findings demonstrated that serum hs-CRP level is independently correlated with LVMI and suggested that measurement of hs-CRP may provide important clinical information to investigate LVH in LN patients

Serum IL-18 Is Closely Associated with Renal Tubulointerstitial Injury and Predicts Renal Prognosis in IgA Nephropathy

Background. IgA nephropathy (IgAN) was thought to be benign but recently found it slowly progresses and leads to ESRD eventually. The aim of this research is to investigate the value of serum IL-18 level, a sensitive biomarker for proximal tubule injury, for assessing the histopathological severity and disease progression in IgAN. Methods. Serum IL-18 levels in 76 IgAN patients and 36 healthy blood donors were measured by ELISA. We evaluated percentage of global and segmental sclerosis (GSS) and extent of tubulointerstitial damage (TID). The correlations between serum IL-18 levels with clinical, histopathological features and renal prognosis were evaluated. Results. The patients were 38.85 ± 10.95 years old, presented with 2.61 (1.43∼4.08) g/day proteinuria. Serum IL-18 levels were significantly elevated in IgAN patients. Baseline serum IL-18 levels were significantly correlated with urinary protein excretion (r = 0.494, P = 0.002), Scr (r = 0.61, P < 0.001), and eGFR (r = −0.598, P < 0.001). TID scores showed a borderline significance with serum IL-18 levels (r = 0.355, P = 0.05). During follow-up, 26 patients (34.21%) had a declined renal function. Kaplan-Meier analysis found those patients with elevated IL-18 had a significant poor renal outcome (P = 0.03), and Cox analysis further confirmed that serum IL-18 levels were an independent predictor of renal prognosis (β = 1.98, P = 0.003)

Unsupervised Single-Scene Semantic Segmentation for Earth Observation

Earth observation data have huge potential to enrich our knowledge about our planet. An important step in many Earth observation tasks is semantic segmentation. Generally, a large number of pixelwise labeled images are required to train deep models for supervised semantic segmentation. On the contrary, strong intersensor and geographic variations impede the availability of annotated training data in Earth observation. In practice, most Earth observation tasks use only the target scene without assuming availability of any additional scene, labeled or unlabeled. Keeping in mind such constraints, we propose a semantic segmentation method that learns to segment from a single scene, without using any annotation. Earth observation scenes are generally larger than those encountered in typical computer vision datasets. Exploiting this, the proposed method samples smaller unlabeled patches from the scene. For each patch, an alternate view is generated by simple transformations, e.g., addition of noise. Both views are then processed through a two-stream network and weights are iteratively refined using deep clustering, spatial consistency, and contrastive learning in the pixel space. The proposed model automatically segregates the major classes present in the scene and produces the segmentation map. Extensive experiments on four Earth observation datasets collected by different sensors show the effectiveness of the proposed method. Implementation is available at https://gitlab.lrz.de/ai4eo/cd/-/tree/main/unsupContrastiveSemanticSeg