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Constraining Interlayer Slipping in P2-Type Layered Oxides with Oxygen Redox by Constructing Strong Covalent Bonds
Publisher Correction: Annual migrations, vertical habitat use and fidelity of Atlantic bluefin tuna tracked from waters off the United Kingdom
Mechanism of Hydrogen Generation Catalyzed by a Single Atom and Its Spin Regulation
Sensor-assessed grasping time as a biomarker of functional impairment in rheumatoid arthritis
Abstract Quantifying physical impairment in rheumatoid arthritis (RA) is important to determine disease burden and disability. Therefore, we aimed to define hand function impairments in RA patients using an opto-electronic measurement system (OMS). In this cross-sectional pilot study, spatio-temporal and hand segment data were collected during the fine motor skill Moberg Picking-Up Test (MPUT) and two elementary hand movement tasks in RA patients and healthy controls (HC) using a marker-based OMS. The duration of two MPUT movement phases (grasping, transporting 12 objects) and kinematic characteristics during the elementary movements were evaluated. We analyzed between-group differences using linear mixed-effects models accounting for within-participant clustering of hands and adjusting for age and sex. Measurements were obtained from 24 RA and 23 HC. The mean duration of the grasping phase of MPUT was longer in RA patients compared to HC while transporting times were identical, showing a significant group-phase interaction (p < 0.001). Interphalangeal joint angle ratios were similar in RA and HC (p > 0.05) with a lower ratio in both groups for the task thumb-finger opposition compared to flexion of joints. In RA patients especially grasping objects was impaired, and performance time for a subset of objects may serve as a quantitative biomarker of functional impairment.
Spatiotemporal variation and dynamic simulation of carbon stock based on PLUS and InVEST models in the Li River Basin, China
Enantioselective Synthesis of Chiral 1,4-Dihydroquinolines via Iridium-Catalyzed Asymmetric Partial Hydrogenation of Quinolines
Neuroprotective effect of NSCs-derived extracellular vesicles in Parkinson’s disease models
Covalent Dynamic DNA Networks to Translate Multiple Inputs into Programmable Outputs
Author Correction: Involvement of 8-O-acetylharpagide for Ajuga taiwanensis mediated suppression of senescent phenotypes in human dermal fibroblasts
Proline <i>cis</i>/<i>trans</i> Conformational Selection Controls 14–3–3 Binding
Robust recursive estimation for the errors-in-variables nonlinear systems with impulsive noise
Anion-Guided Hierarchical Assembly of Heterometallic Clusters
Genomic profiling of pediococcus acidilactici BCB1H and identification of its key features for Biotechnological innovation, food technology and medicine
Tailoring CO<sub>2</sub> Adsorption Configuration with Spatial Confinement Switches Electroreduction Product from Formate to Acetate
Underwater image enhancement via multiscale disentanglement strategy
Sialylation Shields Glycoproteins from Oxidative Stress: Mechanistic Insights into Sialic Acid Oxidation and Structural Stability
A taguchi neural network–based optimization of a dual-port, dual-band MIMO antenna encompassing the 28/34 GHz millimeter wave regime
Abstract This study presents a novel printed antenna design that operates at the millimeter-wave frequencies of 28 and 34 GHz, which are crucial for the current and upcoming mobile communication generations. The radiating component in the antenna is a slot-etched rectangular ring that is fed through a stepped impedance microstrip line feed. Using advanced machine learning techniques, the design parameters of the suggested antenna have been fine-tuned to ensure optimal impedance matching at 28 GHz within the frequency range of 27.61–28.49 GHz. Additionally, the antenna also provides excellent impedance matching at 34.5 GHz within the frequency range of 33.61–34.27 GHz. Using the designated antenna, a Multiple Input Multiple Output (MIMO) system with two ports is constructed. The MIMO system’s performance is evaluated by analyzing channel capacity loss (CCL), diversity gain (DG), and envelope correlation coefficient (ECC), which showcases outstanding outcomes. The study further explores the optimization of a antenna’s structure using a Taguchi-based Neural Network (Taguchi NN) approach to predict the reflection coefficient (|S11|) across a frequency range of 27–35 GHz. By systematically varying the gap width (∆w) and shift (∆t), a dataset was generated and used to train the network. The optimal model configuration achieved a validation Mean Square Error (MSE) of 2.244 and an R² of 0.848 enabling reliable prediction of the reflection coefficient (|S11|) without extensive simulations. The findings further highlight the construction and experimental assessment of a single-element antenna and MIMO system, which exhibit excellent impedance matching across both lower and higher frequency bands. The antenna displays a maximum gain of 8.75 and 5.5 dBi at frequencies of 28 and 34 GHz, respectively. The recommended antenna exhibits excellent radiation efficiency across both lower and higher frequency bands, with rates of 98.46% and 99.17%, respectively. In addition, the experimental measurements of the coupling coefficients between the MIMO antenna systems indicate extremely low coupling values. This results in an efficient MIMO system that is well-suited for future millimeter-wave (mm-wave) applications.