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Tailoring electron transfer dynamics across solid-liquid interfaces is fundamental to the interconversion of electrical and chemical energy. Stacking atomically thin layers with a small azimuthal misorientation to produce moiré superlattices enables the controlled engineering of electronic band structures and the formation of extremely flat electronic bands. Here, we report a strong twist-angle dependence of heterogeneous charge transfer kinetics at twisted bilayer graphene electrodes with the greatest enhancement observed near the 'magic angle' (~1.1°). This effect is driven by the angle-dependent tuning of moiré-derived flat bands that modulate electron transfer processes with the solution-phase redox couple. Combined experimental and computational analysis reveals that the variation in electrochemical activity with moiré angle is controlled by a structural relaxation of the moiré superlattice at twist angles of

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. [ABSTRACT FROM AUTHOR]

Dispersion of nanofiller in the polymer matrix is a vital factor that influences the properties of the fabricated nanocomposites at the laboratory level. Characterization techniques like TEM and FESEM, having a small sample size, tend to miss out on the big picture for the analysis of mixing on a larger scale. At the industrial level, conducting such testing with varying experimental conditions is not viable in terms of both cost and time. Through this study, we propose a simple method to examine the extent of dispersion using a simple camera and employing deep learning (DL) models. For this purpose, an analogous study has been performed to study the sensitivity of the processing techniques, to better understand the findings in our previous articles. A two component-colored solution (oil–water) was utilized as a proxy for the nanofiller-polymer matrix system. Different processing methods were employed namely ultrasonication, homogenization, sequential ultrasonication and homogenization and simultaneous ultrasonication and homogenization. The variation in processing technique significantly affects the dispersion which is attributed to the different mixing mechanisms (turbulent, diffusive, and convective) incurred in these processing techniques. Inferences are withdrawn by detecting patterns in a large sample size which highlights that DL models provide us with a holistic viewpoint of real-time observations. It also ameliorates human interpretation by unraveling obscure information which can go unnoticed by human eyes. [ABSTRACT FROM AUTHOR]

Context: Serratus anterior tightness is associated with scapular dyskinesis and overall shoulder dysfunction, which affects the range of motion. The most effective intervention to stretch the serratus anterior is unknown. Objective: To evaluate the effect of a therapist-administered novel serratus anterior stretch (SAS) on shoulder range of motion. Method: This study recruited 30 healthy subjects of age 21.20 (1.69) years, height 1.65 (0.11) m, and weight 60.90 (10.36) kg in equal ratio of males and females who scored 1 or 2 on the shoulder mobility test of functional movement screening. A single intervention of a novel SAS was applied to the shoulder. Outcome variables before and after the SAS included the following: shoulder ROM (flexion, abduction, internal rotation, and external rotation) and functional movements of reaching up behind the back and reaching down behind the neck. Results: A paired t test was used to analyze the data. Following the acute SAS intervention, all shoulder ROM improved significantly (P <.000). The change in internal rotation was 6.00° (7.47°), external rotation was 5.66° (9.35°), abduction was 13.50° (11.82°), flexion was 20° (13.33°), reaching up behind the back was 5.10 (2.21) cm, and reaching down behind the neck was 5.41 (2.89) cm. The most marked improvement was in reaching up behind the back (24.48%) and reaching down behind the neck (22.78%). A very large effect size (>1) was observed across most of the variables. Conclusion: An acute SAS intervention improves shoulder mobility in healthy individuals. It is recommended for the trial on the prevention and rehabilitation of shoulder pathologies with restriction in shoulder mobility. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the leading cause of death worldwide in different cohorts. It is well known that miRNAs have a crucial role in regulating the development of cardiovascular physiology, thus impacting the pathophysiology of heart diseases. MiRNAs also have been reported to be associated with cardiac reactions, leading to myocardial infarction (MCI) and ultimately heart failure (HF). To prevent these heart diseases, proper and timely diagnosis of cardiac dysfunction is pivotal. Though there are many symptoms associated with an irregular heart condition and though there are some biomarkers available that may indicate heart disease, authentic, specific and sensitive markers are the need of the hour. In recent times, miRNAs have proven to be promising candidates in this regard. They are potent biomarkers as they can be easily detected in body fluids (blood, urine, etc.) due to their remarkable stability and presence in apoptotic bodies and exosomes. Existing studies suggest the role of miRNAs as valuable biomarkers. A single biomarker may be insufficient to diagnose coronary artery disease (CAD) or acute myocardial infarction (AMI); thus, a combination of different miRNAs may prove fruitful. Therefore, this review aims to highlight the role of circulating miRNA as diagnostic and prognostic biomarkers in cardiovascular diseases such as coronary artery disease (CAD), myocardial infarction (MI) and atherosclerosis.

X-ray Compton scattering experiments along with parallel first-principles computations were carried out on LiNiO2 to understand the effects of W doping on this cathode material for Li-ion batteries. By employing high-energy x rays exceeding 100 keV, an insight is gained into the fate of the W valence electrons, which are adduced to undergo transfer to empty O 2p energy bands within the active oxide matrix of the cathode. The substitution of W for Ni is shown to increase the electronic conductivity and to enhance the total magnetization per Ni atom. Our study demonstrates that an analysis of line shapes of Compton scattered x rays in combination with theoretical modeling can provide a precise method for an atomic level understanding of the nature of the doping process. [ABSTRACT FROM AUTHOR]