Measuring Raven's Progressive Matrices Combining Eye-Tracking Technology and Machine Learning (ML) Models.

Extended testing time in Raven's Progressive Matrices (RPM) can lead to increased fatigue and reduced motivation, which may impair cognitive task performance. This study explores the application of artificial intelligence (AI) in RPM by combining eye-tracking technology with machine learning (ML) models, aiming to explore new methods for improving the efficiency of RPM testing and to identify the key metrics involved. Using eye-tracking metrics as features, ten ML models were trained, with the XGBoost model demonstrating superior performance.

The two-stage processing of judgment of confidence: evidence from ERP.

Background: The judgment of confidence (JOC) refers to the confidence in the accuracy of the target item individuals have just retrieved and is a typical retrospective metacognitive monitoring process. In the classical paradigm of JOC, JOC occurs after the recognition or recall task. While initially viewed as a single-stage monitoring process, recent research on JOC suggests its internal mechanisms may be more complex, potentially encompassing both retrieval and monitoring processes.

The Symptom Structure and Causal Relationships of Comorbid Anxiety and Depression Among Chinese Primary and Middle School Teachers: A Network Analysis.

Background: In China, as educational reforms progress, the characteristics of teachers' work have undergone significant changes, resulting in extremely high levels of stress that can trigger anxiety and depression. Anxiety and depression often co-occur, with two mainstream theories explaining this co-existence: the tripartite model and the diathesis-stress model. However, systematic research focusing on this population is relatively scarce, and the applicability of these models has not been thoroughly tested.

Using PBPK modeling to supplement clinical data and support the safe and effective use of dolutegravir in pregnant and lactating women.

Optimal dosing in pregnant and lactating women requires an understanding of the pharmacokinetics in the mother, fetus, and breastfed infant. Physiologically-based pharmacokinetic (PBPK) modeling can be used to simulate untested scenarios and hence supplement clinical data to support dosing decisions. A PBPK model for the antiretroviral dolutegravir (mainly metabolized by UGT1A1) was verified using reported exposures in non-pregnant healthy volunteers, pregnant women, and the umbilical cord, lactating mothers, and breastfed neonates.

Identifying Mechanism of RSV for the Treatment of COVID-19 and Idiopathic Pulmonary Fibrosis via Suppressing Inflammation Response Through IL-17 Signaling Pathway from the Perspectives of in silico Study.

Background: Both coronavirus disease 2019 (COVID-19) and idiopathic pulmonary fibrosis (IPF) could cause severe pulmonary injury and have extremely dismal prognoses with a high risk of mortality. Resveratrol (RSV), a natural polyphenol, has promising potential in the treatment of viral infection and pulmonary fibrosis.

Objective: The purpose of this research was to investigate the unclear mechanism of RSV as an anti-COVID-19 and IPF therapy.

Development of Highly Efficient Lamb Wave Transducers toward Dual-Surface Simultaneous Atomization.

Highly efficient surface acoustic wave (SAW) transducers offer significant advantages for microfluidic atomization. Aiming at highly efficient atomization, we innovatively accomplish dual-surface simultaneous atomization by strategically positioning the liquid supply outside the IDT aperture edge. Initially, we optimize Lamb wave transducers and specifically investigate their performance based on the ratio of substrate thickness to acoustic wavelength.

Impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels of college students in China.

Background: Higher education students exhibit heightened sensitivity to environmental changes as they navigate the critical transition from adolescence to adulthood. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to universities worldwide, exemplifying a crisis that profoundly affects the learning outcomes and psychological status of college students. Although it is known that campus lockdown has triggered dramatic changes in lifestyles, learning outcomes, and psychological statuses, in-depth knowledge of the causal relationships among these changes remains largely unclear.

Federated Learning-Oriented Edge Computing Framework for the IIoT.

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge.

Risk and related factors of elbow musculoskeletal diseases: a nationwide cross-sectional survey in China.

Objectives: Although studies have shown that work-related musculoskeletal disorders (WMSDs) are common and continue to be a major source of disability and work time loss, there are few reports about elbow WMSDs. The aim of this study was to explore the prevalence and associated factors of elbow WMSDs.

Methods: The valid questionnaires of 57501 workers from 15 different industries nationwide were collected and the c2 test and logistic regression analysis were applied to reveal the prevalence and risk factors of elbow WMSDs.

GMConv: Modulating Effective Receptive Fields for Convolutional Kernels.

In convolutional neural networks (CNNs), the convolutions are conventionally performed using a square kernel with a fixed N × N receptive field (RF). However, what matters most to the network is the effective receptive field (ERF), which indicates the extent to which input pixels contribute to an output pixel. Inspired by the property that ERFs typically exhibit a Gaussian distribution, we propose a Gaussian Mask convolutional kernel (GMConv).

LHPP expression in triple-negative breast cancer promotes tumor growth and metastasis by modulating the tumor microenvironment.

Triple-negative breast cancer (TNBC) is a highly aggressive and metastatic form of breast cancer that lacks an effective targeted therapy. To identify new therapeutic targets, we investigated the phosphohistidine phosphatase, LHPP, which has been implicated in the development of several types of cancer. However, the full significance of LHPP in cancer progression remains unclear due to our limited understanding of its molecular mechanism.

TiO Electron Transport Layer with p-n Homojunctions for Efficient and Stable Perovskite Solar Cells.

Low-temperature processed electron transport layer (ETL) of TiO that is widely used in planar perovskite solar cells (PSCs) has inherent low carrier mobility, resulting in insufficient photogenerated electron transport and thus recombination loss at buried interface. Herein, we demonstrate an effective strategy of laser embedding of p-n homojunctions in the TiO ETL to accelerate electron transport in PSCs, through localized build-in electric fields that enables boosted electron mobility by two orders of magnitude. Such embedding is found significantly helpful for not only the enhanced crystallization quality of TiO ETL, but the fabrication of perovskite films with larger-grain and the less-trap-states.

Dopamine Sulfonate Ligand-Assisted TiO Deposition Enabling Highly Efficient and Stable Perovskite Solar Cells.

The metal oxide electron transport layers (ETLs) with flat morphology and high electrical quality are essential to manufacture highly efficient perovskite solar cells (PSCs), in which the regulation of the metal oxide deposition process plays a crucial role. Herein, a judiciously designed dopamine sulfonate (DS) ligand-assisted deposition of titanium dioxide (TiO) films approach is implemented based on electrostatic repulsion and steric hindrance of assembled ligands to improve colloidal nanoparticles dispersity in precursor and effectively inhibit their aggregation, which could enable obtaining smooth topography of TiO films and initiating growth of top high-quality perovskite films. Furthermore, sulfonate bridges bonded on the perovskite buried layer that is beneficial to form better buried interface contact and accelerate electron extraction.

Binding patterns of inhibitors to different pockets of kinesin Eg5.

The kinesin-5 family member, Eg5, plays very important role in the mitosis. As a mitotic protein, Eg5 is the target of various mitotic inhibitors. There are two targeting pockets in the motor domain of Eg5, which locates in the α2/L5/α3 region and the α4/α6 region respectively.

Accurate network pharmacology and novel ingredients formula of herbal targeting estrogen signaling for psoriasis intervention.

Ethnopharmacological Relevance: As a common chronic inflammatory skin disease, psoriasis is incompletely understood and brings a lot of distress to patients. The estrogen signaling pathway has been implicated in its pathogenesis, making it a potential therapeutic target. Si Cao Formula (SCF) has demonstrated promise in treating psoriasis clinically.

Synergistic Combination of SbSiTe Additives for Enhanced Average ZT and Single-Leg Device Efficiency of BiSbTe-based Composites.

Thermoelectric materials are highly promising for waste heat harvesting. Although thermoelectric materials research has expanded over the years, bismuth telluride-based alloys are still the best for near-room-temperature applications. In this work, a ≈38% enhancement of the average ZT (300-473 K) to 1.