Language Functional Connectivity Alterations During Resting State in Brain Arteriovenous Malformation Patients.

Objectives: Unruptured brain arteriovenous malformations (AVMs) typically do not cause aphasia, even when the traditional language areas are affected by the nidus. We attempted to elucidate its language reorganization mechanism by analyzing the alterations in functional connectivity using functional connectivity (FC) and track-weighted static functional connectivity (TW-sFC) approaches.

Methods: This cross-sectional study prospectively enrolled patients with AVMs involving left-hemisphere language areas and healthy controls.

A generalist foundation model and database for open-world medical image segmentation.

Vision foundation models have demonstrated vast potential in achieving generalist medical segmentation capability, providing a versatile, task-agnostic solution through a single model. However, current generalist models involve simple pre-training on various medical data containing irrelevant information, often resulting in the negative transfer phenomenon and degenerated performance. Furthermore, the practical applicability of foundation models across diverse open-world scenarios, especially in out-of-distribution (OOD) settings, has not been extensively evaluated.

An infrared night vision image enhancement algorithm based on cross-level feature fusion.

Infrared night vision images are caused by color overflow and coloring discontinuity due to insufficient light at night, resulting in larger halo area and lower PSNR value after enhancement by single feature fusion method. For this reason, an infrared night vision image enhancement algorithm based on cross-level feature fusion is proposed. This method is used to denoise infrared night vision images, based on smooth wavelet decomposition.

Stable Ultrabroad-Absorbing Radical Achieves Efficient NIR-II Photothermal Conversion via Facile Synthesis.

Inspired by the electron-withdrawing ability of nitroxide radicals, a novel open-shell material, EDOT-TPAO is reported, synthesized via one-step demethylation and oxidation of its closed-shell precursor, EDOT-TPAOMe. Time-dependent density functional theory calculations confirm an acceptor-donor-acceptor configuration of EDOT-TPAO where radical termini act as electron acceptors. This structural transformation narrows the optical bandgap from 2.

Global, regional, and national burden of colorectal cancer among women of childbearing age from 1990 to 2021: a systematic analysis based on the global burden of disease study 2021.

Purpose: Colorectal cancer treatment measures can lead to reduced fertility and premature ovarian failure in women of childbearing age (WCBA), resulting in a major global burden. This study aimed to assess the global, regional and national burden of colorectal cancer (CRC) in WCBA and to provide the most up-to-date and comprehensive information available on WCBA CRC.

Methods: Four main indicators, including prevalence, incidence, mortality, and DALYs, related to CRC in WCBA were extracted from the Global Burden of Disease (GBD) database from 1990 to 2021.

Demonstration of Monolithic Integration of InAs Quantum Dot Microdisk Light Emitters and Photodetectors Directly Grown on On-Axis Silicon (001).

Silicon-based microcavity quantum dot lasers are attractive candidates for on-chip light sources in photonic integrated circuits due to their small size, low power consumption, and compatibility with silicon photonic platforms. However, integrating components like quantum dot lasers and photodetectors on a single chip remains challenging due to material compatibility issues and mode field mismatch problems. In this work, we have demonstrated monolithic integration of an InAs quantum dot microdisk light emitter, waveguide, and photodetector on a silicon platform using a shared epitaxial structure.

An Autonomous Localization Vest System Based on Advanced Adaptive PDR with Binocular Vision Assistance.

Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors.

The Effect of Scratch-Induced Microscale Surface Roughness on Signal Transmission in Radio Frequency Coaxial Connectors.

Electrical connectors play a vital role in ensuring reliable signal transmission in high-frequency microsystems. This study explores the impact of microscale scratch-induced surface roughness on the alternating current (AC) contact impedance of RF coaxial connectors. Unlike traditional approaches that assume idealized surface conditions, controlled micro-defects were introduced at the central contact interface to establish a quantitative relationship between surface morphology and signal degradation.

Adaptive Feature- and Scale-Based Object Tracking with Correlation Filters for Resource-Constrained End Devices in the IoT.

Sixth-generation (6G) wireless technology has facilitated the rapid development of the Internet of Things (IoT), enabling various end devices to be deployed in applications such as wireless multimedia sensor networks. However, most end devices encounter difficulties when dealing a large amount of IoT video data due to their lack of computational resources for visual object tracking. Discriminative correlation filter (DCF)-based tracking approaches possess favorable properties for resource-constrained end devices, such as low computational costs and robustness to motion blur and illumination variations.

A Frontier Review of Semantic SLAM Technologies Applied to the Open World.

With the growing demand for autonomous robotic operations in complex and unstructured environments, traditional semantic SLAM systems-which rely on closed-set semantic vocabularies-are increasingly limited in their ability to robustly perceive and understand diverse and dynamic scenes. This paper focuses on the paradigm shift toward open-world semantic scene understanding in SLAM and provides a comprehensive review of the technological evolution from closed-world assumptions to open-world frameworks. We survey the current state of research in open-world semantic SLAM, highlighting key challenges and frontiers.

A Kalman Filter-Based Localization Calibration Method Optimized by Reinforcement Learning and Information Matrix Fusion.

To address the degradation in localization accuracy caused by insufficient robustness of filter parameters and inefficient multi-trajectory data fusion in dynamic environments, this paper proposes a Kalman filter-based localization calibration method optimized by reinforcement learning and information matrix fusion (RL-IMKF). An actor-critic reinforcement learning network is designed to adaptively adjust the state covariance matrix, enhancing the Kalman filter's adaptability to environmental changes. Meanwhile, a multi-trajectory information matrix fusion strategy is introduced, which aggregates multiple trajectories in the information domain via weighted inverse covariance matrices to suppress error propagation and improve system consistency.

On the Rate-Distortion Theory for Task-Specific Semantic Communication.

Semantic communication has attracted considerable interest due to its potential to support emerging human-centric services, such as holographic communications, extended reality (XR), and human-machine interactions. Different from traditional communication systems that focus on minimizing the symbol-level distortion (e.g.

IDMM-IDS: An efficient and robust intrusion detection system for the IoT based on the inverted Dirichlet mixture model.

The Internet of Things (IoT) has permeated all facets of modern life, offering revolutionary applications from smart homes to industrial automation. However, the widespread adoption of IoT systems has amplified security vulnerabilities, necessitating robust intrusion detection systems (IDSs) to protect these devices. Traditional IDS solutions often face challenges in resource-constrained IoT environments due to high computational demands and limited adaptability to emerging threats.

Deep learning-based quantitative assessment of renal chronicity indices in lupus nephritis.

Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients. However, assessment by pathologists is hindered by challenges such as substantial time requirements, high interobserver variation, and susceptibility to fatigue. This study aims to develop an effective deep learning (DL) pipeline that automates the assessment of CI and provides valuable prognostic insights from a disease-specific perspective.

Van der Waals Magnetic Tunnel Junctions Based on Two-Dimensional 1T-VSe and Rotationally Aligned h-BN Monolayer.

Magnetic tunnel junctions (MTJs) are pivotal for spintronic applications such as magneto resistive memory and sensors. Two-dimensional van der Waals heterostructures offer a promising platform for miniaturizing MTJs while enabling the twist-angle engineering of their properties. Here, we investigate the impact of twisting the insulating barrier layer on the performance of a van der Waals MTJ with the structure graphene/1T-VSe/h-BN/1T-VSe/graphene, where 1T-VSe serves as the ferromagnetic electrodes and the monolayer h-BN acts as the tunnel barrier.

Consensus of flexible Timoshenko manipulators with both disturbance observers and event-triggered distributed estimators.

In this paper, consensus control is investigated for the flexible Timoshenko manipulator agents (FTMAs) system with event-triggered communication. The disturbance observers are initially designed with the purpose of observing the unknown disturbance. Then the distributed estimators are developed to estimate the time-varying target instruction from the leader agent.

Spatial Proteomics Using BiFCPL Identifies Regulators of DMV Formation Involved in Coronavirus Replication.

β-Coronaviruses hijack host factors to remodel host endo-membranes to form double membrane vesicles (DMVs), which act as central hubs for the replication of viral genomes. Understanding the molecular mechanism underlying DMV formation is critical for developing effective antiviral strategies and has garnered significant attention. However, the host factors involved in DMV formation remain scanty.

Anxiety or engaged? Research on the impact of technostress on employees' innovative behavior in the era of artificial intelligence.

Technostress has emerged as a prevalent source of workplace stress as artificial intelligence (AI) becomes increasingly integrated into organizations. However, current literature predominantly focuses on the negative psychological impacts of technostress, highlighting the need for research into its potential benefits. To clarify the impact of technostress on employee innovative behavior, this study proposes a conceptual model grounded in transactional stress theory.

[Treatment of foot and ankle deformities combined with knee and lower limb deformities using QIN Sihe's surgical strategy].

Objective: To summarize the clinical characteristics of foot and ankle deformities combined with knee and lower limb deformities and evaluate the advantages, clinical outcomes, and considerations of QIN Sihe's surgical strategy for treating such complex deformities.

Methods: Between January 2022 and December 2024, 32 patients with foot and ankle deformities combined with knee and lower limb deformities were enrolled. The cohort included 23 males and 9 females, aged 10-67 years (mean, 41.

Phase transition predictions of synergistic contagions using spectral approach.

Synergistic contagions are common in natural and social systems, with network topology playing a crucial role in shaping these dynamics. Despite extensive researches, a comprehensive understanding of how specific structural features influence synergistic contagions, especially their phase transitions, remains lacking. This study demonstrates that the spectral approach can serve as a high-precision analytical tool to describe the impact of network structure, particularly degree heterogeneity and degree correlations, on synergistic contagions.

Artificial intelligence-based multimodal model for the identification of ulcerative colitis with concomitant cytomegalovirus colitis.

Background: Ulcerative colitis (UC), a chronic immune-mediated colon inflammation, impacts patients' quality of life. Immunosuppressive-treated UC patients are prone to opportunistic infections like cytomegalovirus (CMV) infection, which exacerbates UC, causes steroid resistance, and elevates surgery and mortality risks. Identifying CMV colitis from UC exacerbation is difficult due to overlapping symptoms and low biopsy detection rates.

Assessment of spatial-average absorbed power density and peak temperature rise in skin model under localized eletromagnetic exposure.

Numerical dosimetry for assessments of the absorbed power density (APD) and temperature rise has been conducted using multi-layer skin models, incorporating skin, fat, muscle, and other components, providing a scientific foundation for setting exposure limits. However, the influence of the vasculature on dosimetry outcomes remains underexplored. In this study, we developed a synthetic blood vessel model and integrated it into multi-layer skin models.

A Blockchain-Based Secure Data Transaction and Privacy Preservation Scheme in IoT System.

With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. How to achieve fine-grained access control and privacy protection for massive devices while ensuring secure and reliable data circulation has become a key issue that needs to be urgently addressed in the current IoT field.

Minimization of Resource Consumption with URLLC Constraints for Relay-Assisted IIoT.

In relay-assisted Industrial Internet of Things (IIoT) systems with ultra-reliable low-latency communication (uRLLC) requirements, finite blocklength coding imposes stringent resource constraints. In this work, the packet error probability (PEP) and blocklength allocation across two-hop links are jointly optimized to minimize total blocklength (resource consumption) while satisfying reliability, latency, and throughput requirements. The original multi-variable problem is decomposed into two tractable subproblems.

Blockchain-Powered LSTM-Attention Hybrid Model for Device Situation Awareness and On-Chain Anomaly Detection.

With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof data storage and improving prediction accuracy for imbalanced anomaly detection for potential deployment in the Industrial Internet of Things (IIoT) remain critical issues. This study proposes a blockchain-powered Long Short-Term Memory Network (LSTM)-Attention hybrid model: an LSTM-based Encoder-Attention-Decoder (LEAD) for industrial device anomaly detection.