Multi-Sensor Fusion for Wheel-Inertial-Visual Systems Using a Fuzzification-Assisted Iterated Error State Kalman Filter.

This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments.

Magneto-Photonic Effect of FeO@SiO Nanorods for Visualizing the Direction of Magnetic Fields with High Spatiotemporal Resolution.

In this work, we demonstrate the visualization of the complex magnetic fields by utilizing the magneto-photonic effect of FeO@SiO nanorod suspension with one-to-one correspondence between the visible colors and magnetic field directions. The selected anisotropic nanorods possess appropriate saturated magnetization and high electrostatic repulsion, which is magnetically direction-responsive but strength-insensitive, accurately detecting the field direction while eliminating the influence from intensity. The combined experiment-simulation study validates the accuracy of the simulation, allowing us to further determine the intensity distribution of the magnetic field.

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles.

As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cleared by cilia or lung mucus. In the last decade, potential immune toxicity arising from exposure to MeONPs has been extensively debated, as lung macrophage is the main pathway for cleaning inhaled exogenous particles.

-to- Extrapolation on Lung Toxicity Induced by Metal Oxide Nanoparticles Data-Mining.

While analyses are commonly employed for chemical risk assessments, predicting chronic lung toxicity induced by engineered nanoparticles (ENMs) still faces many challenges due to complex interactions at multiple nanobio interfaces. In this study, we developed a rigorous method to compile published evidence on the lung toxicity of metal oxide nanoparticles (MeONPs) and revealed previously overlooked -to- extrapolation (IVIVE) relationships. A comprehensive multidimensional data set containing 1102 data points, 75 pulmonary toxicological biomarkers, and 20 features (covering effects, physicochemical properties, and exposure conditions) was constructed.

Improved mechanical performance and forming accuracy of ZrO fixed partial denture based on the digital light processing technology.

Fixed partial dentures are the primary treatment for dentition defects. Digital light processing (DLP) 3D printing technology is an advanced technique with significant advantages and potential in the field of dental restoration, particularly in cases requiring high precision and personalization. However, challenges persist in printing fixed partial dentures that meet the strength requirements for clinical applications.

Mitral Valve Repair Versus Replacement in Patients Undergoing Concomitant Aortic Valve Replacement.

Aim: Mitral valve repair (MVr) is associated with more favourable long-term outcomes than mitral valve replacement (MVR) in cases of isolated mitral valve disease suitable for repair. However, there is debate regarding whether the superiority of MVr extends to patients with concomitant aortic and mitral valve disease. Therefore, this meta-analysis was conducted to compare the survival benefits between aortic valve replacement (AVR) plus MVr with a double valve replacement (DVR).

Effect of TetR Family Transcriptional Regulator PccD on Phytosterol Metabolism of .

Androstenedione (AD) is an important intermediate for the production of steroidal drugs. The process of transforming phytosterols into AD by is mainly the degradation process of the phytosterol side chain, and the excessive accumulation of propionyl-CoA produced by will produce toxic effects, which seriously restricts the transformation performance of strains. In this study, sp.

The Prognostic Values of Serum Liver Enzymes in Intrahepatic Cholangiocarcinoma Patients After Liver Resection: A Multi-Institutional Analysis of 605 Patients.

Purpose: The value of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT), in predicting the prognosis of intrahepatic cholangiocarcinoma (ICC) patients who underwent curative resection has not been elucidated. Therefore, we aimed to construct prognostic nomograms for surgically treated ICC patients.

Methods: The impact of liver enzymes on overall survival (OS) and recurrence-free survival (RFS) was analysed using Kaplan-Meier analysis and evaluated by univariate and multivariate analyses.

Microscopical Observation and Transcriptome Analysis Reveal the Effects of High-Altitude Ecosystem in the Qualities of Different Genetic Varieties Resources.

Improving the biomass and nutritional quality of is important for its breeding and resource conservation, but there are few studies on the effects of high altitude on its biomass and quality. In this study, 27 varieties of were cultivated both at 1600 m and 150 m altitudes to investigate the effect of different altitudes in the biomass and quality traits of . At high altitude, all varieties exhibited decreased picking period, reduced fresh and dry weights, diminished stem length and diameter, as well as lowered nitrogen (N), phosphorus (P), and kalium (K) contents.

Synchronous recognition of amines in oxidative carbonylation toward unsymmetrical ureas.

Unsymmetrical ureas are commonly found in pharmaceuticals and bioactive compounds. However, devising strategies to introduce two distinct amines selectively in the construction of unsymmetrical ureas remains a challenge. In this work, we use a synchronous recognition strategy that takes advantage of radical and nucleophilic activation to discriminate between secondary and primary amines.

Cellulose-based functional textiles through surface nano-engineering with MXene and MXene-based composites.

The emergence of smart textiles with the ability to regulate body temperature, monitor human motion, exhibit antibacterial properties, sound fire alarms, and offer fire resistance has sparked considerable interest in recently. MXene displays remarkable attributes like high metallic conductivity, electromagnetic shielding capability, and photothermal/electrothermal properties. Furthermore, due to the highly polar surface groups, MXene nanosheets show exceptional hydrophilic properties and are able to establish strong connections with the polar surfaces of natural fabrics.

Potassium ion channel modulation at cancer-neural interface enhances neuronal excitability in epileptogenic glioblastoma multiforme.

The central nervous system (CNS) is increasingly recognized as a critical modulator in the oncogenesis of glioblastoma multiforme (GBM), with interactions between cancer and local neuronal circuits frequently leading to epilepsy; however, the relative contributions of these factors remain unclear. Here, we report a coordinated intratumor shift among distinct cancer subtypes within progenitor-like families of epileptic GBM patients, revealing an accumulation of oligodendrocyte progenitor (OPC)-like subpopulations at the cancer-neuron interface along with heightened electrical signaling activity in the surrounding neuronal networks. The OPC-like cells associated with epilepsy express KCND2, which encodes the voltage-gated K channel K4.

Strong and ductile cellulose film improved by the in situ incorporation of a genetically engineered protein conjugated synthetic polymer during bacterial cellulose growth.

Bacterial cellulose (BC) has been extensively applied to fabricate advanced biomaterials, although it remains challenging due to its poor toughness and water stability. Herein a genetically engineered protein-conjugated synthetic polymer is designed to improve BC film's strength and flexibility. Initially, the hybrid polymer is constructed by grafting Family 3 carbohydrate-binding modules (CBM3) to amphoteric polyacrylamide polymer (AmPAM), one of the paper industry's most widely used dry-strength agents.

Active biodegradable bacterial cellulose films with potential to minimize the plastic pollution: Preparation, antibacterial application, and mechanism.

Petroleum-based films have triggered a serious global pollution crisis because they are difficult to recycle, degrade, and reuse. Developing alternative sustainable active films represents a powerful strategy to address these issues. Here, a multifunctional biodegradable bacterial cellulose (BC) film incorporated with guanidine-based polymer (PHGH)/gallic acid (GA) was constructed (termed OBC-PHGH/GA).

Bi-Functional Agarose-Filled Porous Polysulfone Protective Layer for Dendrite-Free Zn Anode.

The uneven electric field and slow Zn desolvation lead to rapid dendrite growth during Zn plating and stripping, which severely deteriorates the performance of Zn metal anodes (ZMAs) in Zn-ion batteries (ZIBs). Although polymer-based artificial protective (PBAP) layers are widely applied to homogenize the electric field of ZMAs, they often fail to promote the desolvation process that eventually induces Zn dendrite growth. Herein, a bi-functional protective layer, comprising a finger-like porous matrix of polysulfone (PSF) and a hydroxyl-rich filler of agarose (AG), is constructed to suppress Zn dendrite growth.

Origin of unique electronic structures of single-atom alloys unraveled by interpretable deep learning.

We uncover the origin of unique electronic structures of single-atom alloys (SAAs) by interpretable deep learning. The approach integrates tight-binding moment theory with graph neural networks to accurately describe the local electronic structure of transition and noble metal sites upon perturbation. We emphasize the complex interplay of interatomic orbital coupling and on-site orbital resonance, which shapes the d-band characteristics of an active site, shedding light on the origin of free-atom-like d-states that are often observed in SAAs involving d10 metal hosts.