High-Entropy Oxides for Rechargeable Batteries.

High-entropy oxides (HEOs) have garnered significant attention within the realm of rechargeable batteries owing to their distinctive advantages, which encompass diverse structural attributes, customizable compositions, entropy-driven stabilization effects, and remarkable superionic conductivity. Despite the brilliance of HEOs in energy conversion and storage applications, there is still lacking a comprehensive review for both entry-level and experienced researchers, which succinctly encapsulates the present status and the challenges inherent to HEOs, spanning structural features, intrinsic properties, prevalent synthetic methodologies, and diversified applications in rechargeable batteries. Within this review, the endeavor is to distill the structural characteristics, ionic conductivity, and entropy stabilization effects, explore the practical applications of HEOs in the realm of rechargeable batteries (lithium-ion, sodium-ion, and lithium-sulfur batteries), including anode and cathode materials, electrolytes, and electrocatalysts. The review seeks to furnish an overview of the evolving landscape of HEOs-based cell component materials, shedding light on the progress made and the hurdles encountered, as well as serving as the guidance for HEOs compositions design and optimization strategy to enhance the reversible structural stability, electrical properties, and electrochemical performance of rechargeable batteries in the realm of energy storage and conversion.

Rational Design of Organic Electrocatalysts for Hydrogen and Oxygen Electrocatalytic Applications.

Efficient electrocatalysts are pivotal for advancing green energy conversion technologies. Organic electrocatalysts, as cost-effective alternatives to noble-metal benchmarks, have garnered attention. However, the understanding of the relationships between their properties and electrocatalytic activities remains ambiguous. Plenty of research articles regarding low-cost organic electrocatalysts started to gain momentum in 2010 and have been flourishing recently though, a review article for both entry-level and experienced researchers in this field is still lacking. This review underscores the urgent need to elucidate the structure-activity relationship and design suitable electrode structures, leveraging the unique features of organic electrocatalysts like controllability and compatibility for real-world applications. Organic electrocatalysts are classified into four groups: small molecules, oligomers, polymers, and frameworks, with specific structural and physicochemical properties serving as activity indicators. To unlock the full potential of organic electrocatalysts, five strategies are discussed: integrated structures, surface property modulation, membrane technologies, electrolyte affinity regulation, and addition of anticorrosion species, all aimed at enhancing charge efficiency, mass transfer, and long-term stability during electrocatalytic reactions. The review offers a comprehensive overview of the current state of organic electrocatalysts and their practical applications, bridging the understanding gap and paving the way for future developments of more efficient green energy conversion technologies.

Knockdown of RGMA improves ischemic stroke via Reprogramming of Neuronal Metabolism.

Neuronal energy metabolism dysregulation is involved in various pathologies of Ischemia-reperfusion (I/R), yet the role of RGMA in neuronal metabolic reprogramming has not been reported. In this study, we found that RGMA expression significantly increased after I/R, and compared to control mice, mice with MCAO/R showed an increase in glycolytic metabolic products and the expression of glycolytic pathway proteins. Furthermore, RGMA levels are closely related to neuronal energy metabolism. We discovered that knockdown of RGMA can shift neuronal energy metabolism towards oxidative phosphorylation and the pentose phosphate pathway, thereby protecting mice from ischemic reperfusion injury. Mechanistically, knockdown of RGMA can downregulate PGK1 expression, reducing the increase in glycolytic flux following ischemia reperfusion. Moreover, we found that knockdown of RGMA can reduce the interaction between USP10 and PGK1, thus affecting the ubiquitination degradation of PGK1. In summary, our data suggest that RGMA may regulate neuronal energy metabolism by inhibiting the USP10-mediated deubiquitination of PGK1, thus protecting it from I/R injury. This study provides new ideas for clarifying the intrinsic mechanism of neuronal damage after I/R.

Integrated Analysis of Immune Infiltration and Hub Pyroptosis-Related Genes for Multiple Sclerosis.

Purpose: Studies on overall immune infiltration and pyroptosis in patients with multiple sclerosis (MS) are limited. This study explored immune cell infiltration and pyroptosis in MS using bioinformatics and experimental validation. Methods: The GSE131282 and GSE135511 microarray datasets including brain autopsy tissues from controls and MS patients were downloaded for bioinformatic analysis. The gene expression-based deconvolution method, CIBERSORT, was used to determine immune infiltration. Differentially expressed genes (DEGs) and functional enrichments were analyzed. We then extracted pyroptosis-related genes (PRGs) from the DEGs by using machine learning strategies. Their diagnostic ability for MS was evaluated in both the training set (GSE131282 dataset) and validation set (GSE135511 dataset). In addition, messenger RNA (mRNA) expression of PRGs was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in cortical tissue from an experimental autoimmune encephalomyelitis (EAE) model of MS. Moreover, the functional enrichment pathways of each hub PRG were estimated. Finally, co-expressed competitive endogenous RNA (ceRNA) networks of PRGs in MS were constructed. Results: Among the infiltrating cells, naive CD4+ T cells (P=0.006), resting NK cells (P=0.002), activated mast cells (P=0.022), and neutrophils (P=0.002) were significantly higher in patients with MS than in controls. The DEGs of MS were screened. Analysis of enrichment pathways showed that the pathways of transcriptional regulatory mechanisms and ion channels associating with pyroptosis. Four PRGs genes CASP4, PLCG1, CASP9 and NLRC4 were identified. They were validated in both the GSE135511 dataset and the EAE model by using qRT-PCR. CASP4 and NLRC4 were ultimately identified as stable hub PRGs for MS. Single-gene Gene Set Enrichment Analysis showed that they mainly participated in biosynthesis, metabolism, and organism resistance. ceRNA networks containing CASP4 and NLRC4 were constructed. Conclusion: MS was associated with immune infiltration. CASP4 and NLRC4 were key biomarkers of pyroptosis in MS.

Using mixed reality technique combines multimodal imaging signatures to adjuvant glioma photodynamic therapy.

Background: Photodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT's key operating zones. Methods: We used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival. Results: We constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment. Conclusion: Using the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

Design of Miniature Ultrawideband Active Magnetic Field Probe Using Integrated Design Idea.

This article presents a miniature ultrawideband active magnetic probe which is composed of a passive structure and an active amplification circuit structure. The active circuit mainly contains two chips, specifically an amplification chip (HMC797APM5E) and a power management chip (HMC980LP4E). The maximum size of the probe is no more than 64 × 41.5 mm2. Compared with the passive probe with the same-sized loop, the sensitivity of the proposed probe is enhanced by 25 dB through the active circuit design. The working frequency bandwidth of the proposed probe can cover 9 kHz to 18 GHz. Additionally, the flatness is about ±4 dB in terms of |S21| in the stable working bandwidth. It is efficient for high-frequency near-field scanning.

Construction of a Co/MnO Mott-Schottky Heterostructure to Achieve Interfacial Synergy in the Oxygen Reduction Reaction for Aluminum-Air Batteries.

The rational design of non-noble metal-based electrocatalysts for an efficient oxygen reduction reaction (ORR) is an important research topic to promote the advancement of aluminum-air batteries. In this work, heterostructural Co/MnO nanoparticles encapsulated in a N-doped carbon electrocatalyst were prepared via one-step pyrolysis utilizing different reduction potentials of Co and Mn ions, and the heterointerface between the two phases was confirmed. The prepared catalyst displays Pt/C competitive ORR performance because of the interfacial synergy of a Co/MnO Mott-Schottky (M-S) heterostructure, which leads to boosted conductivity, formation of an M-S barrier, and a reduced oxygen reduction energy barrier for excited electrons. Furthermore, the Co/MnO-based aluminum-air battery displays good discharge performance, demonstrating good feasibility for practical application.

Plasma-Assisted Synthesis of Defect-Rich O and N Codoped Carbon Nanofibers Loaded with Manganese Oxides as an Efficient Oxygen Reduction Electrocatalyst for Aluminum-Air Batteries.

The oxygen reduction reaction (ORR) with sluggish kinetics on the cathode of aluminum-air (Al-air) batteries greatly limits their further development. Here, a new strategy is proposed to synthesize oxygen and nitrogen codoped carbon nanofibers loaded with manganese oxides (MnO/Mn2O3/ONCNF-n) as an efficient electrocatalyst for ORR by using oxygen plasma surface etching. The MnO/Mn2O3/ONCNF-3 exhibit superior ORR performance in an alkaline electrolyte, which is attributed to various active sites including N and O heteroatoms, vacancies, and manganese oxides. Additionally, the fabricated homemade Al-air battery (AAB) with MnO/Mn2O3/ONCNF-3 exhibits a maximum power density of 129.7 mW cm-2, demonstrating comparable performance to AABs based on the commercial Pt/C catalyst. This work provides a new approach of using O2 plasma for enhancing the ORR catalytic activities of carbon materials.

Structure and Interface Modification of Carbon Dots for Electrochemical Energy Application.

Carbon dots (CDs) as new nanomaterials have attracted much attention in recent years due to their unique characteristics. Notably, structure and interface modification (carbon core, edge, defects, and functional groups) of CDs have been considered as valid methods to regulate their properties, which contain electron transfer effect, electrochemical activity, fluorescence luminescent, and so on. Additionally, CDs with ultrasmall size, excellent dispersibility, high specific surface area, and abundant functional groups can guarantee positive and extraordinary effects in electrical energy storage and conversion. Therefore, CDs are used to couple with other materials by constructing a special interface structure to enhance their properties. Here, diverse structural and interfacial modifications of CDs with various heteroatoms and synergy effects are systematically analyzed. And not only several main syntheses of CDs-based composites (CDs/X) are summarized but also the merit and demerit of CDs/X in electrical energy storage are discussed. Finally, the applications of CDs/X in energy storage devices (supercapacitors, batteries) and electrocatalysts for practical applications are discussed. This review mainly provides a comprehensive summary and future prospect for synthesis, modification, and electrochemical applications of CDs.

A Panoramic Localizer Based on Coarse-to-Fine Descriptors for Navigation Assistance.

Visual Place Recognition (VPR) addresses visual instance retrieval tasks against discrepant scenes and gives precise localization. During a traverse, the captured images (query images) would be traced back to the already existing positions in the database images, rendering vehicles or pedestrian navigation devices distinguish ambient environments. Unfortunately, diverse appearance variations can bring about huge challenges for VPR, such as illumination changing, viewpoint varying, seasonal cycling, disparate traverses (forward and backward), and so on. In addition, the majority of current VPR algorithms are designed for forward-facing images, which can only provide with narrow Field of View (FoV) and come with severe viewpoint influences. In this paper, we propose a panoramic localizer, which is based on coarse-to-fine descriptors, leveraging panoramas for omnidirectional perception and sufficient FoV up to 360∘. We adopt NetVLAD descriptors in the coarse matching in a panorama-to-panorama way, for their robust performances in distinguishing different appearances, utilizing Geodesc keypoint descriptors in the fine stage in the meantime, for their capacity of detecting detailed information, formatting powerful coarse-to-fine descriptors. A comprehensive set of experiments is conducted on several datasets including both public benchmarks and our real-world campus scenes. Our system is proved to be with high recall and strong generalization capacity across various appearances. The proposed panoramic localizer can be integrated into mobile navigation devices, available for a variety of localization application scenarios.

A Comparative Study in Real-Time Scene Sonification for Visually Impaired People.

In recent years, with the development of depth cameras and scene detection algorithms, a wide variety of electronic travel aids for visually impaired people have been proposed. However, it is still challenging to convey scene information to visually impaired people efficiently. In this paper, we propose three different auditory-based interaction methods, i.e., depth image sonification, obstacle sonification as well as path sonification, which convey raw depth images, obstacle information and path information respectively to visually impaired people. Three sonification methods are compared comprehensively through a field experiment attended by twelve visually impaired participants. The results show that the sonification of high-level scene information, such as the direction of pathway, is easier to learn and adapt, and is more suitable for point-to-point navigation. In contrast, through the sonification of low-level scene information, such as raw depth images, visually impaired people can understand the surrounding environment more comprehensively. Furthermore, there is no interaction method that is best suited for all participants in the experiment, and visually impaired individuals need a period of time to find the most suitable interaction method. Our findings highlight the features and the differences of three scene detection algorithms and the corresponding sonification methods. The results provide insights into the design of electronic travel aids, and the conclusions can also be applied in other fields, such as the sound feedback of virtual reality applications.

Integrated and Binder-Free Air Cathodes of Co3 Fe7 Nanoalloy and Co5.47 N Encapsulated in Nitrogen-Doped Carbon Foam with Superior Oxygen Reduction Activity in Flexible Aluminum-Air Batteries.

All-solid-sate Al-air batteries with features of high theoretical energy density, low cost, and environmental-friendliness are promising as power sources for next-generation flexible and wearable electronics. However, the sluggish oxygen reduction reaction (ORR) and poor interfacial contact in air cathodes cause unsatisfied performance. Herein, a free-standing Co3 Fe7 nanoalloy and Co5.47 N encapsulated in 3D nitrogen-doped carbon foam (Co3 Fe7 @Co5.47 N/NCF) is prepared as an additive-free and integrated air cathode for flexible Al-air batteries in both alkaline and neutral electrolytes. The Co3 Fe7 @Co5.47 N/NCF outperforms commercial platinum/carbon (Pt/C) toward ORR with an onset potential of 1.02 V and a positive half-wave potential of 0.92 V in an alkaline electrolyte (0.59 V in sodium chloride solution), which is ascribed to the unique interfacial structure between Co3 Fe7 and Co5.47 N supported by 3D N-doped carbon foam to facilitate fast electron and mass transfer. The high ORR performance is also supported by in-situ electrochemical Raman spectra and density functional theory calculation. Furthermore, the fabricated Al-air battery displays good flexibility and delivers a power density of 199.6 mW cm-2 , and the binder-free and integrated cathode shows better discharge performance than the traditionally slurry casting cathode. This work demonstrates a facile and efficient approach to develop integrated air cathode for metal-air batteries.

PALVO: visual odometry based on panoramic annular lens.

Visual odometry has received a great deal of attention during the past decade. However, being fragile to rapid motion and dynamic scenarios prevents it from practical use. Here, we present PALVO by applying panoramic annular lens to visual odometry, greatly increasing the robustness to both cases. We modify the camera model for PAL and specially design the initialization process based on the essential matrix. Our method estimates the camera's poses through two-stage tracking, meanwhile builds the local map using a probabilistic mapping method based on the Bayesian framework and feature correspondence search along the epipolar curve. Several experiments are performed to verify our algorithm, demonstrating that our algorithm provides an extremely competitive performance in robustness to rapid motion and dynamic scenarios, meanwhile achieves the same level of accuracy as the state-of-the-art visual odometry.

Unconstrained self-calibration of stereo camera on visually impaired assistance devices.

Stereo cameras are widely used in wearable visually impaired assistance devices (VIADs). However, the inevitable vibration, shock, and mechanical stress may make the camera pair become misaligned and cause a sharp decline in the quality of the acquired depth map, which significantly influences the performance of VIADs. In this paper, we propose an epipolar-constraint-based unconstrained self-calibration method that requires neither user involvement nor specific environment, while achieving a rotation accuracy of 0.83 mrad and a translation accuracy of 0.42 mm. Several approaches are proposed to address the image matching issues, including blurred images removal, mismatched key points removal, etc. Based on correctly matched key point pairs, a planar quadric-distribution approach is proposed to ensure the quality and consistency of the final key point group. These collection approaches ensure the reliability of key point pairs, which is the most important factor to realize high accuracy with minimum constraint. A comprehensive set of experiments demonstrates the high robustness of the proposed methods, which are suitable for VIADs. We also present a field test with blindfolded users to validate the flexibility and applicability of the approach.

Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired.

It is very difficult for visually impaired people to perceive and avoid obstacles at a distance. To address this problem, the unified framework of multiple target detection, recognition, and fusion is proposed based on the sensor fusion system comprising a low-power millimeter wave (MMW) radar and an RGB-Depth (RGB-D) sensor. In this paper, the Mask R-CNN and the single shot multibox detector network are utilized to detect and recognize the objects from color images. The obstacles' depth information is obtained from the depth images using the MeanShift algorithm. The position and velocity information on the multiple target is detected by the MMW radar based on the principle of a frequency modulated continuous wave. The data fusion based on the particle filter obtains more accurate state estimation and richer information by fusing the detection results from the color images, depth images, and radar data compared with using only one sensor. The experimental results show that the data fusion enriches the detection results. Meanwhile, the effective detection range is expanded compared to using only the RGB-D sensor. Moreover, the data fusion results keep high accuracy and stability under diverse range and illumination conditions. As a wearable system, the sensor fusion system has the characteristics of versatility, portability, and cost-effectiveness.

Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians.

Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely to the scenarios of assisted navigation. Those vehicle-based approaches are vulnerable to viewpoint, appearance and route changes (between database and query images) caused by wearable cameras of assistive devices. Facing these practical challenges, we propose Visual Localizer, which is composed of ConvNet descriptor and global optimization, to achieve robust visual localization for assisted navigation. The performance of five prevailing ConvNets are comprehensively compared, and GoogLeNet is found to feature the best performance on environmental invariance. By concatenating two compressed convolutional layers of GoogLeNet, we use only thousands of bytes to represent image efficiently. To further improve the robustness of image matching, we utilize the network flow model as a global optimization of image matching. The extensive experiments using images captured by visually impaired volunteers illustrate that the system performs well in the context of assisted navigation.

Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation.

Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework.

Polarimetric target depth sensing in ambient illumination based on polarization-coded structured light.

Depth sensing is a basic issue in three-dimensional computer vision, and structured light is one of the most prevailing methods for it. However, complex surroundings and strong ambient illumination are fairly unfavorable to depth sensing based on structured light. Complex surroundings increase computation overhead and require extra effort to be separated from the target object. Strong ambient illumination is unfavorable to the signal-noise ratio of structured light and, thus, increases the difficulty of decoding. In this paper, we propose that the polarization-coded structured light is capable of target enhanced depth sensing in ambient illumination. We present the polarimetric principle, an improved algorithm of polarization-coded structured light, and signal-noise-ratio analysis in ambient illumination. Experimental results show that polarization-coded structured light is efficient and robust for target depth sensing of a complicated environment. The polarization-coded structured light is promising to the target depth sensing in an outdoor scenario and industrial inspection.

Detecting Traversable Area and Water Hazards for the Visually Impaired with a pRGB-D Sensor.

The use of RGB-Depth (RGB-D) sensors for assisting visually impaired people (VIP) has been widely reported as they offer portability, function-diversity and cost-effectiveness. However, polarization cues to assist traversability awareness without precautions against stepping into water areas are weak. In this paper, a polarized RGB-Depth (pRGB-D) framework is proposed to detect traversable area and water hazards simultaneously with polarization-color-depth-attitude information to enhance safety during navigation. The approach has been tested on a pRGB-D dataset, which is built for tuning parameters and evaluating the performance. Moreover, the approach has been integrated into a wearable prototype which generates a stereo sound feedback to guide visually impaired people (VIP) follow the prioritized direction to avoid obstacles and water hazards. Furthermore, a preliminary study with ten blindfolded participants suggests its effectivity and reliability.