SAMCF: Adaptive global style alignment and multi-color spaces fusion for joint optic cup and disc segmentation.

The optic cup (OC) and optic disc (OD) are two critical structures in retinal fundus images, and their relative positions and sizes are essential for effectively diagnosing eye diseases. With the success of deep learning in computer vision, deep learning-based segmentation models have been widely used for joint optic cup and disc segmentation. However, there are three prominent issues that impact the segmentation performance. First, significant differences among datasets collecting from various institutions, protocols, and devices lead to performance degradation of models. Second, we find that images with only RGB information struggle to counteract the interference caused by brightness variations, affecting color representation capability. Finally, existing methods typically ignored the edge perception, facing the challenges in obtaining clear and smooth edge segmentation results. To address these drawbacks, we propose a novel framework based on Style Alignment and Multi-Color Fusion (SAMCF) for joint OC and OD segmentation. Initially, we introduce a domain generalization method to generate uniformly styled images without damaged image content for mitigating domain shift issues. Next, based on multiple color spaces, we propose a feature extraction and fusion network aiming to handle brightness variation interference and improve color representation capability. Lastly, an edge aware loss is designed to generate fine edge segmentation results. Our experiments conducted on three public datasets, DGS, RIM, and REFUGE, demonstrate that our proposed SAMCF achieves superior performance to existing state-of-the-art methods. Moreover, SAMCF exhibits remarkable generalization ability across multiple retinal fundus image datasets, showcasing its outstanding generality.

Effective Solution toward the Issues of Zn-Based Anodes for Advanced Alkaline Ni-Zn Batteries.

Alkaline nickel-zinc (Ni-Zn) batteries, as traditional rechargeable aqueous batteries, possess an obvious advantage in terms of energy density, but their development has been hindered by the anode-concerned problems, Zn dendrites, self-corrosion, passivation, deformation, and hydrogen evolution reaction (HER). Herein, to solve these problems, a dual protective strategy is proposed toward the anode using ZnO as an initial active material, including a C coating on ZnO (ZnO@C) and a thin poly(vinyl alcohol) (PVA) layer coating on the electrode (ZnO@C-PVA). In a three-electrode configuration, the reversible capacity can reach 600 mAh g-1 for the ZnO@C-PVA. Using excessive commercial Ni(OH)2 as the cathode, the alkaline Ni-Zn cells exhibit good electrochemical performance: Discharge capacity can be as high as 640-650 mAh g-1 at 4 A g-1 with a Coulomb efficiency (CE) as high as 97-99% after activity, suggesting low self-corrosion and HER. Capacity retention is 97% after 1200 cycles, indicating rather good durability. The discharge capacity is even slightly increased with the increase of charge/discharge current density (≤8 A g-1), implying good rate performance. Additionally, the discharge voltage can reach 1.8 V (midpoint value) at various current densities, reflecting the fast reaction kinetics of the anode. Most importantly, no Zn dendrites and passivation are observed after long-term cycling. The strategy proposed here can solve the anode-concerned problems effectively, exhibiting a high application prospect.

Fabrication of a Cation Exchange Membrane with Largely Reduced Anion Permeability for Advanced Aqueous K-ion Battery in an Alkaline-Neutral Electrolyte Decoupling System.

Herein, an efficient method to prepare sulfonated polyether ether ketone (SPEEK) based cation exchange membranes (CEMs) is developed, where polyethersulfone (PES) is used as an additive. The optimized membrane of 30 wt.%PES/SPEEK-M exhibits a rather low anion permeability and a high ionic conductivity of 9.52 mS cm-1 together with low volume swelling in water. Meanwhile, tensile strength of the membrane is as high as 31.4 MPa with a tensile strain of 162%. As separators for aqueous K-ion batteries (AKIBs) with decoupled gel electrolytes (Zn anode in alkaline and Prussian blue (FeHCF) cathode in neutral). Discharge voltage of the AKIB can reach 2.3 V. Meanwhile, Zn dendrites can be effectively suppressed in the gel anolyte. Specific capacities of the FeHCF cathode are 116.7 mAh g-1 at 0.3 A g-1 (close to its theoretical value), and 95.0 mAh g-1 at 1.0 A g-1 , indicating good rate performance. Capacity retention of the cathode is as high as 91.2% after 1000 cycles' cycling owing to the well remained neutral environment of the catholyte. There is almost no pH change for the catholyte after cycling, indicating good anion-blocking or cation-selecting ability of the 30 wt.%PES/SPEEK-M, much better than other membranes.

Reliability Evaluation for Clustered WSNs under Malware Propagation.

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.