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The cathode in lithium-selenium (Li-Se) batteries has garnered extensive attention owing to its superior specific capacity and enhanced conductivity compared to sulfur. Nonetheless, the adoption and advancement of Li-Se batteries face significant challenges due to selenium's low reactivity, substantial volume fluctuations, and the shuttle effect associated with polyselenides. Single-atom catalysts (SACs) are under the spotlight for their outstanding catalytic efficiency and optimal atomic utilization. To address the challenges of selenium's low chemical activity and volume expansion in Li-Se batteries, through electrospun, we have developed a lotus root-inspired carbon nanofiber (CNF) material, featured internal multi-channels and anchored with molybdenum (Mo) single atoms (Mo@CNFs). Mo single atoms significantly enhance the conversion kinetics of selenium (Se), facilitating rapid formation of Li2Se. The internally structured multi-channel CNF serves as an effective host matrix for Se, mitigating its volume expansion during the electrochemical process. The resulting cathode, Se/Mo@CNF composite, exhibits a high discharge specific capacity, superior rate performance, and impressive cycle stability in Li-Se batteries. After 500 cycles at a current density of 1 C, it maintains a capacity retention rate of 82% and nearly 100% coulombic efficiency (CE). This research offers a new avenue for the application of single-atom materials in enhancing advanced Li-Se battery performance. [ABSTRACT FROM AUTHOR]

Highlights: Si/C Composite and Nanostructure Engineering: Advanced Si/C composites and multidimensional nanostructures address key challenges in silicon anodes, like volume expansion and unstable SEI, enhancing LIBs performance. Artificial SEI, Prelithiation, and Binders: Focus on stable artificial SEI layers, efficient prelithiation, and cutting-edge binders to improve Coulombic efficiency and reduce capacity loss, enhancing Si anode durability and efficiency. Real-World Application and Scalability: Analysis of these strategies highlights scalability and commercial viability, transitioning Si-anode technologies to practical, high-performance LIBs applications. Silicon (Si) has emerged as a potent anode material for lithium-ion batteries (LIBs), but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation, leading to material pulverization and capacity degradation. Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance, yet still grapples with issues like pulverization, unstable solid electrolyte interface (SEI) growth, and interparticle resistance. This review delves into innovative strategies for optimizing Si anodes' electrochemical performance via structural engineering, focusing on the synthesis of Si/C composites, engineering multidimensional nanostructures, and applying non-carbonaceous coatings. Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li+ transport, thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency. We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss. Our review uniquely provides a detailed examination of these strategies in real-world applications, moving beyond theoretical discussions. It offers a critical analysis of these approaches in terms of performance enhancement, scalability, and commercial feasibility. In conclusion, this review presents a comprehensive view and a forward-looking perspective on designing robust, high-performance Si-based anodes the next generation of LIBs. [ABSTRACT FROM AUTHOR]

Stable bacterial communities are essential for maintaining soil functions. However, the role of abundant and rare bacterial taxa in maintaining bacterial community stability remains controversial. To explore the relationship between abundant and rare taxa and bacterial community stability, we used high‐throughput sequencing technology and molecular ecological networks to characterize the distribution of abundant and rare bacterial taxa under different nitrogen (N) fertilization rates (0, 150, 200, and 250 kg N hm−2). Our findings revealed that rare taxa had much higher Simpson's diversity index values than abundant taxa. Moreover, rare taxa Simpson's diversity decreased with increasing N application, while bacterial community differences (Bray–Curtis distance dissimilarity) increased (i.e., bacterial community stability decreased with increasing N application). The network topology showed that rare sub‐communities had higher modularity and a greater proportion of positive links than abundant taxa. The rare bacterial Simpson's diversity was significantly and negatively correlated with bacterial community differences, no correlation occurred between abundant bacterial Simpson's diversity and bacterial community differences. This study underscores the importance of rare species in maintaining bacterial community stability. Consequently, when optimizing agricultural management practices to ensure the stability of bacterial communities and soil functions, it is crucial not only to concentrate consider the roles of abundant species involved in nutrient cycling but also to give greater consideration to the role of rare taxa. [ABSTRACT FROM AUTHOR]

In order to solve the traffic scheduling problem of the WAN exit part, a traffic scheduling optimization method based on SR Policy/EPE was proposed, which simulated and implemented an Internet egress traffic scheduling scheme on the carrier network. The proposed method that can effectively control the flow of business traffic to achieve end-to-end traffic distribution and flexible scheduling of network-wide traffic. Under the premise of ensuring user experience, it effectively improved the utilization of backbone links, optimized the overall network structure, and greatly improved increased network scalability. Apply SR performance measurement function to various performance indicators, and choose the optimal or suboptimal link to solve the WAN exit congestion problem, which proves the feasibility of this solution.

Seamless phase 2/3 design has become increasingly popular in clinical trials with a single endpoint. Trials that define success based on the achievement of all co-primary endpoints (CPEs) encounter the challenge of inflated type 2 error rates, often leading to an overly large sample size. To tackle this challenge, we introduced a seamless phase 2/3 design strategy that employs Bayesian predictive power (BPP) for futility monitoring and sample size re-estimation at interim analysis. The correlations among multiple CPEs are incorporated using a Dirichlet-multinomial distribution. An alternative approach based on conditional power (CP) was also discussed for comparison. A seamless phase 2/3 vaccine trial employing four binary endpoints under the non-inferior hypothesis serves as an example. Our results spotlight that, in scenarios with relatively small phase 2 sample sizes (e.g., 50 or 100 subjects), the BPP approach either outperforms or matches the CP approach in terms of overall power. Particularly, with n1 = 50 and ρ = 0, BPP showcases an overall power advantage over CP by as much as 8.54%. Furthermore, when the phase 2 stage enrolled more subjects (e.g., 150 or 200), especially with a phase 2 sample size of 200 and ρ = 0, the BPP approach evidences a peak difference of 5.76% in early stop probability over the CP approach, emphasizing its better efficiency in terminating futile trials. It's noteworthy that both BPP and CP methodologies maintained type 1 error rates under 2.5%. In conclusion, the integration of the Dirichlet-Multinominal model with the BPP approach offers improvement in certain scenarios over the CP approach for seamless phase 2/3 trials with multiple CPEs. [ABSTRACT FROM AUTHOR]

Aim: Utilizing external information in clinical trials enhances validity by including a wider population and expedites the implementation of adaptive designs, ultimately improving research efficiency. However, current research focused on scenarios in which only the control group benefited from the utilization of external information, while trials involving external information in both experimental and control arms were more complex and might pose challenges when applied in real‐world settings. Methods: To address these concerns, our study pioneered the application of test‐then‐pool, normalized power prior, calibrated power prior, and elastic prior to a two‐arm information borrowing framework and systematically compared their operating characteristics through a series of simulation studies under most and least desirable scenarios. Results: In the most desirable scenarios of information borrowing, all methods managed to control the mean of type I error rates within 5%, among which the normalized power prior, calibrated power prior and elastic prior approaches increased the mean of power from 85.94% to 95%. In the least desirable scenarios, the mean type I error rates for normalized power prior, calibrated power prior and elastic prior approaches exceeded 20%, while the mean power decreased to around 80%. Conclusions: Our findings reveal that the normalized power prior, calibrated power prior and elastic prior approaches are suitable for situations with minimal heterogeneity between historical and current data, whereas the test‐then‐pool approach emerges as a more prudent choice when facing substantial discrepancies between historical and current information for trials consider information borrow in both arms. [ABSTRACT FROM AUTHOR]