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Alkylamines form the backbone of countless nitrogen‐containing small molecules possessing desirable biological properties. Despite advances in amine synthesis through transition metal catalysis and photoredox chemistry, multicomponent reactions that leverage inexpensive materials to transform abundant chemical feedstocks into three‐dimensional α‐substituted alkylamines bearing complex substitution patterns remain scarce. Here, we report the design of a catalyst‐free electroreductive manifold that merges amines, carbonyl compounds and carbon‐based radical acceptors under ambient conditions without rigorous exclusion of air and moisture. Key to this aminative carbofunctionalization process is the chemoselective generation of nucleophilic α‐amino radical intermediates that readily couple with electrophilic partners, providing straightforward access to architecturally intricate alkylamines and drug‐like scaffolds which are inaccessible by conventional means. [ABSTRACT FROM AUTHOR]

In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm's ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832–0.912) in the training cohort and 0.944 (95% CI: 0.890–0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.

Coalbed methane (CBM) can be recovered more effectively through heat injection mining. The slippage effect in coal becomes more significant as CBM is exploited, essential for maintaining consistent production of CBM wells. By "slippage effect," we mean that gas molecules exhibiting non–zero velocity near the wall surface of coal pores, leading to higher gas permeability than liquid permeability. To explore the slippage effect and its control mechanism during heat injection mining, methane seepage experiments with constant effective stress were conducted under five different temperatures and seven pore pressures. An analysis was conducted on the mechanisms by which pore pressure and temperature affect gas slippage effect. Furthermore, the correlation between coal pore structure and the gas slippage effect was revealed using the nuclear magnetic resonance (NMR) technique. The results appear that, first, as pore pressure increases, the slippage effect is suppressed due to the influence of the average molecular free path, despite the positive impact of adsorption–induced matrix expansion. Second, as temperature rises, the average molecular free path and thermal expansion have a positive impact on gas slippage, while matrix shrinkage caused by desorption has a negative impact. The positive impact is stronger, resulting in a continuous enhancement of the slippage effect. Finally, a new slippage factor calculation based on NMR T2 distribution of micropores and transitional pores (<100 nm, T2 < 8.33 ms) was provided, and the method was verified by slippage factor fitted by permeability experimental data. The heat injection mining of CBM can be theoretically guided by the research results. [ABSTRACT FROM AUTHOR]

The corn cob biochar (CCB600) was prepared at 600℃ with wasted corn cob as raw material. The modified corn cob biochar (CCB600-M) was prepared by modifying CCB600 with hydrochloric acid and ultrasonic. CCB600 and CCB600-M were added into constructed rapid infiltration (CRI) systems respectively as one of the filter materials, and their effect on wastewater treatment performance was investigated. The results showed that the average removal rates of COD, NH4+-N and TP in CRI reactor with adding CCB600-M were respectively 4.8%, 6.1% and 18.2% higher than those with adding CCB600, and respectively 9.3%, 11.4% and 39.7% higher than those without adding any biochars. The modified corn cob biochar was a new type of filter materials with excellent performance and low price, which can effectively improve the wastewater treatment performance of CRI systems.

Sheep manure biochar (SMB550) was prepared by limited oxygen pyrolysis at 550°C and applied to constructed rapid infiltration (CRI) systems. The structural characteristics of SMB500 were tested and its effect on the wastewater treatment performance of CRI systems was investigated. The results showed that SMB550 had a large specific surface area, abundant pore structure and rich functional groups, showing a good adsorption potential. Using SMB550 as one of the filter material of CRI system, the average removal rates of COD, NH4+-N and TP were respectively 9.3%, 8.6% and 34.9% higher than those of the blank group, which enhanced the treatment performance of CRI systems for wastewater. As a new type of filter material, SMB550 had a good application prospect in the field of wastewater treatment.

Considering the time-varying tidal parameters in offshore China, we proposed a harmonic analysis method combined with an additional time-varying mode. Tidal analysis was performed on nearly 20 years of sea surface altimetry data from the Jason 1-3, Envisat, and ERS-2 satellites, and 13 tide stations located along the coast of the southeast China Sea. The results show that there are several peak regions for the amplitude and phase lag change rate of the M2 tide in the East China Sea and the South China Sea, which are mainly located in the estuary areas of inland river basins, such as the Yangtze Estuary, the Pearl River Estuary, and the Nanliu Estuary, and the variation ranges are roughly 2.0–4.0 mm/yr and 0.8–2.0°/yr. The variations in tidal parameters were attributed to the changes in water depth and coastline in the estuaries. [ABSTRACT FROM AUTHOR]

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm's solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications. [ABSTRACT FROM AUTHOR]