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The majority of limestone reservoirs in China are characterized by deep burial， low porosity， and low permeability and are influenced by factors such as elevated temperatures， high closure pressures， and strong heterogeneity. Conventional hydrochloric acid fracturing techniques often fail to achieve non-uniform etching on such reservoirs， resulting in poor transformation effects and hindering the efficient development of such reservoirs. The acid fracturing technology of multi-stage alternating injection was used to facilitate non-uniform etching of fracture wall surfaces by iteratively forming viscous fingering in the prepad. However， the law governing the acid-etching fracturing of multi-stage alternating injection remains unclear， and the acid injection stage boundaries are not determined. At present， acid fracturing design typically relies on empirical approaches. To address these gaps， this study built an acid fracturing model of multi-stage alternating injection for limestone reservoirs by considering the influence of viscosity variation and alternating stages based on a two-scale continuous acidification model. Based on the acid fracturing model of multi-stage alternating injection， the influence of acid viscosity， acid injection rate， and acid injection stages on the acid fracture effects was analyzed by combining the acid rock reaction kinetics， acid-etched fracture experimental data， and numerical simulation results. The acid-etching law of multi-stage alternating injection in limestone reservoirs and the acid injection stage boundaries were determined， and the breakthrough volume of acid fluid was introduced to provide the basis for site optimization. The findings suggest that to enhance the non-uniform etching of fracture wall surfaces and obtain high conductivity of acid-etching fractures， an alternating injection process involving hydrochloric acid and thickened acid should be adopted for pure limestone reservoirs at a formation temperature of 90 °C. 0.4% HPAM-thickened acid should be added when the acid injection rate is 5 m3/min， with an optimal acid injection stage of no more than three. At this time， the breakthrough volume of acid fluid is the smallest， which is suitable for on-site construction.

Hydraulic fracturing technology has become a key technical means to improve coalbed methane (CBM) yield. The evolution of CBM reservoir permeability before and after hydraulic fracturing reflects the induced fracturing level. By means of fracturing and stress-permeability experiments on four high rank coal samples of No.3 coal seam of the Permian Shanxi Formation in Sihe Coal Mine, Jincheng Mining Area, Shanxi Province, the permeability changes before and after fracturing are tested, and the evolution of pillar coal sample permeability with stress before and after fracturing is revealed. The results show that under the condition of constant confining pressure and axial pressure, the pore pressure of coal samples increases gradually with the increase of injection pressure. When the injection pressure is greater than the fracture pressure, the coal sample breaks, and the fracture pressure of the coal sample increases linearly with the increase of the confining pressure. Under the same injection pressure, the effective stress increases with the increase of confining pressure and axial pressure. The permeability of coal samples before and after hydraulic fracturing decreases exponentially with the increase of effective stress, and the permeability after fracturing is significantly greater than that before fracturing. The permeability improvement coefficient (PIC) of CBM reservoir is used to evaluate the permeability improvement effect of coal samples. The PIC of four test coal samples increase exponentially with the increase of effective stress, but decrease with the increase of confining pressure. The permeability of CBM reservoir mainly depends on the degree of fracture development and the size of fracture width in coal. The permeability of fractured reservoir is in direct proportion to the 3rd power of fracture width. After hydraulic fracturing, the average fracture length, fracture porosity and fracture width increase by 70.81%−253.25%, 171.88%−383.02% and 20.31%−32.43%, respectively. The fracture width decreases with the increase of effective stress in a negative exponential function. The test results are compared with the measured coal reservoir permeability before and after hydraulic fracturing. The two have good consistency, which explores an effective way for the evaluation of the effect of hydraulic fracturing of coal reservoirs.

In the pursuit of digital transformation, college libraries have increasingly embraced the promotion of digital reading as a critical initiative. While numerous studies have delved into the strategies employed by college libraries in their digital transformation endeavors, there remains a lack of research elucidating the direct influence of digital reading on reader service satisfaction within these institutions. Drawing upon the service quality model, this paper aims to address this gap by examining the multifaceted influence of digital reading on reader service satisfaction in college libraries. By examining the various dimensions of digital reading services, this study employs the fsQCA approach to uncover specific combinations that contribute to heightened levels of reader service satisfaction. The results reveal three distinct configurations that can explain the high level of reader service satisfaction. By elucidating these critical relationships, this research not only provides a contribution to the research regarding the evolving role of college libraries but also provides practical insights for college libraries aspiring to realize digital transformation by promoting digital reading. [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.

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.

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]