Pile foundations, as one of the main foundation forms for bridges and offshore wind power structures, are prone to scour pits around them under the long-term action of water flow, leading to a decrease in bearing capacity. Traditional pile foundation scour prevention measures, such as the construction of protective jetties and riprap protection, are cumbersome and ineffective. Considering the inevitable generation of a large amount of spoil in engineering construction, by optimizing the performance of cement-stabilized soil, it is expected to use the discarded spoil for pile foundation scour management. Aiming at the underwater anti-dispersive cement-stabilized soil based on kaolin, 67 sets of single-factor rotation experiments were carried out to study the effects of changes in the addition of anti-dispersive agents ethylene-vinyl acetate copolymer (EVA), hydroxypropyl methylcellulose (HPMC) from 0‰ to 1‰, cement content from 8% to 14%, and water content from 1.4 to 2 times the liquid limit on the anti-dispersion performance, fluidity, and 7d and 28d unconfined compressive strength of the cement soil. The results show that the anti-dispersive agent HPMC can maximize the anti-dispersion performance of the cement soil, with the addition increased from 0‰ to 1‰, the anti-dispersion performance of the cement soil increased by 76.1%, but the fluidity decreased by 54.0%, and the strength of the 28d age cement soil increased by about 52.9%. Anti-dispersive agents can be added to quickly improve the anti-dispersion performance of the cement soil in pile foundation scour management, but attention should also be paid to its weakening effect on the fluidity of the cement soil; the increase in water content has the greatest impact on the fluidity of the cement soil, with the water content increased from 1.4 times the liquid limit to twice the liquid limit, the fluidity increased by 80.3%; the cement content increased from 8% to 14%, the unconfined compressive strength of the cement soil increased by more than double, and the anti-dispersion performance increased by 26.8%. Based on the experimental results, the recommended mix ratio of kaolin-based cement soil for pile foundation scour repair is: 0.75‰ EVA addition, 1.6 times the liquid limit water content, 10% cement content.

From the perspective of the innovation ecosystem, this study investigates the specific paths through which regional innovation ecosystems drive high-quality economic development in 31 mainland provinces and cities in China, using the fuzzy-set Qualitative Comparative Analysis (fsQCA). It was discovered that: (a) Within regional innovation ecosystems, multiple concurrent causal relationships characterized by interlocking alignments exist among innovation agents, resources, and environments, leading to asymmetrical configurational outcomes between high-quality economic and non-high-quality economic development. (b) On the basis of different configurations of system elements, four paths driving high-quality regional economic development were revealed, each demonstrating a "many paths, one destination" characteristic. These include the innovation agent-aggregated regional innovation ecosystem, the diversified development integrated regional innovation ecosystem, the human resource-supported regional innovation ecosystem under market environment dominance, and the economic resource-driven regional innovation ecosystem under market environment dominance. (c) Under specific conditions, substitutive relationships between conditions of innovation agents and the innovation environment within the system were observed. The findings enrich the research perspective on high-quality economic development and offer path references and empirical evidence for regions aiming to construct effective innovation ecosystems to drive high-quality economic development.

Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training. Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.

Among the various heat transfer mechanisms, boiling heat transfer is distinguished by its capacity to dissipate substantial heat via the latent heat of vaporization with minimal temperature differentials. This phenomenon is pivotal across a range of industrial applications, including the cooling of macro- and micro-electronic devices, boiler tubes in power generation plants, evaporators in refrigeration systems, and nuclear reactors, where the nucleate pool boiling regime and two-phase flow are of particular interest. The drive to enhance heat exchange systems' efficiency has consistently focused on minimizing heat loss through system miniaturization. This investigation employs numerical simulations via the Fluent software to elucidate the heat transfer and cooling processes facilitated by nanofluids with varied concentrations on differently shaped finned surfaces, alongside the utilization of water and ethylene glycol as base fluids. Specifically, the thermal performance of $\mathrm{Al}_2 \mathrm{O}_3$-water nanofluids at different concentrations (0, 0.3, 0.6, 1, 1.2, and 1.4 percent by volume) was scrutinized under boiling conditions across surfaces endowed with circular, triangular, and square fins. The study confirmed that the incorporation of $\mathrm{Al}_2 \mathrm{O}_3$ nanoparticles into the water base fluid not only enhances its thermal conductivity but, in conjunction with micro-finned surfaces, also augments the available surface area, thereby improving wettability. These modifications collectively contribute to a marked increase in the heat transfer coefficient (HTC) and a reduction in the critical heat flux (CHF). Furthermore, it was observed that at a 0.3% volume concentration of $\mathrm{Al}_2 \mathrm{O}_3$ with square fins, the temperature span extends from 373.1 to 383.1 K. Nonetheless, the long-term stability and efficacy of nanofluids are subject to potential impacts from nanoparticle aggregation and sedimentation. This study underlines the synergistic effect of nanoparticle-enhanced fluids and micro-finned surface architectures in bolstering pool boiling heat transfer, signifying a promising avenue for thermal management advancements in various industrial domains.

The advent of fifth-generation (5G) long-term evolution (LTE) technology represents a critical leap forward in telecommunications, enabling unprecedented high-speed data transfer essential for today’s digital society. Despite the advantages, the transition introduces significant challenges, including elevated bit error rate (BER), diminished signal-to-noise ratio (SNR), and the risk of jitter, undermining network reliability and efficiency. In response, a novel low-density parity check (LDPC) decoder optimized for 5G LTE applications has been developed. This decoder is tailored to significantly reduce BER and improve SNR, thereby enhancing the performance and reliability of 5G communications networks. Its design accommodates advanced switching and parallel processing capabilities, crucial for handling complex data flows inherent in contemporary telecommunications systems. A distinctive feature of this decoder is its dynamic adaptability in adjusting message sizes and code rates, coupled with the augmentation of throughput via reconfigurable switching operations. These innovations allow for a versatile approach to optimizing 5G networks. Comparative analyses demonstrate the decoder’s superior performance relative to the quasi-cyclic low-density check code (QCLDC) method, evidencing marked improvements in communication quality and system efficiency. The introduction of this LDPC decoder thus marks a significant contribution to the evolution of 5G networks, offering a robust solution to the pressing challenges faced by next-generation communication systems and establishing a new standard for high-speed wireless connectivity.

Over the past few years, the financial sector has witnessed an increase in the adoption of machine learning models within banking and insurance domains. Advanced analytic teams in the financial community are implementing these models regularly. This paper aims to explore the various machine learning approaches utilized in these sectors and offers recommendations for selecting suitable methods for financial applications. Additionally, the paper provides references to R packages that can be used to compute the machine learning methods. Our aim is to bring a valuable contribution to the field of financial research by providing a more comprehensive and advanced method of credit scoring, which in turn improves assessments of customers' debt repayment capabilities and improves risk management tactics.

In the quest for sustainable and environmentally friendly biofuels, Calophyllum inophyllum L., commonly known as Nyamplung, presents a promising feedstock due to its high oil content (75%) and a significant proportion of unsaturated fatty acids (approximately 71%). Notably, the oil extracted from this species exhibits higher viscosity and reduced capillarity compared to conventional kerosene, posing unique challenges for biodiesel conversion. This study explores the efficacy of electromagnetic induction heating as a novel transesterification method to produce biodiesel from Nyamplung oil. The process was optimized across a range of temperatures (45-65°C), reaction times (0.43-1.03 minutes), methanol to oil molar ratios (6:1), and a catalyst concentration of KOH at 2% of the total weight of oil and methanol. The conversion of Nyamplung oil into biodiesel was primarily assessed through the formation of methyl esters, with Gas Chromatography-Mass Spectrometry (GC-MS) employed for analytical verification. A comprehensive kinetic analysis revealed a transesterification reaction rate constant of rT=6.46×10¹⁴e^{(-1,068.93/RT)} [ME], indicating an activation energy requirement of 1,068 kJ/mol at the operational peak temperature of 65°C. This activation energy is notably lower than that observed with microwave heating, suggesting electromagnetic induction as a more efficient heating mechanism for this reaction. The findings underscore the potential of electromagnetic induction heating in enhancing the conversion efficiency of high-viscosity feedstocks like Nyamplung oil into biodiesel, offering a promising avenue for the production of renewable energy sources. The detailed evaluation of reaction kinetics and activation energies within this study not only contributes to the optimization of biodiesel production processes but also reinforces the viability of Calophyllum inophyllum L. as a sustainable biofuel precursor.

The diagnosis and treatment of breast cancer (BC) are significantly subject to medical imaging techniques, with segmentation being crucial in delineating pathological regions for precise diagnosis and treatment planning. This comprehensive analysis explores a variety of segmentation methodologies, encompassing classical, machine learning, deep learning (DL), and manual segmentation, as applied in the medical imaging field for BC detection. Classical segmentation techniques, which include edge-driven and threshold-driven segmentation, are highlighted for their utilization of filters and region-based methods to achieve precise delineation. Emphasis is placed on the establishment of clear guidelines for the selection and comparison of these classical approaches. Segmentation through machine learning is discussed, encompassing both unsupervised and supervised techniques that leverage annotated images and pathology reports for model training, with a focus on their efficacy in BC segmentation tasks. DL methods, especially models such as U-Net and convolutional neural networks (CNNs), are underscored for their remarkable efficiency in segmenting BC images, with U-Net models noted for their minimal requirement for annotated images and achieving accuracy levels up to 99.7%. Manual segmentation, though reliable, is identified as time-consuming and susceptible to errors. Various metrics, such as Dice, F-score, Intersection over Union (IOU), and Area Under the Curve (AUC), are used for assessing and comparing the segmentation techniques. The analysis acknowledges the challenges posed by limited dataset availability, data range inadequacy, and confidentiality concerns, which hinder the broader integration of segmentation methods into clinical practice. Solutions to overcome these challenges are proposed, including the promotion of partnerships to develop and distribute extensive datasets for BC segmentation. This approach would necessitate the pooling of resources from multiple organizations and the adoption of anonymization techniques to safeguard data privacy. Through this lens, the analysis aims to provide a thorough analysis of the practical implications of segmentation methods in BC diagnosis and management, paving the way for future advancements in the field.

This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measuresof volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's important to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme money or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.