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Objective The advancing speed of shield machine is crucial not only for the tunnel excavation pace but also for the predicting construction schedule and cost. Therefore, it is necessary to predict and analyze the advancing speed of the shield machine. Method Taking the full-section limestone stratum of Longao Station-Olympic Sports Center West Station interval on Jinan Metro Line 3 as research object, data from shield machine excavation are collected and preprocessed using methods such as zero-push value, outlier removal, and data denoising. To improve prediction accuracy, the dataset is divided into training and validation sets. A model for predicting thrust speed is established from linear regression based on cutterhead torque and total thrust, and the influence law of excavation load on advancing speed is analyzed. Result & Conclusion In limestone stratum, the linear regression prediction accuracy of three advancing speed prediction models all exceeds 0.70, indicating that advancing speed can be accurately predicted based on cutterhead torque and total thrust. During excavation with the earth pressure balance shield machine, fluctuations in cutterhead rotation speed and advancing speed are minimal. As the cutterhead rotation speed increases, the cutterhead torque gradually decreases. Cutterhead torque positively influences the advancing speed; the greater the cutterhead torque, the higher the advancing speed. Under the same stratum and excavation conditions, due to the soil extrusion action of cutterhead, merely increasing the total thrust is not conducive to improving advancing speed, calling for the necessity of simultaneously increasing cutterhead rotation speed.

Breast cancer ranks as one of the most common malignancies among women, with its prognosis and therapeutic efficacy heavily influenced by factors associated with the tumor cell biology, particularly the tumor microenvironment (TME). The diverse elements of the TME are engaged in dynamic bidirectional signaling interactions with various pathways, which together dictate the growth, invasiveness, and metastatic potential of breast cancer. The Hedgehog (Hh) signaling pathway, first identified in Drosophila, has been established as playing a critical role in human development and disease. Notably, the dysregulation of the Hh pathway is recognized as a major driver in the initiation, progression, and metastasis of breast cancer. Consequently, elucidating the mechanisms by which the Hh pathway interacts with the distinct components of the breast cancer TME is essential for comprehensively evaluating the link between Hh pathway activation and breast cancer risk. This understanding is also imperative for devising novel targeted therapeutic strategies and preventive measures against breast cancer. In this review, we delineate the current understanding of the impact of Hh pathway perturbations on the breast cancer TME, including the intricate and complex network of intersecting signaling cascades. Additionally, we focus on the therapeutic promise and clinical challenges of Hh pathway inhibitors that target the TME, providing insights into their potential clinical utility and the obstacles that must be overcome to harness their full therapeutic potential. [ABSTRACT FROM AUTHOR]

Steel slabs are key in-process products in the steel production process. The slab allocation problem is to allocate slabs to suitable orders by considering complicated technical restrictions and multiple management requirements. In practice, thousands of slabs in multiple production lines should be allocated to orders. Due to the complexity and large scale of the problem, it takes planners a long time to make decisions, and the obtained allocation schemes are usually ineffective because of the myopic nature of the rule-based methods. In this article, we present a learning-based solution method for solving a practical slab allocation problem in multiple hot rolling lines. We formulate the problem as an integer programming model, then a data-based method is adopted to evaluate the mismatching cost between slabs and orders. To effectively solve the large-sized problem, a learning-based decomposition strategy is proposed to decompose the original problem into several small-sized subproblems. Then, a branch-and-price algorithm is proposed to optimally solve the subproblems. To further speed up the solution process, a primal heuristics based on column generation is designed to solve the large-sized subproblems. The solution methods have been implemented in a steel company and effectively increased slab utilization and reduced production cost. [ABSTRACT FROM AUTHOR]

Heavy metal contamination has long been a tough challenge. Recently, enzyme-induced carbonate precipitation (EICP) has been proposed to handle this problem. This paper aims to explore the efficacy, process, and mechanisms of EICP using crude sword bean urease extracts to remediate Zn2+, Ni2+, and Cr(VI) contamination. A series of liquid batch tests and geochemical simulations, as well as microscopic analyses, were conducted. The liquid batch test results show that Zn2+, Ni2+, and Cr(VI) can be effectively immobilized by the EICP method, and the highest immobilization percentage was observed for Zn2+, reaching up to 99%. Ni2+ and Cr(VI) were immobilized at 62.4% and 24.4%, respectively. Additionally, the immobilization percentage of heavy metals increased with the concentration of added Ca2+. The simulation results and XRD results reveal that the organic molecules in crude sword bean urease can promote ZnCO3, Zn(OH)2, Zn5(CO3)2(OH)6, and NiCO3 precipitation. The FTIR and SEM-EDS results provide evidence for heavy metal adsorption by the functional groups in crude urease and calcium carbonate. The liquid batch test results, as well as the simulation results and the microscopic analysis results, indicate that the mechanism of EICP in heavy metal remediation can be summarized as biomineralization to form heavy metal carbonate precipitates and metal hydroxide precipitates, adsorption by calcium carbonate, and adsorption or complexation or promoting nucleation by organic molecules. [ABSTRACT FROM AUTHOR]

Throughout the COVID-19 pandemic, individuals potentially infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were forcibly recalled to local or central hospitals, where the diagnostic results were obtained a couple of days after the liquid biopsies were subjected to conventional polymerase chain reaction (PCR). This slow output of such a complex and time-consuming laboratory procedure hindered its widespread application. To overcome the limitations associated with such a centralized diagnostic system, we developed a hand-held and all-in-one type test kit in which the analytical results can be obtained in only 30 min. The test kit consists of three major steps for on-site SARS-CoV-2 RNA detection: 1) virus lysis by heat, 2) RNA enrichment by membrane, and 3) real-time detection by colorimetric loop-mediated isothermal amplification (c-LAMP). The proposed device operates in a sample-to-answer format, is fully automated, and reduces dependence on traditional laboratory settings, facilitating large-scale population screening. [ABSTRACT FROM AUTHOR]

The rapid fouling and bacterial contamination of equipment, heat exchangers, and pipelines are major concerns in food manufacturing plants. The process of cleaning‐in‐place (CIP) in the food manufacturing industry involves hazardous chemicals such as sulfuric acid, chlorine, sodium hydroxide, and potassium hydroxide. This study aims to investigate the cleaning efficiency of a novel environment‐friendly solution, plasma‐activated water (PAW), for removing dairy and plant‐based fouling and for biofilm reduction. PAW was produced by exposing water to plasma, which is a partially ionized gas generated by applying electricity to air. PAW prepared in this study had a pH, electrical conductivity (EC), and oxidation–reduction potential (ORP) of 2.5 ± 0.1, 1170. 1 ± 202.2 μS/cm, and 589.0 ± 2.4 mV, respectively. Holding PAW at different temperatures (20–75°C) did not change pH, ORP, and EC significantly, while nitrite and nitrate concentrations in PAW did not show a consistent trend with temperature. The treatment time and temperature of PAW were optimized for cleaning fouled coupons (stainless‐steel type 304 and 316) using model fouling fluids (MFF) with dairy (whey) and plant‐based (oat) proteins using full‐factorial design. The optimized PAW combinations (15 min/75°C and 5 min/75°C) were found to be as effective for fouling removal as compared to CIP controls (conventional caustic and acid solutions). Optimized PAW also showed significant biofilm reduction of Listeria innocua on stainless‐steel coupons with/without fouling, with at least 4.4 log and 4.0 log reductions in L. innocua biofilms when attached to MFF‐whey and MFF‐oat, respectively. Practical applications: Plasma‐activated water (PAW) can inactivate a wide spectrum of microorganisms on various food and food contact surfaces. We propose the use of environment‐friendly plasma‐activated water (PAW), which can be prepared on‐site and on‐demand for cleaning‐in‐place (CIP) operations in the food industry. The results of this study suggest the potential of PAW as a promising CIP alternative for cleaning and sanitizing surfaces soiled by fouling deposits in dairy and plant‐based industries. [ABSTRACT FROM AUTHOR]

Supercritical CO2 heat transfer in vertical tubes has been widely studied, but limited work exists for that in horizontal tubes. In this work, experiments were conducted for CO2 in a horizontal tube with inner diameter of 8 mm under wide range of pressures (5.51–20.41 MPa), covering both subcritical and supercritical conditions. The ranges of heat flux and mass flux were 97.5–404.0 kW/m2 and 510.2–1314.2 kg/m2s, respectively. The heat transfer for subcritical pressure far below critical pressure showed typical flow boiling characteristics, with a wall temperature peak along the tube corresponding to departure from nucleate boiling. For near-critical pressure, it showed characteristics similar to supercritical heat transfer, with continuous increase of wall temperature along the tube. The effects of different parameters including pressure, mass flux, and heat flux on the heat transfer behavior were analyzed and discussed. Moreover, using the pseudo-vapor quality concept of the supercritical three-regime-model introduced in a previous work, the heat transfer coefficient was found to behave very similarly between subcritical and supercritical conditions. The experimental results showed significant deviation from single-phase convection within the two-phase/two-phase-like region compared with the single-phase regions, and which validates the similarity between subcritical boiling and supercritical pseudo-boiling. [ABSTRACT FROM AUTHOR]

Seismic velocity plays an important role in imaging and identifying underground geology. Conventional seismic velocity inversion methods, like full waveform inversion, directly update the velocity model based on the misfit between the observed and synthetic data. However, seismic velocity inversion is a highly nonlinear process, and the inversion effect greatly relies on the initial inversion model. In this paper, we propose a novel network‐domain full waveform inversion method. Different from the existing network‐domain full waveform inversion methods, which use random or fixed numbers as network input, we reparameterize the low‐dimensional acoustic velocity model in a high‐dimensional inversion network parameter domain with seismic observed data as the network input. In this way, the physical information within the observed data can be directly encoded into the inversion parameters, leading to a better inversion effect than the current network‐domain full waveform inversion method. Moreover, comparison experiments on the Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers Overthrust model and the Marmousi model show the advantages of the proposed method over conventional full waveform inversion from the aspects of inversion accuracy, robustness to noisy data, and more complex geological structures. These advantages may benefit from the fact that reparameterization within the inversion network domain can empower the inversion process with the regularization ability of denoising and mitigating the cycle‐skipping issue. In the end, the potential of the proposed method in terms of network initialization is further discussed. [ABSTRACT FROM AUTHOR]

AbstractIn the digital age, human-algorithm interaction has become ubiquitous. The popularity of short-form video apps has led to greater access to and experience with algorithmic recommender systems, but it has also exposed their potential problems and dark sides. However, there is still limited knowledge about the dark side of over-recommendation from the perspective of user perception, especially regarding the negative impact on users’ psychology and behavior. Drawing on social cognitive theory, this study constructed a theoretical model to investigate how information environment factors influence users’ cognitive dissonance and discontinuance intention under the antecedent dimension of perceived over-recommendation. The model was tested using partial least squares structural equation modeling on 322 valid questionnaires from users of Chinse mass short video apps (eg, Douyin). The results showed that when users perceived over-recommendation, information narrowing, information redundancy, perceived overload, and privacy invasion significantly increased their cognitive dissonance, ultimately leading to discontinuance intention. Notably, cognitive dissonance fully mediated the relationship between the information environment and discontinuance intention, and self-efficacy did not play a significant moderating role. Additionally, the local path effects varied significantly across groups with different characteristics. To maintain the sustainability of short video platforms, it is crucial to explore moderate recommendation mechanisms. User-centered functionality improvements and differentiated behavioral interventions can help mitigate negative psychology and discontinuance intention among users. [ABSTRACT FROM AUTHOR]

Detection of printed circuit board assembly (PCBA) defects is crucial for improving the efficiency of PCBA manufacturing. This paper proposes PCBA-YOLO, a YOLOv5-based method that can effectively increase the accuracy of PCBA defect detection. First, the spatial pyramid pooling module with cross-stage partial structure is replaced in the neck network of YOLOv5 to capture the resolution features at multiple scales. Second, large kernel convolution is introduced in the backbone network to obtain larger effective receptive fields while reducing computational overhead. Finally, an SIoU loss function that considers the angular cost is utilized to enhance the convergence speed of the model. In addition, an assembled PCBA defect detection dataset named PCBA-DET is created in this paper, containing the corresponding defect categories and annotations of defect locations. The experimental results on the PCB defect dataset demonstrate that the improved method has lower loss values and higher accuracy. Evaluated on the PCBA-DET dataset, the mean average precision reaches 97.3 % , achieving a real-time detection performance of 322.6 frames per second, which considers both the detection accuracy and the model size compared to the YOLO series of detection networks. The source code and PCBA-DET dataset can be accessed at https://github.com/ismh16/PCBA-Dataset ., (© 2024. The Author(s).)