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Background: This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC). Methods: We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy. Results: The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P

Underground space development is a crucial approach to addressing traffic congestion in China’s cities, especially through underground construction. However, the traditional pit internal support system, particularly the concrete internal support system, has significant drawbacks. These include high energy consumption and carbon emissions, which are increasingly prominent issues. In this paper, a hollow precast concrete-filled steel tube (H-CFST) internal support system is proposed and a node connection scheme is designed. Through numerical simulation, the load-bearing characteristics of H-CFST with different aspect ratios, hollow ratios, hoop thicknesses, and hoop lengths are investigated, and the optimal design parameters are obtained. Finally, following a field application, monitoring data reveal that the precast H-CFST internal support demonstrates superior load-bearing capacity compared to the concrete internal support, successfully meeting the criteria for replacement of the concrete support.

Anchor cable prestressing is one of the key factors in maintaining the stability of the supporting structure and controlling ground deformation. In order to further understand the influence of anchor cable prestress loss on the stability of the foundation pit, an underground station pit of Qingdao Metro Line 6 is taken as the engineering background, and numerical simulation research is carried out by using FLAC3D, which calculates the surface settlement, the deformation of enclosing piles, the support axial force, and the axial force of anchor cables in the process of excavation of the foundation pit and compares it with the on-site monitoring data, to get the law of the impact of prestressing force on the stability of the foundation pit in order to provide a reference for the design of the soil–rock Combined pit support system design and engineering construction to provide reference.

The prediction of post-construction settlement is an important reference for the evaluation of deformation stability evaluation and building layout planning in thick loess filled ground. To choose suitable models for predicting post-construction settlement in thick loess filled grounds, the characteristics of post-construction settlement curves are analyzed based on the measured settlement of a thick loess fill ground project. Seventeen regression parameter models are established, and some evaluation indexes and methods for models are proposed. The best prediction models for post-construction settlement prediction are optimized. The results indicate that the post-construction settlement curves of the filling area change slowly, with no steep increase in the initial stage of earthwork filling. The settlement rate gradually decreases with time, and there is no horizontal section where the settlement tends to be stable. The optimal regression parameter model can be selected by minimizing the extrapolation prediction error, the internal fitting error, and the posteriori error ratio as the comprehensive control objective. The MMF model （TypeⅡ） and hyperbolic model show high prediction accuracy, good stability, and strong adaptability, with the prediction effect being the best among the 17 models. The more stable the settlement data changes, the better the model prediction effect. Increasing the time span of modeling data would improve the prediction accuracy, but the improvement effect on prediction accuracy would no longer be significant after reaching a certain value.

The method for the determination of migration of lactide in polylactic acid (PLA) food contact material by gas chromatography-mass spectrometry (GC-MS) was established. Olive oil simulants were extracted by acetonitrile, centrifuged and filtered, and then analyzed by GC-MS. Isooctane simulants were directly filtered and analyzed by GC-MS. The method could achieve the determination of lactide migration in PLA. The limit of detection was 0.01 mg/kg and the recoveries at spiked levels were 80.0%~120.0%. The relative standard deviations were 2.6%~6.6% (n=6). Seven PLA samples were analyzed by the method. The detection rate was 85.7% and the range of concentration were 0.033~1.1 mg/kg. The method had the advantages of high sensitivity, high recovery and accuracy. The detection limit can meet the requirements of regulations. The method was suitable for migration of lactide in PLA food contact materials. The results showed that the detection rate of migration of lactide in PLA samples was high and migration of lactide should be paid attention to. Compared with isooctane, the migration of olive oil in the sample was higher than that of isooctane under the substitution condition due to the higher migration temperature of olive oil.

ObjectiveOur study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC.Materials and methodsThe database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits.ResultsMultivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range.ConclusionNLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC.

A new type of grouting material—FCM (fast cementing material)—is being used in coastal and offshore infrastructure projects, such as harbor and tunnel rehabilitation. In order to investigate how this material performs under different conditions, the compressive strength, failure mode, and surface microscopic changes of different fracture penetration degrees and different crack angles of grout-reinforced specimens formed by the FCM and sandstone were investigated in an acidic environment with pH = 1.1 and after freeze–thaw cycles of 5, 10, 20, and 30 times. In addition, through the preliminary determination of FCM grouting material fluidity and setting time, it was determined that the water-material ratio in this test is 0.3, and the sandstone used has good uniformity. The results show that the strength of the original rock can be matched or even exceeded by the solid grout of FCM and sandstone. Acidic environments and freeze–thaw cycles will erode the specimens, mainly on the surface of the specimens. The fracture penetration degree and crack angle determine the degree of strength reduction of the specimens. The compressive strength of the specimens decreases most rapidly within 10 freeze–thaw cycles. After soaking in acidic solution and a freeze–thaw cycle in an acidic environment, particle shedding occurs on the surface of the grout material and a “honeycomb” area appears on the surface of the sandstone. As the freeze–thaw cycle increases, the particle-shedding area and the honeycomb area increase continuously.

The purpose of this study is to investigate deep dynamic stall over a pitching National Advisory Committee for Aeronautics 0015 airfoil and its control with an alternating current dielectric barrier discharge (AC-DBD) plasma actuator by means of large eddy simulation. The airfoil oscillates at a reduced frequency of k = 0.08 with the angle of attack (AoA) varying between 5° and 25°. The chord-based Reynolds number is Re = 7.6 × 104. Rich fluid dynamics and physics involved in the dynamic stall are resolved by large eddy simulation. It is found that the dynamic stall under consideration initiates with the onset of a leading edge vortex (LEV). The deep dynamic stall is characterized and dominated by four successive LEVs at high angles of attack. The control of dynamic stall with AC-DBD plasma actuation is investigated in depth. At low AoAs, the plasma forcing can delay the laminar-to-turbulent transition of the attached flow over the suction surface of the airfoil. Although the formation of the first LEV is delayed by the plasma actuation, the first three LEVs cannot be effectively suppressed in high AoAs beyond 20°. The impact of the AC-DBD on the flow becomes increasingly pronounced in the downstroke phase, and at the intermediate angles of attack, the flow separation can be effectively controlled. Substantial gain from the flow control is obtained. The area of the lift hysteresis loop and drag force are appreciably reduced, and the nose-down pitch moment is alleviated notably. Finally, the influence of forcing frequency on flow control is examined.

In this research article we have demonstrated the sustainable green synthesis of a novel starch templated CuO NP following a clean and non-hazardous pathway. Ultrasonic irradiation was used to promote the reaction in alkaline medium. The numerous hydroxyl groups present in starch was exploited in the green reduction of immobilized copper ions in situ. They also helped to stabilize the as synthesized Cu NPs by encapsulation or capping. The morphology and physicochemical characteristics were ascertained over an array of analytical techniques like Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), Elemental Mapping, Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), and Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES). Biologically, the nanocomposite exhibited excellent cytotoxicity against human renal cell carcinoma (RCC-GH, CaKi-2 and HEK293) cell lines without affecting the normal (HUVEC) cell line. IC50 values of the nanocomposite were found at 139, 208and 125 against RCC-GH, CaKi-2 and HEK293 cell lines respectively and accordingly, HEK293 afforded the best adenocarcinoma activity.

Sarcomatoid renal cell carcinoma is a de-differentiated form of kidney cancer with an extremely poor prognosis. Genes associated with sarcomatoid differentiation may be closely related to the prognosis of renal cell carcinoma. The prognosis of renal cell carcinoma itself is extremely variable, and a new prognostic model is needed to stratify patients and guide treatment. Data on clear cell renal cell carcinoma with or without sarcomatoid differentiation were obtained from TCGA database, and a sarcomatoid-associated gene risk index (SAGRI) and column line graphs were constructed using sarcomatoid-associated genes. The predictive power of the SAGRI and column line graphs was validated using an internal validation set and an independent validation set (E-MTAB-1980). The SAGRI was constructed using four sarcoma-like differentiation-related genes, COL7A1, LCTL, NPR3, ZFHX4, and had a 1-year AUC value of 0.725 in the training set, 0.712 in the internal validation set, and 0.770 in the independent validation set for TCGA training cohort, with high model reliability. The molecular characteristics among the SAGRI subgroups were analyzed by multiple methods, and results suggested that the SAGRI-HIGH subgroup may benefit more from immunotherapy to improve prognosis. SAGRI satisfactorily predicted the prognosis of patients with clear cell renal cell carcinoma with or without sarcomatoid differentiation.

Fissures are common in high filled ground, it is a potential risk for the safety and stability of engineering sites. Based on the monitoring and detection data of ground fissures in a high loess filled ground in Northern Shaanxi, the development characteristics, distribution regularity and time variation of ground fissures are analyzed, and the formation mechanism of fissures is also discussed according to terrain conditions, thickness of filling and deformation characteristics. The results show that the fissures are mainly developed in the filling ground, and they are distributed in the areas with the thickness less than 15 m and 20 m away from the excavation-filling boundary, the main area is the transition zone between excavation and fill (thickness of excavation and filling is less than or equal to 5 m). The trend of fissures is approximately consistent with the excavation-filling boundary or the contour line of original foundation, the increasing rate of the fissure width decreases gradually, and it takes about 3 months from its appearance to stabilization. The fissures often develop with a sinkhole and extends along the joint surface between the filling and valley slope due to the effect of precipitation erosion and subsurface erosion, with a vertical depth of 7.5 m. The differential settlement and horizontal displacement caused by the difference of gully topography and thickness of filling are the main causes of fissures in high loess filled ground.

With the striking development of big data, individual privacy and data security obtain unprecedented importance. Database anonymization mechanism is created for protecting individual privacy by adding noise to the result of a query, which finds a tradeoff between the privacy and utility of personal data. However, corresponding attacks are emerging continuously resulting in a high risk of individual identification. In this paper, we learn patterns of malicious SQL queries and propose a novel detection method. Association rules are used to mine patterns and features of noise-exploitation attacks, and parse trees are applied to the feature extraction of SQL, thereby we construct feature vectors and input them into the classifiers. At the same time, we also propose a SQL generation method to generate query samples based on a real database for model training and testing. Experiments show that our detection method can significantly prevent noise-exploitation attacks including almost all differential attacks and 91% cloning attacks based on the synthetic dataset, which ensures a strong degree of data utility.

This study aims to explore the influence of organizational learning and dynamic capability on organizational performance of human resource service enterprises with the moderating role of technology environment and market environment. Data were gathered from 360 human resource service enterprises, and applied the hierarchical linear regression method and structural equation model to test the hypotheses. We found that organizational learning has a significantly positive impact on resource integration capability, as well as has a significantly positive impact on resource reconfiguration capability of human resource service enterprises. Resource integration capability and resource reconfiguration capability have a significantly positive impact on organizational performance. Moreover, results indicated that the resource integration capability and resource reconfiguration capability partially mediate in the relationship between organizational learning and organizational performance. Furthermore, technology environment and market environment have positive moderation effect between resource integration capability and organizational performance of human resource service enterprises, as well as have positive moderation effect between resource reconfiguration capability and organizational performance of human resource service enterprises. The current study contributes to a better understand the impact mechanism of organizational learning on organizational performance from the perspective of organizational learning theory and dynamic capability theory. In addition, this study provides implications for human resource service enterprises and managers to improve organizational performance.

Objective To evaluate the effect of friction behavior on migration levels of metal elements from stainless-steel articles for food contact. Methods The migration levels of metal elements from used and new stainless-steel articles were compared according to GB 4806.9-2016 and Council of Europe Resolution CM/Res (2013) 9. The migration conditions were boiling in 4% acetic acid for 30 min followed by at room temperature for 24 h and soaking in 5 g/L citric acid at 100 ℃ for 2 h, respectively. Meanwhile, the impact of dynamic friction on migration were investigated on new stainless-steel articles, where the friction method were referred to the GB/T 32095.2-2015 standard. Migration of metal elements complied with the corresponding standard. Results Based on the European standard, migration of lead in the first migration test was above limit of quantitation (LOQ) in all five used stainless-steel articles after friction, but none in new ones. The quantification rate of chromium in the first migration test in 80% (4/5) of used stainless-steel articles were above LOQ after friction by GB 4806.9-2016 standard while were 20% (1/5) in new ones. The migration level of Cr was higher by GB 4806.9-2016 standard than by European resolution in single dynamic friction study. Results from the dynamic friction experiments indicated that the migration of Cr and Ni reached equilibrium after 3 times of friction. Conclusion The migration condition in GB 4806.9-2016 standard had stronger leaching ability for Cr from stainless-steel articles than that in European resolution. Friction behavior promoted corrosion and metal element migration of stainless steel. Long-term used stainless-steel products had higher lead and chromium migration than new products, but without safety risks.

The design of a thermal cavity receiver and the arrangement of the fluid flow layout within it are critical in the construction of solar parabolic dish collectors, involving the prediction of the thermal–fluid physical field of the receiver and optimization design. However, the thermal–fluid analysis coupled with a heat loss model of the receiver is a non-linear and computationally intensive solving process that incurs high computational costs in the optimization procedure. To address this, we implement a net-based thermal–fluid model that incorporates heat loss analysis to describe the receiver’s flow and heat transfer processes, reducing computational costs. The physical field results of the net-based thermal–fluid model are compared with those of the numerical simulation, enabling us to verify the accuracy of the established thermal–fluid model. Additionally, based on the developed thermal–fluid model, a topology optimization method that employs a genetic algorithm (GA) is developed to design the cavity receiver and its built-in net-based flow channels. Using the established optimization method, single-objective and multi-objective optimization experiments are conducted under inhomogeneous heat flux conditions, with objectives including maximizing temperature uniformity and thermal efficiency, as well as minimizing the pressure drop. The results reveal varying topological characteristics for different optimization objectives. In comparison with the reference design (spiral channel) under the same conditions, the multi-objective optimization results exhibit superior comprehensive performance.

To solve the current privacy leakage problems of cloud storage services, research on users’ payment intention for cloud storage services with privacy protection is extremely important for improving the sustainable development of cloud storage services. An evolutionary game model between cloud storage users and providers that considers privacy is constructed. Then, the model’s evolutionary stability strategies via solving the replication dynamic equations are analyzed. Finally, simulation experiments are carried out for verifying and demonstrating the influence of model parameters. The results show that the evolutionary stable strategies are mainly affected by the privacy protection profit growth coefficient of both parties, input costs, free-riding gains, and other factors. If the profit growth coefficient is very small, users will not choose to pay and providers will not choose to actively protect user information. As the profit growth coefficient increases, both parties will promote the development of privacy protection with a higher probability. The results are beneficial for cloud storage providers to increase the number of paid users and thus to achieve the sustainable development of cloud storage service.

The waste mud from slurry shield tunnel construction can easily cause environmental pollution. Thus, the RCSLP curing agent is developed to reutilize the waste mud produced in the Jinan Yellow River tunnel. The strength properties of the soils solidified with cement or RCSLP were tested, and their microstructures were studied by scanning electron microscopy. The results show that for the RCSLP curing agent, an optimum cement content of 15% and an optimum additive content of 12% improved the early compressive strength of the solidified soil. The mechanical properties of the RCSLP solidified soil were improved due to the significantly increased hydration products. The findings of this research could help achieve the utilization of waste mud from slurry shield tunnel construction.

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions.

In this study, a target analytical approach using high-performance liquid chromatography−tandem mass spectrometry (HPLC−MS/MS) was developed to simultaneously determine six isothiazolinones containing 2-Methylisothiazol-3(2H)-one (MI), 5-Chloro-2-methyl-4-isothiazolin-3-one (CMI), 1,2-benzisothiazolin-3-one (BIT), 2-Octyl-3(2H)-isothiazolinone (OIT), Dichlorooctylisothiazolinone (DCOIT), and 2-methyl-1,2-benzisothiazolin-3-one (MBIT) in water-based adhesive used for food contact materials. The main factors affecting extraction efficiency such as extraction method, extraction time, extraction solvent, and solid−liquid ratio have been evaluated by using real adhesive samples. Multiple-reaction monitoring (MRM) was used for the qualitative and quantitative analyses of targeted isothiazolinones. This method was demonstrated as an effective and reliable technique for detecting multiple isothiazolinones with satisfactory recoveries (81.5~107.3%), and the limits of detection (LOD) and quantification (LOQ) were obtained at a low level. This method was validated and applied to the determination of six isothiazolinones in commercial water-based adhesives. The present results revealed that these adhesives contained a combination of isothiazolinones (BIT, MI, CMI, and MBIT) with the concentration ranging from 2.27 to 123.5 mg/kg. To our knowledge, it is the first time it has been reported that MBIT was detected in water-based adhesives used for food contact materials, which requires a further investigation for its migration to food and the risk to human health.

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

The investigation of flow separation control over a NACA 0015 airfoil model using a fluidic oscillator (FO) is conducted through delayed detached eddy simulation. First, the flowfield within and outside an FO operating in quiescent air is resolved simultaneously. The oscillation of the jet flow induced by the FO is attributed to the cyclic expansion and contraction of the recirculation bubbles located near the two Coanda surfaces in the mixing chamber. Significantly, the predicted jet oscillation frequency closely matches the experimental data, validating the accuracy of our findings. Next, the FO is integrated into an airfoil model to suppress the flow separation. The airfoil is under deep stall conditions, with angles of attack of 20 and 17° and a Reynolds number of Re=4.8×105. The key driving force for flow reattachment is the spanwise vortices induced by the oscillating jet, which substantially enhance the mixing between the separated flow and the external high-momentum flow. Consequently, the aerodynamic performance of the airfoil is notably improved. Additionally, both spectral and dynamic mode decomposition analyses indicate that the flow, under the influence of the FO forcing, becomes more orderly and well-organized and is effectively locked into the forcing frequency. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of an anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform. [ABSTRACT FROM AUTHOR]

Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations -low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance. [ABSTRACT FROM AUTHOR]

Objective: Our study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC. Materials and methods: The database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort (n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits. Results: Multivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range. Conclusion: NLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC. [ABSTRACT FROM AUTHOR]

The changes in pore structure within soil-rock mixtures under freeze-thaw cycles in cold regions result in strength deterioration, leading to instability and slope failure. However, the existing studies mainly provided qualitative analysis of the changes in pore or strength of soil-rock mixture under freeze-thaw cycles. In contrast, few studies focused on the quantitative evaluation of pore change and the relationship between the freeze-thaw strength deterioration and pore change of soil-rock mixture. This study aims to explore the correlation between the micropore evolution characteristics and macro-mechanics of a soil-rock mixture after frequent freeze-thaw cycles during the construction and subsequent operation in a permafrost region. The pore characteristics of remolded soil samples with different rock contents (i.e., 25%, 35%, 45%, and 55%) subjected to various freeze-thaw cycles (i.e., 0, 1, 3, 6, and 10) were quantitatively analyzed using nuclear magnetic resonance (NMR). Shear tests of soilrock samples under different normal pressures were carried out simultaneously to explore the correlation between the soil strength changes and pore characteristics. The results indicate that with an increase in the number of freeze-thaw cycles, the cohesion of the soil-rock mixture generally decreases first, then increases, and finally decreases; however, the internal friction angle shows no apparent change. With the increase in rock content, the peak shear strength of the soil-rock mixture rises first and then decreases and peaks when the rock content is at 45%. When the rock content remains constant, as the number of freeze-thaw cycles rises, the shear strength of the sample reaches its peak after three freeze-thaw cycles. Studies have shown that with an increase in freeze-thaw cycles, the medium and large pores develop rapidly, especially for pores with a size of 0.2-20 µm. Freeze-thaw cycling affects the internal pores of the soil-rock mixture by altering its skeleton and, therefore, impacts its macro-mechanical characteristics. [ABSTRACT FROM AUTHOR]