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Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R 2 of the final model are 0.193, 29.197 × 10 4 m 3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.

Background Microsatellite instability (MSI) status guides immunotherapy and Lynch syndrome (LS) screening. Given the 5–10% discordance rate between mismatch repair immunohistochemistry (MMR-IHC) and Microsatellite instability polymerase chain reaction (MSI-PCR), true high microsatellite instability (MSI-H) cases in proficient mismatch repair (pMMR) colorectal cancer (CRC) may miss critical interventions. This study investigates molecular pathological characteristics between concordant and discordant groups assessed by these two methods, aiming to reduce the risk of misdiagnosing MSI-H and LS. Methods The MMR-IHC and MSI-PCR were conducted respectively in 2910 CRC patients. Sanger sequencing identified the mutation status of KRAS, BRAF, and PIK3CA genes, and next-generation sequencing (NGS) detected LS-related genes. We compared the molecular pathological features between the pMMR&MSS and pMMR&MSI-H groups, as well as between the dMMR&MSI-H and dMMR&MSS groups. Eleven screening strategies for pMMR&MSI-H were formulated for pMMR patients. Results The consistency rate between the two methods was 96.8% (2816/2910), with a discordance rate of 3.2% (94/2910), comprising 43 cases in the pMMR&MSI-H group and 51 cases in the dMMR&MSS group. Germline mutations in LS-associated genes were detected in 36.4% (4/11) of the pMMR&MSI-H group but were absent in all 9 dMMR&MSS cases. Compared with the pMMR&MSS consistent group, the pMMR&MSI-H inconsistent group showed higher prevalence in patients with right colon (55.8% vs. 20.3%), well-differentiated (18.8% vs. 8.4%), and PIK3CA exon 20 (E20) mutations (30.0% vs. 8.7%). Compared with the dMMR&MSI-H consistent group, the dMMR&MSS inconsistent group was more common in patients with rectum (49.0% vs. 15.2%), stage IV patients (23.5% vs. 8.3%), and PIK3CA E20 wild-type (97.8% vs. 82.4%). The strategy of supplemental MSI-PCR detection for the right colon pMMR patients could identify 55.8% (24/43) of pMMR&MSI-H patients. Adding the detection of patients with PIK3CA E20 mutation based on the right colon can increase the PMMR&MSI-H detection rate to 65.1% (28/43). Conclusion We recommend supplemental MSI-PCR testing for pMMR CRC patients with right colon OR PIK3CA E20 mutation. For those with MSI-H results, subsequent NGS should be performed to identify germline mutations in LS-associated genes.

Accumulation landslides are prone to occur during the continuous infiltration of heavy rainfall, which seriously threatens the lives and property safety of local residents. In this paper, based on the Green-Ampt (GA) infiltration model, a new slope rainfall infiltration function is derived by combining the effect of air resistance and lateral seepage of saturated zone. Considering that when the soil layer continues to infiltrate after the saturation zone is formed, the air involvement cannot be discharged in time, which delays the infiltration process. Therefore, the influence of air resistance factor in soil pores is added. According to the infiltration characteristics of finite long slope, the lateral seepage of saturated zone is introduced, which makes up for the deficiency that GA model is only applicable to infinite long slope. Finally, based on the seepage characteristics of the previous analysis, the overall shear strength criterion is used to evaluate the stability of the slope. The results show that the safety factor decreases slowly with the increase of size and is inversely correlated with the slope angle and initial moisture content. The time of infiltration at the same depth increases with the increase of size and slope angle, and is inversely correlated with the initial moisture content, but is less affected by rainfall intensity. By comparing with the results of experimental data and other methods, the results of the proposed method are more consistent with the experimental results than other methods.

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper’s method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment.

Chikungunya fever has emerged as a significant public health concern in tropical and subtropical regions worldwide. Understanding the epidemiological patterns and clinical features of chikungunya virus infection is crucial for developing effective disease surveillance and management strategies. We conducted a retrospective analysis of 311 laboratory-confirmed chikungunya fever cases presenting to a tertiary hospital in Foshan, Guangdong Province, China, from June to September 2025. The study population had a median age of 32.35 years (interquartile range: 10-53 years), with females comprising 56.3%. Fever occurred in 85.9% of patients, with 49.2% experiencing moderate to high-grade fever. Joint symptoms were present in 76.5% of patients, with 48.6% developing moderate to severe arthralgia. Rash was a significant clinical manifestation with an incidence of 73%, of which 44.1% of patients presented with severe rash. Laboratory abnormalities included leukopenia (6.4%), lymphopenia (64.6%), and elevated C-reactive protein (66.2%). Multivariate analysis identified rash presence, joint symptoms, and younger age as factors significantly associated with moderate-severe disease presentations. However, as rash and joint symptom severity were incorporated into the severity scoring system, these associations should be interpreted as descriptive characterization rather than independent prediction. This study characterizes the clinical features of acute chikungunya fever, with age-related severity differences and specific clinical and laboratory markers associated with disease severity. Rash emerges as a significant clinical manifestation associated with disease severity, highlighting its value in clinical assessment of chikungunya fever. These findings support the development of risk stratification tools and evidence-based management protocols for chikungunya fever patients.

AimsDue to the lack of large clinical cohorts in the Chinese populations with colorectal cancer (CRC) and gastric cancer (GC), there is no consensus among the preferred panel for microsatellite instability (MSI)-PCR testing. This study aims to evaluate a more appropriate panel.MethodsWe tested the MSI status of 2572 patients with CRC and GC using the NCI panel and 2 mononucleotide panels (5 and 6 mononucleotide panels). Immunohistochemistry (IHC) was employed to perform mismatch repair protein testing in 1976 samples.ResultsWe collected 2572 patients with CRC and GC. The National Cancer Institute (NCI) panel failed to detect 13 cases. Of the 2559 cases that received results from all three panels, 2544 showed consistent results. In the remaining 15 cases, 9 showed discrepancies between MSI-H and MSI-L, and 6 showed discrepancies between MSI-L and microsatellite stability (MSS). The misdiagnosis rate of MSI-L was significantly lower in two mononucleotide panels than in the NCI panel (12.5% vs 87.5%, p=0.010) in CRC. In patients with GC, only the NCI panel detected three MSI-L cases, while the results of the two mononucleotide panels were one MSI-H and two MSS. Based on their IHC results, the MSI-L misdiagnosis rate of the NCI panel was 33.3%. Furthermore, compared with two mononucleotide panels, the NCI panel had a much lower rate of all loci instability in CRC (90.8% and 90.3% vs 25.2%) and GC (89.5% and 89.5% vs 12.0%).ConclusionIn Chinese patients with CRC and GC, the five and six mononucleotide panels have advantages for detecting MSI over the NCI panel.

The occurrence of debris flows are a significant threat to human lives and property. Estimating the debris flow scale is a crucial parameter for assessing disaster losses in such events. Currently, the commonly used method for estimating debris flow runoff relies on fitting techniques, which often yield low prediction accuracy and limited data representation capabilities. Addressing these challenges, this study proposes an improved grey wolf algorithm optimized support vector machine prediction model. The model’s effectiveness is validated using data from 72 debris flow events in Beichuan County. The results demonstrate a prediction accuracy of 95.9% using this approach, indicating its strong predictive capabilities for debris flow scale. Additionally, it is observed that the basin area, the basin relative, and the main channel length are the key factors influencing debris flow scale in Beichuan County.

All-inorganic lead halide perovskite nanocrystals (NCs) have great optoelectronic properties with promising applications in light-emitting diodes (LEDs), lasers, photodetectors, solar cells, and photocatalysis. However, the applications of perovskite NCs face many challenges, including environmental issues and poor endurability of the perovskite-based devices caused by the intrinsic toxicity of Pb and the poor stabilities of perovskites against moisture, heat and light. To address those issues of the lead halide perovskite NCs, we utilized stable lead-free perovskite as a shell to protect the lead halide perovskite NCs, as well as developed lanthanide ions (Ln3+) doped/based-lead-free perovskite NCs to enhance their optical properties, making them compatible substitutions of the lead halide perovskite.In the first work of this dissertation, lead-free vacancy-ordered double perovskite Cs2SnX6 (X = Cl, Br, I) was grown epitaxially on the surface of CsPbX3 NCs by a hot-injection method. The effectiveness of the non-toxic shell protection is demonstrated by the enhanced environmental and phase stability against UV illumination and polar solvents such as water and ethanol. The photoluminescence quantum yields (PL QYs) increase for the CsPbCl3 and CsPbBr3 NCs after shelling because of the type I band alignment of the core/shell materials, while enhanced charge transport properties obtained from CsPbI3/Cs2SnI6 core/shell NCs due to the efficient charge separation in the type II core/shell band alignment.To understand the interfacial properties of core/shell NCs, we developed a facile method to grow a lead-free CsMnCl3 shell on the surface of CsPbCl3 NCs to form CsPbCl3/CsMnCl3 core/shell NCs with enhanced environmental stability and improved PLQYs. Interestingly, the Mn2+ from CsMnCl3 shell can diffuse into the lattice of CsPbCl3 core by thermal annealing of the resulting core/shell NCs, activating the orange PL from Mn2+ dopants. The Mn PL to host CsPbCl3 PL ratio can be precisely tuned by adjusting the thermal annealing time of the core/shell NCs.Incorporation of rare earth lanthanide ions into lead-free halide perovskite nanocrystals (NCs) is an effective and promising way to expand their optical, magnetic, and electrochemical properties. We developed various Ln3+ (including Yb3+, Er3+, and Nd3+), doped Sb3+- or Bi3+-based and Sb3+/Bi3+ alloyed lead-free perovskite NCs, including vacancy-induced perovskite (A3B(III)2X9), double perovskite (A2B(I)B(III)X6), and layered-double perovskite (A4B(II)B(III)2X12) NCs in this work. Interestingly, it is found that the lanthanide ions, even Ln3+ ions with large ionic radii, such as Nd3+ ions with their 112 pm ionic radius, in an octahedral (Oh) coordination environment, prefer smaller isovalent Sb(III) Oh cation sites (90 pm) instead of Bi(III) Oh sites (117 pm) in the lead-free perovskite NCs due to the relatively high polarizabilities of lanthanide ions and the more intense second-order Jahn-Teller in [SbCl6] 3- compared to that in [BiCl6] 3-. The efficient Ln3+ doping in Sb3+-rich alloyed perovskite NCs was confirmed by elemental analysis and leads to enhanced Ln3+ ion near-infrared (NIR) photoluminescence (PL) of the doped NCs. Furthermore, we first synthesized Cs2KLnCl6 NCs with strong blue emissions through bandgap radiative excitonic recombination, and the characteristic NIR emissions from Ln3+ can be turned on by doping Sb3+. This study provides a fundamental understanding of Ln3+ doping behavior in lead-free perovskite NCs and new opportunities for designing efficient Ln3+-based functional materials.

Traditional method of determining closure and initiation stress of fractured rocks by analyzing the stress-strain curve has problems such as strong subjectivity and large errors. This study utilized the rock closure stress values and onset stress values determined by three traditional methods, namely, axial strain method, fracture volume method and empirical value taking method, as the base database. The Student t distribution theory was used to obtain a confidence interval based on its overall distribution of values and to achieve a combination of the advantages of multiple methods. Within confidence interval, the Monte Carlo stochastic simulation was used to determine the convergence interval of the second stage to further improve the accuracy. Finally, mean value of the randomly sampled values after reaching the convergence stage was taken as the probability value of rock closure and crack initiation stress. The results showed that the 3 traditional methods for calculating rock closure and initiation stresses are significantly different. In contrast, the proposed method biases more towards multi-numerical distribution intervals and also considers the preference effects of different calculation methods. In addition, this method does not show any extreme values that deviate from the confidence intervals, and it has strong accuracy and stability compared to other methods.

Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection.