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Deep close distance coal seam mining is affected by the goaf of upper coal and remaining coal pillar, and the roadway of lower coal is difficult to maintain stability. By means of theoretical analysis, physical simulation test and numerical simulation, this paper analyzes the failure characteristics of roadway in deep close distance coal seam and the evolution law of stress field, and optimizes the reasonable layout of lower coal roadway. The results show that: (1) The analytical formula of the evolution of the floor principal stress difference with depth is derived theoretically, and the distribution law of the shear stress of the rock mass in the floor of the coal seam is obtained. (2) The airway below the goaf can maintain good stability. High stress accumulation phenomenon occurs in the transport roadway below the coal pillar, which can not keep stable. (3) Due to the stress difference under the goaf and the coal pillar, the shear stress increases and the rock mass is damaged, and the left gang of the transport roadway is prone to instability destruction. (4) By comparing the distribution characteristics of plastic zones of transport roadway at different locations, it is determined that the transport roadway is more appropriate to be arranged 45 m below the coal pillar from the goaf.

In view of the difficulty of rock mass crushing and support on the surface of deep soft rock roadway with high stress, RMT-150 C rock mechanics experiment system was used to carry out the orthogonal test for sensitivity analysis of rock bolt support parameters of cemented broken rock mass. Through numerical simulation, the relationship between bolt parameters and the form and range of the formed compressive stress field was studied. The evolution law of interfacial shear stress in the anchorage section at the stage of elastic deformation is obtained. The results show that the main factors affecting the compressive strength of anchor solid are the number of anchor rods per unit area, followed by the presence or absence of anchor net, the anchor depth ratio, and the diameter of anchor rods. Increasing the anchorage length and diameter of the bolt is beneficial to improving the shear strength reserve of the bolt; The length of bolt affects the superposition range of axial compressive stress zone, the spacing of bolt affects the superposition range of radial compressive stress zone, and the diameter of bolt has little effect on the superposition range of compressive stress zone. The longer the length of bolt and the larger the spacing, the smaller the superposition range of compressive stress zone, or even disappear. The prestressing of bolt can make up for the supporting defects caused by long length and large spacing. The larger the prestressing is, the shape of compressive stress zone changes from “peanut shape” to “pomegranate shape”, and the active supporting effect becomes better gradually. The research results have reference significance for the selection of engineering bolt supporting parameters.

In response to the insufficient research on the damage weakening evolution law of surrounding rock during roadway (tunnel) excavation and retreat, as well as the inadequate understanding of the anchoring mechanism of rock bolts in such rock masses, a series of triaxial loading-unloading and uniaxial reloading experiments were conducted on sandstone specimens using the RMT-150 C rock mechanics testing system. Microstructural characteristics of the damaged rock mass were investigated through scanning electron microscope (SEM) analysis, revealing the damage weakening mechanism of the rock. Through laboratory experiments and theoretical analysis, in combination with the failure characteristics of rock-anchor composite bodies, the underlying causes of changes in failure modes were analyzed to explore the anchoring mechanism of bolts in damaged rock. The experimental results indicate that during the triaxial loading-unloading tests, the degree of rock damage varied depending on the axial unloading point, exhibiting a trend of first decreasing and then increasing. For damaged rock masses, the presence of bolts did not alter the failure modes, which manifested as splitting failure at low damage levels and shear slip failure at high damage levels. However, the bolts did influence the characteristics of the failure, with the failure features of damaged rock being more diverse under the influence of bolts. The failure mode of the rock is related to the degree of damage, which can be represented by the ratio η of wing crack length to main crack length. When η is large, splitting failure occurs, whereas smaller η leads to shear slip failure. Bolts can reduce the stress intensity factor at the crack tip to some extent, inhibiting the propagation of wing cracks. This study effectively explains, from a microscopic perspective, the relationship between the failure modes of rock-anchor composite bodies and the anchoring effect of bolts.

Laryngeal squamous cell carcinoma (LSCC) is a common tumor type. High recurrence rates remain an important factor affecting the survival and quality of life of advanced LSCC patients. We aimed to build a new nomogram and a random survival forest model using machine learning to predict the risk of LSCC progress. The study included 671 patients with AJCC stages III–IV LSCC. To develop a prognostic model, Cox regression analyses were used to assess the relationship between clinic-pathologic factors and disease-free survival (DFS). RSF analysis was also used to predict the DFS of LSCC patients. The ROC curve revealed that the Cox model exhibited good sensitivity and specificity in predicting DFS in the training and validation cohorts (1 year, validation AUC = 0.679, training AUC = 0.693; 3 years, validation AUC = 0.716, training AUC = 0.655; 5 years, validation AUC = 0.717, training AUC = 0.659). Random survival forest analysis showed that N stage, clinical stage, and postoperative chemoradiotherapy were prognostically significant variables associated with survival. The random forest model exhibited better prediction ability than the Cox regression model in the training cohort; however, the two models showed similar prediction ability in the validation cohort.

The coordination of agricultural water use efficiency (AWUE) and the level of agricultural economic development (AEDL) is crucial for promoting high-quality development in the Qilian Mountains region. This study was aimed at examining the synergistic development trends and spatial distribution characteristics of AWUE and AEDL. This study focused on 11 cities (autonomous prefectures) within and adjacent to the Qilian Mountains region, employing the Super-SBM model and a comprehensive evaluation model to measure AWUE and AEDL. The coupling coordination degree model and coefficient of variation method were used to analyze the level of development coordination as well as spatial differences. The findings indicate that (1) from 2010 to 2022, both AWUE and the AEDL in these areas showed a significant upward trend, with AWUE increasing from 0.379 to 0.924 and AEDL rising from 0.284 to 0.437. The spatial analysis reveals a pattern of high values in the northeast and low values in other regions; (2) the coupling degree (CD) between AWUE and AEDL is in the primary coupling stage (0.456–0.474), with the coupling coordination degree (CCD) transitioning from low (0.401) to high coordination (0.554) between 2010 and 2022. The spatial distribution characteristics of the CD and CCD are high in the middle section and low in the eastern and western sections. Furthermore, the high coordination area exhibits a spreading evolutionary trend, from Zhangye City to Hexi Corridor regions, from the middle to the east and west sections, and from the north to the south section. These findings suggest that the coupling and coordination between AWUE and AEDL in the Qilian Mountains region have been improved, reaching a higher level. Combined with targeted suggestions, the findings offer valuable insights for designing targeted policies and water-saving measures to advance sustainable agricultural development in arid areas.

Supplier selection (SS) has emerged as a critical challenge for companies aiming to enhance the operational management of their supply chains, a task that has grown in complexity with the advent of Industry 4.0 and the ongoing digital transformation. Recognizing the gaps in current literature—specifically, the lack of consideration for stakeholders' expectations in guiding SS, as well as the inadequate handling of epistemic and stochastic uncertainties—this paper introduces a multiple-criteria Quality Function Deployment (QFD)-based model for SS. To address epistemic uncertainty, we put forward a novel subjective judgment representation method, which is named as linguistic term set integrated with discrete subjective probability distribution (LTS-DSPD), to enable decision-makers to express their judgments in a manner that is both simpler and more nuanced. Furthermore, we also give the elicitation methods and computing techniques for LTS-DSPD. Then, we integrate stakeholders’ requirements, along with their preferences and expectations for these requirements to inform and guide SS. To effectively operationalize this guidance, we design the QFD-based methods to transform stakeholders' inputs into the assessment criteria for SS, the weights of criteria, and the expectations for the performances of suppliers on each criterion, respectively. To address stochastic uncertainty, we have developed an innovative methodology for characterizing it, and adopt prospect theory to quantify the overall utility of alternative suppliers. The paper concludes with a case study to demonstrate its practical application and effectiveness in streamlining SS process.

Rhizomatous plants forming patchy expansions show high potential for sand fixation in mobile dunes with heterogeneous resources, yet resource allocation strategies among different plant parts and reproductive methods during patch expansion remain poorly understood. This study investigated the growth dynamics and allocation of key morphological traits (plant height, leaf area, and rhizome length) of Psammochloa villosa across clonal patches of different sizes in the desert–oasis ecotone of Northwest China. Results revealed that aboveground traits exhibited rapid unimodal growth, peaking in the order of plant height followed by leaf area, whereas rhizomes showed prolonged bimodal growth (approximately 8 months) with delayed peak phases. During clonal expansion from micro to medium patches, asexual clusters decreased the growth rate and increment of leaf area per ramet but markedly increased that of rhizome length, reflecting a shift from photosynthetic to clonal investment. The ratio of belowground to aboveground growth and their allometric relationship both shifted when patch size reached an lg‐transformed area of 2–3, while sexual reproduction occurred exclusively in large patches (lg‐transformed area more than 4). The maximum growth increment of aboveground traits in sexual clusters was more than double that in asexual clusters, indicating a high energy cost associated with sexual reproduction. This was supported by increased aboveground allocation in asexual ramets alongside reduced absolute rhizome growth, despite elevated relative rhizome growth rates. Consequently, sexual reproduction occurs at the expense of rhizome expansion, requiring extensive clonal expansion to accumulate resources in the resource‐limited environment. P. villosa thus adapts to arid sandy habitats through stage‐specific resource reallocation during patch expansion, with long‐term spatial occupation via rhizomes preceding the costly sexual reproduction. These insights elucidate the persistence mechanisms of sand‐fixing rhizomatous herbs and highlight the need to quantify threshold patch sizes and ages to enable sexual reproduction across environmental gradients.

Background: Head and neck squamous cell carcinoma (HNSCC) was the seventh most common cancer worldwide in 2018. Lymphatic metastasis (LM) is closely related to HNSCC prognosis and recurrence. However, the underlying mechanism of LM remains unclear. Therefore, this study aimed to identify the key genes in the LM of HNSCC. Methods: We used The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) between LM and non-LM cases. A random forest model, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, and cytoHubba were used to identify hub genes among DEGs, including KRT20 (Cytokeratins 20). We analyzed the survival of KRT20 in TCGA, and we overexpressed KRT20 in HNSCC cell lines to investigate its effects on migration and invasion. We also correlated the expression of KRT20 in HNSCC tissue microarrays with survival and clinicopathological features. Results: We identified 243 DEGs—143 upregulated genes and 100 downregulated genes. Further analysis revealed that KRT20 is a potential key gene associated with LM and overall survival rates among patients with HNSCC. Overexpression of KRT20 increased the migration and invasion ability of HNSCC cell lines Tu686 and FD-LSC-1. Tissue microarray studies demonstrated an overexpression of KRT20 among N1+ patients (including N1-N3 patients). Survival analysis results and the clinicopathological features of HNSCC tissue microarrays were consistent with our analysis of TCGA. Thus, a high KRT20 expression level might suggest an adverse HNSCC prognosis. Our gene set enrichment analysis showed that KRT20 participates in many metabolic pathways, including those related to tumorigenesis and cancer development. Conclusions: We propose that KRT20 may be a key gene in HNSCC with LM.

Flowering phenology is a critical life-history trait determining the reproductive success of entomophilous plants in alpine ecosystems. However, comprehensive characterizations and cross-level comparative studies of these phenological patterns remains limited. In this study, we conducted high-frequency phenological monitoring of 36 entomophilous species across 15 families in a typical alpine grassland in the Qilian Mountains, extracting parameters at the individual, population, functional group, and community levels. Our results demonstrate that the community flowering season lasted approximately 100 days, beginning in early June and terminating in early September. Community flower abundance exhibited a bimodal distribution, reaching distinct peaks in early July and early August. Overall flowering synchrony among plants was generally low, indicating clear temporal niche differentiation. The plant community comprised early-, mid-, and late-flowering groups. The temporal overlap of early- and mid-flowering species jointly contributed to the first floral peak in early July, whereas late-flowering species drove the second peak in early August. Among these functional groups, the early-flowering group accounted for the lowest proportion of species, whereas the late-flowering group accounted for the highest. Both early- and late-flowering groups tended toward mass-flowering strategies, while the mid-flowering group leaned toward steady-flowering. At the interspecific level, earlier-flowering species typically exhibited shorter flowering durations and more right-skewed floral distributions, meaning the interval from the first to the peak flowering date was shorter than that from the peak to the last flowering date. At the intraspecific level, individuals that initiated flowering early or late typically exhibited longer flowering durations but lower synchrony with conspecifics; conversely, individuals blooming during the middle of the population’s season exhibited the opposite pattern. These findings systematically delineate the hierarchical temporal structure of alpine entomophilous communities, objectively reflecting the differential utilization of a limited growing season through inter- and intraspecific phenological variation.

Perfect transfer of the two-qubit quantum information in spin chain without any magnetic field is investigated. In our work, the spin chain is placed in a particular low-intensity magnetic field whose value is symmetrical about the chain and whose difference value between the nearest lattice points is constant. And the influence of the magnetic field has been studied by the method of matrix diagonalization. It is shown that the difference value of the magnetic field determines the fidelity of the two-qubit quantum information transfer in the spin chain. On the basis of this, we propose a theoretical scheme to achieve the quantum control of the two-qubit quantum information perfect transfer by applying this magnetic field, so that the perfect transfer time of information can be controlled arbitrarily as needed.