The idea of green mining has attracted much attention over the past decade. Accurate identification of key elements of ecological restoration in mining areas is an important prerequisite for ecosystem restoration and reconstruction and improving the quality of ecological environment. The goal of this study is to develop a five-factor index system for ecological restoration in mining areas, with the Huojitu well serving as a case study of a typical western shallow-buried high-intensity mining area in China. The factors include vegetation cove, soil, ecological landscape, land damage and site condition. An obstacle factor diagnosis model based on the coupling of obstacle degree and Shefold restriction law has been established in this research. This model is used to identify the obstacle factors and analyze the key elements of ecological restoration in the mining area. The key elements of ecological restoration are identified by combining the obstacle degree of each obstacle factor. According to the findings, out of all the areas included in the study, the one pertaining to soil conditions was the biggest at 35.29 km2, or 31.91% of the total, followed by land damage condition (21.25 km2 similar to 19.20%), site condition (19.74 km2 similar to 17.84%), vegetation cover (3.34 km2, similar to 3.02%), and ecological landscape (31.08 km2 similar to 28.03%). Based on the identification results of critical elements in mining area ecological restoration, this study proposes targeted remediation strategies and formulates corresponding site-specific rehabilitation measures to facilitate efficient ecosystem recovery in mining regions. This approach not only advances the practical implementation of ecological restoration technologies but also provides a valuable reference framework for sustainable ecosystem management in post-mining landscapes.

Verticillium wilt (VW) is one of the most common and devastating diseases in cotton production, and early diagnosis is very important to alleviate the damage of VW. Recent studies have shown that early diagnosis and prevention of soil-borne diseases can be achieved by detecting spectral changes related to chlorophyll fluorescence and transpiration. However, there are no systematic studies to report the heterogeneity of photosynthetic characteristics and their spectral responses of plant leaves at the early stage of VW. In this study, the spatial heterogeneity characteristics in chlorophyll fluorescence of cotton leaves during the incubation period of VW were discussed, and the pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information, aiming to realize the early diagnosis of VW of cotton. The results showed that the chlorophyll fluorescence parameters Y(NPQ) (quantum yield of regulated energy dissipation) and NPQ/4 (non-photochemical quenching/4) values of cotton increased and the Y(II) (effective quantum yield of photosystem II) decreased significantly during the asymptomatic period of VW, indicating heterogeneity in photosynthetic capacity of leaves in the early stage of VW, i.e., VW developed from leaf margins to leaf center, and leaf margin was the area where chlorophyll fluorescence changed firstly. Furthermore, the multi-task learning model constructed with vegetation index and wavelet features accurately inversed the pixellevel heterogeneous characteristics of leaf Y(NPQ) and Y(II). The spectral information had the best inversion performance for the local heterogeneous regions of Y(II), with a classification accuracy of 85.6 %, a Kappa coefficient of 0.71, an r2 (coefficient of determination) of 0.66, and a RMSE (root mean square error) of 0.06. According to the inversion results of the local heterogeneous region of Y(II), the accurate diagnosis of early-stage VW was realized, with an accuracy of 87.4 % and a Kappa coefficient of 0.75. This study will provide a new method for the early prevention and control of VW.

Because pineapple is an important crop in Vietnam, it is crucial to assess the nutrition status of the pineapple. Although the diagnosis and recommendation integrated system (DRIS) is a reliable approach, finding the right leaf position to diagnose is vital. Therefore, the aim of the current study is to determine suitable leaf positions for creating DRIS norms for macro- and micronutrients in pineapple leaf. Healthy pineapple leaves without pest or disease damages were sampled from 60 pineapple farms and analyzed for N, P, K, Na, Ca, Mg, Cu, Fe, Zn, and Mn concentrations. The results revealed that the critical yield was 13.3 t ha-1 among the 60 farms, dividing into 23 farms as the high-yielding group (>= 13.3 t ha(-1)) and 37 farms as the low-yielding group (< 13.3 t ha(-1)). The concentrations of mineral nutrients (N, P, K, Ca, Mg, Cu and Zn) and pineapple fruit yields in the high-yielding group were greater than those in the low-yielding one. On the other hand, the Na, Fe, and Mn concentrations showed the opposite pattern. Selected leaf positions must possess significantly different nutrient ratios and have more than 14 nutrient ratio pairs between the two yield groups. Therefore, leaf positions from +15 to +19 were selected to create DRIS norms. Nine sets of DRIS norms have been created at leaf +1, +3, +7, +9, +16, +18, +21, +22, and +29 for plant pineapples.

Background: The current focus is largely on whole course medical management of coronavirus disease-19 (COVID-19) with real-time polymerase chain reaction (RT-PCR) and radiological features, while the mild cases are usually missed. Thus, combination of multiple diagnostic methods is urgent to understand COVID-19 fully and to monitor the progression of COVID-19. Methods: laboratory variables of 40 mild COVID-19 patients, 30 patients with community-acquired pneumonia (CAP) and 32 healthy individuals were analyzed by principal component analysis (PCA), Kruskal test, Procrustes test, the vegan package in R, CCA package and receiver operating characteristic to investigate the characteristics of the laboratory variables and their relationships in COVID-19. Results: The correlations between the laboratory variables presented a variety of intricate linkages in the COVID-19 group compared with the healthy group and CAP patient group. The prediction probability of the combination of lymphocyte count (LY), eosinophil (EO) and platelets (PLT) was 0.847, 0.854 for the combination of lactate (LDH), creatine kinase isoenzyme (CK-MB), and C-reactive protein (CRP), 0.740 for the combination of EO, white blood cell count (WBC) and neutrophil count (NEUT) and 0.872 for the combination of CK-MB and P. Conclusions: The correlations between the laboratory variables in the COVID-19 group could be a unique characteristic showing promise as a method for COVID-19 prediction and monitoring progression of COVID-19 infection.

There is a complex multifactorial coupling effect among the damages of various protection structures on slopes. Existing research focused on the health assessment of individual structures is often insufficient in representing the overall health status of the protection engineering system. Considering the characteristics of expansive soil slope protection engineering, this study proposes a health diagnosis method using combined weights and binary K-means clustering algorithm. The method quantifies the damage data of protection structures based on subjective and objective weights, and clusters the data by combining the binary K-means method and target vector layer to obtain the diagnosis results. Furthermore, an XGBoost-based surrogate diagnosis model is constructed to omit the repetitive modelling process in practical applications to achieve dynamic diagnosis. The proposed method is validated to an expansive soil slope in Gaochun district, Nanjing. The results show that the proposed method can accurately evaluate protection engineering with different degrees of damage; the surrogate model follows the same weight assignment process as the diagnostic method to establish reliable prediction. Based on the proposed method, damage coupling effects between individual protection structures are captured, and targeted maintenance and repair can be implemented. The proposed method can be further extended to other slope engineering.

Forest fires are disturbances that, in addition to causing effects on vegetation, have an impact on the components and properties of soils. Fires influence soil biota, soil organic matter, macro and micronutrients, and physical properties such as bulk density, porosity, hardness, infiltration, aggregates, color and soil permeability. Therefore, the main objective of this research was to evaluate the physical and hydrological properties of soils affected by forest fires in El Salto, Durango. In particular, two areas damaged by forest fires (2023-2024) were analyzed and compared against an unaffected area to determine the degree of recovery of soil properties after a forest fire. The dominant soil corresponds to the Cambisol type, characterized by being a shallow soil, with the presence of fragmented rocks and the formation of a differentiated profile in horizons. The results indicate that the unaffected area presents optimal conditions in the soil physical properties unlike the observed in the areas were forest fires ocurred; the rates of water infiltration into the soil, apparent density, porosity, aggregate stability, color and mechanical resistance to penetration were significantly modified. The information will be useful to have a better diagnosis of the state of the soil before and after the forest fires occurred, and in this way to be able to make more precise management recommendations of water, soil and vegetation resources.