Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples. The predicted probability values are integrated by four heterogeneous ensemble strategies to expand samples from high-confidence results. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), partition frequency ratio (PFR), and other verification methods. The results demonstrate that the negative sample based on FFR sampling is more accurate than the random sampling method, and the FR-SSELR model based on frequency ratio sampling and semi-supervised ensemble strategy exhibits the highest performance (AUC = 0.971, ACC = 0.941). A more reasonable landslide susceptibility map was drawn based on this model, with the lowest percentage of landslides in the low and very low susceptibility zones (sum of PFR = 0.194), as well as the highest percentage of landslides in the high and very high susceptibility zones (sum of PFR = 6.800). Furthermore, the FR-SSELR model improved economic benefits by 3.82-14.2%, offering valuable guidance for decision-making regarding landslide management and the sustainability of Ji'an City.

Effect of a Novel 'Scan Body' on the In Vitro Scanning Accuracy of Full-Arch Implant Impressions.

OBJECTIVE: This in vitro study aimed to determine whether a newly designed arcuate scan body can improve intraoral scanning accuracy for implant rehabilitation of edentulous jaws. MATERIAL AND METHODS: A master model containing 4 implant abutment replicas was fabricated and digitized with different scan bodies using an intraoral scanner. Four types of scan bodies were evaluated: original scan bodies (group OS), computer-aided design and computer-aided manufacturing (CAD/CAM) scan bodies without extension (group CS), CAD/CAM scan bodies with straight extension (group CSS), and CAD/CAM scan bodies with arcuate extension (group CSA). Conventional splinted open-tray impressions (group CI) were used as controls. The master model and the poured casts were digitized using a laboratory scanner. Impressions were repeated 10 times each in 5 groups. Scans in standard tessellation language format were exported to reverse engineering software and root mean square (RMS) values were used for trueness and precision assessments. In each group, 45 RMS values were acquired for precision evaluation and 10 RMS values were obtained for trueness assessment. Statistical evaluation was performed with the Kruskal-Wallis test and Dunn-Bonferroni test (α = 0.05). RESULTS: The median trueness values were 41.40, 55.95, 39.80, 39.75, and 22.30 µm for group OS, CS, CSS, CSA, and CI, respectively. CI showed better trueness than OS (P = .020), CS (P < .001), and CSS (P = .035). The median precisions for group OS, CS, CSS, CSA, and CI were 47.40, 51.50, 43.90, 25.20, and 24.60 µm. respectively. The precision of CSA and CI were higher than OS (P < .001), CS (P < .001), and CSS (P < .001). Between CI and CSA, there was no significant difference (P = 1.000). CONCLUSIONS: For full-arch implant rehabilitation, the scan body with arcuate extension could improve the intraoral scanning precision and showed similar 3-dimensional discrepancy compared to conventional splinted open-tray impressions.

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development.

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

Analysis on the susceptibility of environmental geological disasters considering regional sustainable development.

Environmental geological disasters seriously threaten human lives and property. A reasonable analysis of the susceptibility to environmental geological disasters is the basis for disaster prevention and mitigation and can promote the sustainable development of regional economy. This study analyzes the susceptibility of environmental geological disasters, such as collapses, landslides, and debris flows in Helong City, China. Through investigation and comprehensive analysis, ten environmental, geological disaster causing factors, including stratum lithology, distance from the fault, elevation, slope, aspect, rainfall, distance from the water system, NDVI, distance from the road, and profile curvature, were extracted. Combined with GIS, a vulnerability analysis database of environmental geological disasters was established, and vulnerability zoning prediction was performed by using two models of information amount and a generalized regression neural network (GRNN). Then, disaster-vulnerability factors such as population density, road density, GDP, and land use type were added. The results show that the predicted results of the two models are similar to the actual survey results.. The environmental geological disasters in the study area are mainly low and not prone to occur, and the northeast and central areas are highly prone to environmental geological disasters, which are the key prevention and control areas in the study area. The coverage rate of high-vulnerability areas with a high degree of economic development is 8.63%, and the prediction results of the GRNN model are mostly distributed in spots and strips, which is more conducive to accurate disaster prevention and mitigation and cost reduction, promotes regional sustainable development, and has guiding significance for disaster prevention and control and urban planning and construction.

GIS-based for prediction and prevention of environmental geological disaster susceptibility: From a perspective of sustainable development.

Geological disasters seriously threaten the safety of human life, property, ecological resources, and the environment. Effective control of geological disasters is the focus of achieving sustainable social development. The Helong City (Jilin Province, China) was selected as the case study. Combined with GIS technology, a new integrated prediction model of geological disaster susceptibility was developed to improve the accuracy of geological disaster assessment, reduce the cost of geological disaster treatment, and ensure the sustainable development of ecological environment. The research results showed that elevation and normalized difference vegetation index (NDVI) were the key factors affecting susceptibility. Compared with the conventional model, the accuracy of the developing integrated model FR-DT and FR-RF was improved by more than 6%, and the disaster points were more concentrated in the high susceptibility zone. Statistical results of disaster treatment cost estimation and gross domestic product (GDP) value showed that the integrated model can save about 10% of treatment cost, and the ratio of total GDP/disaster governance cost was higher. The performance of the integrated model FR-DT and FR-RF had obvious advantages over the conventional model in terms of prediction accuracy, prevention pertinence, and prevention cost. These research results promote the advancement of geological disaster prevention and control technology, ensure the safety of the geological environment, and are of great significance to the sustainable development of the regional economy.

Probabilistic-fuzzy risk assessment and source analysis of heavy metals in soil considering uncertainty: A case study of Jinling Reservoir in China.

Considering the uncertainty caused by the random error of the sample measurement, the heterogeneity of spatial and temporal distribution of pollutants, and the traditional method of selecting a single parameter for evaluation, based on fuzzy theory, Hakanson potential ecological risk index (considering heavy metal enrichment, ecotoxicity and bioavailability) and United States Environmental Protection Agency health risk assessment system, the fuzzy ecological risk and health risk assessment methods for of heavy metals in soil were established. In the soil of the Jinling Reservoir area, Cd, which has high bioavailability, had the highest average contribution rate to RI, and thus was, regarded as a priority metal for ecological risk. Sites JL9 and JL11 were the priority areas. The heavy metals did not pose a clear hazard to human health, but children had a higher health risk. Pb and As were regarded as the priority metals for health risk. Fuzzy evaluation provided the risk interval and membership degree, contained more parameter information, quantified and reduced the uncertainty of parameters, provided more comprehensive results, and compensated for the deficiency of deterministic evaluation. As the main source of heavy metals, the intensity of agricultural activities in the study area must be controlled to avoid excessive input and accumulation of heavy metals, which may damage the ecological environment and endanger human health.

Ecological risk, health risk assessment, and pollution source analysis of Xinli Lake wetland based on triangular fuzzy number.

Wetland environmental pollution has become a global problem involving the ecological environment and human health. This study measured the concentration of seven potentially toxic elements (PTEs Hg, Cd, Zn, Cu, Cr, Pb, and As) in the soil upstream of the Xinli Lake wetland in China. Based on the fuzzy theory, the sources, spatial distribution, ecological risks, and health risks of pollutants are studied. The result shows that the concentrations of the seven potentially toxic elements are close to or exceed the background value, and their spatial distribution showed irregular changes. The soil upstream of the wetland has not been seriously polluted, and Cd, which has higher bioavailability, is the priority element for ecological risk. Pollutants do not harm human health; children face higher health risks; Pb and As have the highest carcinogenic and non-carcinogenic risks, respectively. Zn, Cu, Cr, Pb, and As in the study area are derived from agricultural activities, while Hg and Cd are mainly affected by soil-forming parent materials. Attention should be paid to controlling the intensity of agricultural activities to avoid excessive input and accumulation of pollutants that would harm the ecological environment and human health.

Improved scanning accuracy with newly designed scan bodies: An in vitro study comparing digital versus conventional impression techniques for complete-arch implant rehabilitation.

OBJECTIVES: To compare the accuracy of an original and two newly designed CAD/CAM scan bodies used in digital impressions with one another as well as conventional implant impressions. MATERIAL AND METHODS: A reference model containing four implants was fabricated. Digital impressions were taken using an intraoral scanner with different scan bodies: original scan bodies for Group I (DO), CAD/CAM scan bodies without extensional structure for Group II (DC), and CAD/CAM scan bodies with extensional structure for Group III (DCE). For Group IV, conventional splinted open-tray impressions (CI) were taken. The reference model and conventional stone casts were digitalized with a laboratory reference scanner. The Standard Tessellation Language datasets were imported into an inspection software for trueness and precision assessment. Statistical analysis was performed with a Kruskal-Wallis test and Dunn-Bonferroni test. The level of significance was set at α = .05. RESULTS: The median of trueness was 35.85, 38.50, 28.45, and 25.55 µm for Group I, II, III, and IV, respectively. CI was more accurate than DO (p = .015) and DC (p = .002). The median of precision was 48.40, 48.90, 27.30, and 19.00 for Group I, II, III, and IV, respectively. CI was more accurate than DO (p < .001), DC (p < .001), and DCE (p = .007). DCE was more accurate than DC (p < .001) and DO (p < .001). CONCLUSIONS: The design of the extensional structure could significantly improve scanning accuracy. Conventional splinted open-tray impressions were more accurate than digital impressions for full-arch implant rehabilitation.

[Clinical application of IPS-empress 2 pressable all-ceramic crowns].

OBJECTIVE: To evaluate the clinical prosthetic effect of IPS-Empress 2 pressahie ceramic crowns. METHODS: 198 teeth of 70 patients were restored with IPS-Empress 2 pressahie ceramic crowns. The patients were asked to return in one week and every half year. The clinical prosthetic effect was evaluated. RESULTS: Through follow-up of 3-38 months, the veneer porcelain crowns of 3 teeth were broken. 2 crowns fall off due to teeth fracture, gingivitis occurred in 2 teeth, pulpitis or periapical periodontitis occurred in 3 teeth. The shades of 3 crowns were darkening. The prosthetic effect of 185 teeth was satisfied. The rate of satisfaction was 93.4%. CONCLUSION: IPS-Empress 2 pressable all-ceramic crown has the advantages of aesthetic effect, good hiocompatihility and simple fabrication. But its strength is not enough for posterior teeth and it can not cover the deep color of non-vital teeth and metal materials.