Data Mining and Deep Learning for Predicting the Displacement of “Step-Like” Landslides

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R2 values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

Triggering Factors for Organism' Evolution

The purpose of the article is to know the trigerring factor for arganism’ Evolution. Evolution is defined as the change in the heritable features of a population of organisms from one generation to the next, or from one generation to the next. Evolution, according to the contemporary conception, is characterized by changes in the attributes of living organisms through time, with these changes being guided by the process of natural selection

Cyclic vomiting syndrome responding to amisulpride – A case report

Cyclic vomiting syndrome (CVS) is an uncommon functional condition defined by recurring episodic stereotyped vomiting with a sudden start and an unknown origin. CVS can be triggered by a variety of conditions, and the course and management are often determined by the triggering factors. We present the case of a 13-year-old female youngster who complained of frequent bouts of vomiting that coincided with the onset of menses. We started her on oral amisulpride 50 mg a few days before each menstrual period. As a result of the treatment, in the following menstrual cycle, she had a dramatic improvement in her symptoms.

Monitoring the Effects of Slope Hazard Mitigation and Weather on Rockfall along a Colorado Highway Using Terrestrial Laser Scanning

Rockfall is a frequent hazard in mountainous areas, but risks can be mitigated by the construction of protection structures and slope modification. In this study, two rock slopes along a highway in western Colorado were monitored monthly using Terrestrial Laser Scanning (TLS) before, during, and after mitigation activities were performed to observe the influence of construction and weather variables on rockfall activity. Between September 2020 and February 2021, the slopes were mechanically scaled and reinforced using rock bolts, wire mesh, and polyurethane resin injection. We used a state-of-the-art TLS monitoring workflow to process the acquired point clouds, including semi-automated algorithms for alignment, change detection, clustering, and rockfall-volume calculation. Our initial hypotheses were that the slope-construction activities would have an immediate effect on the rockfall rate post-construction and would exhibit a decreased correlation with weather-related triggering factors, such as precipitation and freeze-thaw cycles. However, our observations did not confirm this, and instead an increase in post-construction rockfall was recorded, with strong correlation to weather-related triggering factors. While this does not suggest that the overall mitigation efforts were ineffective in reducing rockfall hazard and risk of large blocks, we did not find evidence that mitigation efforts influenced the rockfall hazard associated with the release of small- to medium-sized blocks (<1 m3). These results can be used to develop improved and tailored mitigation methods for rock slopes in the future.

Analysis of plant root properties, texture and porosity that affects landslides in Tangka Sub-Watershed

Tangka Sub-Watershed in West Sinjai District is one of the sub-watersheds in South Sulawesi, which often experiences landslides. Factors causing landslides were divided into control factors and trigger factors. One of the triggering factors for landslides is the characteristics of soil and plant properties. Several previous research results stated that soil characteristics greatly affect the occurrence of landslides, while plant roots help stabilize soil bonds and slopes. This study aims to determine the triggering factors of the soil (soil texture and porosity) and the distribution of plant roots that affect the occurrence of landslides. Soil texture analysis using hydrometer method. Soil porosity and moisture content using gravimetric and pycnometer methods. The results showed that the soil texture was dominated by silty clay loam impact on low soil cohesion. Intensive land use decreases soil porosity to <50% and increases soil bulk density to 1.4 g/cm3. The soil quickly becomes saturated and could induce the rotting of plant roots. These characteristics increase the vulnerability of the soil, thereby increasing the potential for landslides.