Topographic Data Research Articles

Geoelectrical and seismoelectric mapping of subsurface pollution in a closed landfill near the Tongo Bassa and Ngongue river, Douala Cameroon

Located in Douala V (Cameroon), Makepé Missoké is a lowland area that is influenced by the Tongo Bassa and Ngongué rivers. The site has a closed dumpsite, that operated from the 1975’s to 2003 for the disposal and storage of domestic waste produced in Douala. Geoelectric profiling (ERT) coupled with seismo-electric imaging was used to characterize the shallow aquifer of the Douala subbasin and map subsurface pollution. Fourteen geoelectrical profiles were deployed between June and August 2018 and March 2019. Along these lines, 49 sets of seismo-electric point data were collected in December 2021. A total of 118 wells drilled (2018) were used to obtain water level and topographic data. The aquifer geometry, hydraulic characteristics, permeability, lithology and leachate plume extent were determined. This survey enables us to visualize waste infiltration and migration within Makepé. The drilled wells indicate that the main lithologies observed are clay, sandy clay, sand, clayey sand and gravel. The leachate plume observed after processing the electric profiles had a resistivity signature of ≤ 10 Ωm, and high electrical conductivity are observed in some wells. The leachate migrates within the subsurface along a northwest‒southwest trend, where ground water pollution is observed due to leachate infiltration. This infiltration resulted in poor water quality indices in some collected samples. Such pollution is common in unconfined aquifers (< 50 m) due to the absence of a confining layer at the landfill. An increase in resistivity values with depth toward the northeast direction indicates progressive vertical dilution during leachate mineralization. This study integrated geoelectric and seismo-electric tomography with basic water chemistry analysis to effectively characterize the groundwater within the phreatic Quaternary/Mio-Pliocene aquifers of the Douala basin.

A Constrained Spectral Approximation of Subgrid‐Scale Orography on Unstructured Grids

AbstractThe representation of subgrid‐scale orography is a challenge in the physical parameterization of orographic gravity‐wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non‐quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale‐aware spectrum of subgrid‐scale orography on unstructured geodesic grids. The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well‐known least‐squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least‐squares spectral analysis in representing the physical energy of a spectrum. Studies involving real‐world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work.

More mass wasting activities reported in Sedongpu gully of Tibetan Plateau, China

Although the increase in the frequency of mass wasting events in the Sedongpu gully in recent years indicates that the Sedongpu gully has entered an intense mass wasting period, the current literature focuses only on ice‒rock avalanche events and lacks comprehensive knowledge of Sedongpu gully activity. To clarify the spatiotemporal distribution and scales of mass wasting events, we analysed multiple images from 1969 to present (including optical images and synthetic aperture radar (SAR) images) and topography data from 2013 to present. Since 1969, there have been at least 19 obvious mass wasting events that can be divided into 3 subpatterns: ice‒rock avalanches (IRAs, 8 events), ice‒moraine avalanches (IMAs, 2 events), and glacier debris flows (GDFs, 9 events). Since 2017, the Sedongpu gully has entered the most active period, i.e. more than 68% of events occurred after 2017, and approximately 530 Mm3 and 185 Mm3 of materials were removed from mixtures of glacial and moraine (MGM) and glacial source areas (GSAs). Recent continuous warning states that the temperature of the Sedongpu gully area exceeded 0 °C from April to July 2012, and the 2017 Mw 6.4 Nyingchi earthquake was critical in the current intense erosion state.

Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques

Landslides are among the most dangerous hazards in Asia, posing a significant threat to human lives, infrastructure, and sustainable development. Landslide susceptibility maps provide useful insights into hazard potential but lack quantitative exposure assessments to develop targeted mitigation strategies and resource allocation. This study aims to propose an integrated approach for landslide hazards and exposure evaluation using machine learning models on the Google Earth Engine environment and geospatial analysis techniques. A geospatial database was established to predict hazards and evaluate exposure, including data on topography, geology, hydrology, climate features, land use, and building data. The landslide susceptibility map was created with advanced machine-learning algorithms, including Classification And Regression Tree (CART), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The ROC curve and the Wilcoxon signed-rank test were employed to evaluate the performance differences among the CART, GB, RF, and SVM models. The results indicated that the RF model demonstrated the highest performance, leading to its selection for creating a landslide susceptibility map. Landslide exposure was evaluated by overlaying the landslide susceptibility map with building data to quantify the number of affected houses by landslides across districts and communes. The analysis results identified the Cho Don district as the most exposed, with 46,237 households located in high and very high landslide susceptibility zones, followed by Ba Be district (39,631 households), Bac Kan city (37,266 households), Bach Thong district (28,495 households), Cho Moi district (28,436 households), Na Ri district (17,723 households), Ngan Son district (14,142 households), and Pac Nam district (13,034 households). These findings enable the identification of the potential consequences of landslides on infrastructure, human settlements, and livelihoods, contributing to the promotion of disaster reduction and prevention strategies.

E-Cigarette Nicotine Delivery Among Young Adults by Nicotine Form, Concentration, and Flavor

Concerns have been raised about the abuse liability of modern e-cigarettes that use acidic additives to form nicotine salts, making the inhalation of nicotine smoother than freebase nicotine. To examine the effects of nicotine form and concentration and e-liquid flavor on subjective effects ratings, vaping behavior, and nicotine uptake among young adults who use e-cigarettes. In this single-blind, within-participant, crossover randomized clinical trial, a convenience sample of young adults aged 21 to 25 years who currently used e-cigarettes was recruited from December 2021 to August 2023, for in-person research laboratory visits in Columbus, Ohio. Participants completed up to 9 vaping sessions, starting with their usual e-cigarette brand in the first session followed by 1 of 8 laboratory-prepared e-liquids in a randomly assigned order in each subsequent session. Prepared e-liquids varied by nicotine form (salt-based vs freebase), nicotine concentration (5% vs 1% weight per weight), and flavor (menthol vs tobacco). Each session included a 5-minute, 10-puff standardized vaping period followed by 30 minutes of ad libitum vaping. At 4 time points (0, 5, 10, and 35 minutes) during each vaping session, plasma samples were collected for assessing nicotine uptake, and self-reports of urges, craving, and withdrawal were collected via questionnaires. Positive subjective effects were self-reported after 35 minutes of vaping using a visual analog scale; urges and cravings were reported using the Questionnaire of Smoking Urges (QSU). Puff topography data were collected throughout each vaping session. Seventy-two participants (mean [SD] age, 22.4 [1.4] years; 42 [58.3%] female) who sampled at least 1 laboratory-prepared e-liquid composed the analytic sample. Salt-based (vs freebase) nicotine e-liquids increased nicotine intake, with 5% salt-based e-liquids delivering the highest mean plasma levels of nicotine (11.2 ng/mL [95% CI, 9.3-13.2 ng/mL] at 5 minutes; 17.2 ng/mL [95% CI, 14.3-20.1 ng/mL] at 35 minutes) irrespective of flavors. Higher positive subjective effect ratings (eg, for liking) were received by salt-based (42.8; 95% CI, 39.4-46.1) vs freebase (32.0; 95% CI, 28.6-35.3) nicotine, 1% (43.4; 95% CI, 40.2-46.6) vs 5% (31.2; 95% CI, 27.7-34.6) nicotine, and menthol-flavored (43.2; 95% CI, 39.7-46.7) vs tobacco-flavored (31.5; 95% CI, 28.4-34.7) e-liquids. Salt-based and 1% but not menthol-flavored nicotine elicited more intense puffing (eg, 25% [95% CI, 12%-40%] more total puffs for nicotine salts vs freebase). All study e-liquids reduced urges and cravings, with 5% vs 1% nicotine being more effective (mean [SE] QSU-Desire score at 35 minutes, 15.4 [0.5] vs 16.7 [0.5]). In this crossover randomized clinical trial among young adult e-cigarette users, salt-based (vs freebase) nicotine e-liquids increased nicotine intake and yielded more positive subjective effects ratings and intense puffing behaviors, suggesting higher abuse potential. Restricting the level of acidic additives and menthol flavoring may reduce the addictiveness of e-cigarettes. ClinicalTrials.gov Identifier: NCT05458895.

Numerical simulation for large baseline interferometric imaging altimeter

Sea topography information holds significant importance in oceanic research and the climate change detection. Radar imaging altimetry has emerged as the leading approach for global ocean observation, employing synthetic aperture radar (SAR) interferometry to enhance the spatial resolution of Sea topography. Nevertheless, current payload capacity and satellite hardware limitations prevent the extension of the interferometric baseline by enlarging the physical antenna size. This constraint hinders achieving centimeter-level accuracy in interferometric altimetry. To address this challenge, we conducted a numerical simulation to assess the viability of a large baseline interferometric imaging altimeter (LB-IIA). By controlling the baseline within the range of 600–1000 m through spiral orbit design in two satellites and mitigating baseline de-correlation with the carrier frequency shift (CFS) technique, we aimed to overcome the above limitations. Our findings demonstrate the efficacy of the CFS technique in compensating for baseline decoherence, elevating coherence from less than 0.1 to over 0.85. Concurrently. The height difference accuracy between neighboring sea surfaces reaches 1 cm within a 1 km resolution. This study is anticipated to serve as a foundational reference for future interferometric imaging altimeter development, catering to the demand for high-precision sea topography data in accurate global bathymetry inversion.

On the transferability of residence time distributions in two 10-km long river sections with similar hydromorphic units

Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface and their residence time distributions (RTDs) in the subsurface are important for managing the water quality and ecosystem health in dynamic river corridors. However, field measurements and directly simulating high-spatial resolution HEFs and RTDs can be time-consuming, particularly at watershed-scale. Recent research has proposed the potential application of using hydromorphic units (HUs), which cluster the bathymetry and surface water hydrodynamics attributes, to aid in RTD estimation. However, more pioneering studies have demonstrated that underground structure and large-scale river channel geomorphology are the primary factors controlling the HEFs and RTDs. To address this contradiction, this work evaluates the HEFs and resulting HU-RTD relationships for two 10-km long river sections along the Columbia River, leveraging a one-way coupled three-dimensional transient surface–subsurface water transport modeling framework. Applying such a framework at the two river sections with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. The comparison reveals that the similarity and transferability of the HU-RTD relationship is very low for the two investigated river sections. This suggests that, creating a general algorithm to estimate RTDs in large-scale river reaches based solely on surface water hydrodynamics and short-distance bathymetry topography data may be nearly impossible.

Land suitability mapping for groundnut production in southern region of Borno state, Nigeria

Groundnut (Arachis hypogea) is a leguminous oilseed crop cultivated in the semi-arid and subtropical region of the world. In West Africa, groundnut plays an important role. This includes food production for household consumption, cash crops, sources of employment, and incomes for smallholders in rural households. In Nigeria, it was among the major sources of income before the oil boom. Some of the activities surrounding its production are the development of commerce, employment, and foreign exchange. This study aims to identify the most suitable sites for groundnut production in the Southern Borno Region, Nigeria with the objectives to identify areas of groundnut cultivation within the southern Borno region and to conduct a suitability analysis for groundnut production within the study area. Primary and secondary data and questionnaires were used in this study. Climatic data such as Temperature, Rainfall, Precipitation, Wind speed, Relative Humidity, Soil, and Topographic data were also used. The parameters were overlaid using the weighted overlaid operation in ArcGIS 10.3 and ranked from the most suitable to the least suitable for wheat production within the study region. The result of the study revealed that groundnut is cultivated in all parts of Southern Borno Region. It revealed that areas around Askira/Uba, Chibok and Damboa are the most suitable site for groundnut cultivation. This study demonstrates the capabilities of Remote Sensing and GIS Systems for multi-criteria analysis in Land Suitability Mapping. It is recommended that farmers around Askira/Uba, Chibok and Damboa be encouraged to cultivate more groundnuts.

IDENTIFICATION OF POTENTIAL LANDSLIDE AREAS IN NUSANIWE SUB-DISTRICT USING SLOPE MORPHOLOGY METHOD

This study aims to identify landslide-prone areas in Nusaniwe District using the Slope Morphology Method (SMORPH), which is based on slope and slope shape analysis. The research method uses the Slope Morphology Method (SMORPH) to assess landslide potential in Nusaniwe District. The analysis was carried out by mapping the slope gradient and slope shape in the area to determine areas at high risk of landslides. Topographic and geological data were collected and analyzed to identify factors that contribute to landslide risk. The results of the analysis show that complex geological and topographic conditions in Nusaniwe District increase the risk of landslides. Areas with high slope gradients and steep slope shapes are identified as the most landslide-prone areas. This assessment revealed a significant relationship between slope gradient, slope shape, and landslide potential in the area. The conclusion is that the analysis of slope and slope shape using the Slope Morphology Method (SMORPH) is effective in identifying landslide-prone areas in Nusaniwe District. To reduce landslide risk, interdisciplinary collaboration, community education, and development of effective mitigation strategies are needed. These findings provide a basis for planning and implementing landslide prevention measures in areas with similar conditions. Keywords: Landslide, Nusaniwe, Slope Morphology

Adopting Machine Learning to Automatically Identify a Suitable Surgery Type for Refractive Error Patients

efractive error is a visual impairment that arises when the ocular anatomy hinders the proper focusing of light onto the retina, the light-sensitive tissue layer located at the posterior region of the eye. This condition poses difficulties in achieving clear vision. Refractive error stands as the prevailing kind of visual impairment. The objective of this study is to classify two surgical approaches utilized in the treatment of refractive defects. Two commonly performed refractive surgeries are Photo-Refractive Keratectomy (PRK) and Laser-Assisted In-Situ Keratomileusis (LASIK). Artificial Intelligence (AI) encompasses a specific branch known as Machine Learning (ML), which is the focal point of this investigation. ML is dedicated to the advancement and use of algorithms that possess the capacity to acquire knowledge from data and enhance their predictive capabilities without explicit programming. The present study employs sophisticated ML methods to classify different types of refractive defect surgeries using a dataset of 124 samples obtained from Al-Rabee Hospital in Iraq, specifically focusing on corneal topography data. Two ML approaches, namely K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), are employed to predict the kind of refractive defect surgery. The findings produced from the experiment demonstrated an accuracy rate of 90.32% for the KNN algorithm and a perfect accuracy rate of 100% for the ANN algorithm. Additionally, the KNN algorithm exhibited a sensitivity of 90% and a specificity of 90.54%. The study’s findings indicate that the ANN classifier outperforms the KNN classifier.

Characteristics and Driving Mechanisms of Coastal Wind Speed during the Typhoon Season: A Case Study of Typhoon Lekima

The development and utilization of wind energy is of great significance to the sustainable development of China’s economy and the realization of the “dual carbon” goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system’s wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions.

Quantitative estimation of the effective elastic thickness around the Burma Plate and correlation analysis of its influencing factors

The Burma Plate is a microplate that extends along the boundary between the Indian and Eurasian plates. It is characterized by an extraordinarily complex lithospheric tectonic setting, resulting from the continental collision in the north, the oceanic crustal subduction in the south, and the large amount of sediment from the Tibetan Plateau. The lithospheric strength is a key to understanding the tectonic evolution of the Burma Plate. In this study, we use topography and gravity disturbance data to estimate the spatial distribution of effective elastic thickness Te, which is a measure of lithospheric strength. The Tevalues range from ∼10 km to 80 km, with higher values in the Indian Plate than those in the other regions. The non-isostatic flexural effects of sediment loading and subducting slab pull can bias the Te estimation, with maximum reductions of ∼50 km and ∼10 km, respectively. The consistent distributions of the Te and the shear wave velocity anomaly ΔVsat 100 km depth suggest that the lithospheric strength is generally controlled by the thermal structure of the upper mantle. Meanwhile, the Te variations are highly related to the geometry of the subducting Indian Plate along the collision and subduction zones, indicating that the plate tectonics play a dominant role in determining the lithospheric strength of the Burma Plate.

Estimating the dynamics of ecosystem functions under climate change in a temperate forest region

Understanding the dynamics and spatial patterns of forest biodiversity and functioning is vital for effective conservation and management. This study introduces a new framework for estimating the spatio-temporal dynamics of biodiversity and ecosystem functions in northeastern China. Our novel approach evaluates 96 future scenarios, including four points in time, four socio-economic pathways, and six global climate models based on an extensive network of permanent field plots with specific bioclimatic, topographic, and soil data. Our findings indicate a future trend of diminishing biodiversity and forest biomass productivity and increasing biomass density and carbon storage. The relationship between biodiversity and ecosystem functionality indicates a non-linear response to changes in temperature and precipitation, and a stable correlation between biodiversity and ecosystem stability across most of the socio-economic pathways. Unravelling the complex relationship between biodiversity, ecosystem functions, and stability of production, in the face of global change, requires new methods of analysis that were used in this study, including machine learning and other advanced methods.

Influence of Opacity Depth on Big Bubble Formation During Deep Anterior Lamellar Keratoplasty in Corneal Stromal Scars.

To identify the key preoperative predictors of big bubble (BB) formation during deep anterior lamellar keratoplasty in patients with corneal stromal scars (CSS). This retrospective cohort study included consecutive patients with CSS after infective keratitis who underwent BB-deep anterior lamellar keratoplasty between January 2021 and July 2023 at a tertiary referral center. Topographic and tomographic data were collected to compare the rates and types of BB formations. Anterior segment optical coherence tomography (AS-OCT) was employed to assess the maximum depth of opacity by dividing the stroma into 3 zones of equal thickness: anterior (stage A), mid (stage B), and posterior stroma (stage C). Multivariate logistic regression analysis was performed to identify the potential preoperative predictors of bubble formation. Pneumatic dissection was achieved in 13 of 33 eyes (39.4%), with 11 BB type 1 eyes (33.3%) and 2 BB type 2 eyes (6.1%). According to AS-OCT grading, bubble formation was more frequent with CSS involving more superficial stromal layers (P <0.032). In the eyes with stage C, bubble formation failed 12 out of 14 times (85.7%, P <0.026). Spearman correlation showed that bubble formation was inversely associated with the AS-OCT grading (rho = -0.443, P = 0.001). After logistic regression analysis, AS-OCT grading was found to be the sole factor that predicted bubble formation (coeff. -1.58, confidence interval 95% -3.03 to -0.12, P = 0.034). Depth of opacity in CSS was the key determinant for predicting the success of pneumatic dissection, as advanced AS-OCT stages are strongly associated with BB failure.

Analysis of Spatiotemporal Evolution and Driving Forces of Vegetation from 2001 to 2020: A Case Study of Shandong Province, China

As global climate change intensifies and human activities escalate, changes in vegetation cover, an important ecological indicator, hold significant implications for ecosystem protection and management. Shandong Province, a critical agricultural and economic zone in China, experiences vegetation changes that crucially affect regional climate regulation and biodiversity conservation. This study employed normalized difference vegetation index (NDVI) data, combined with climatic, topographic, and anthropogenic activity data, utilizing trend analysis methods, partial correlation analysis, and Geodetector to comprehensively analyze the spatiotemporal variations and primary driving factors of vegetation cover in Shandong Province from 2001 to 2020.The findings indicate an overall upward trend in vegetation cover, particularly in areas with concentrated human activities. Climatic factors, such as precipitation and temperature, exhibit a positive correlation with vegetation growth, while land use changes emerge as one of the key drivers influencing vegetation dynamics. Additionally, topography also impacts the spatial distribution of vegetation to a certain extent. This research provides a scientific basis for ecological protection and land management in Shandong Province and similar regions, supporting the formulation of effective vegetation restoration and ecological conservation strategies.

Tidal dynamic response to riverbed evolution in the Yangtze River Estuary

Since 1958, there have been significant changes in the Yangtze River estuary. Due to extensive reclamation and construction of ports and channels, the water area has drastically decreased, resulting in corresponding changes in hydrodynamics and riverbeds at the mouth of the river. According to the analysis of measured topographic data and Delft3D-FLOW model for seven typical historical periods since 1958 at the Yangtze River Estuary, this study investigates the characteristics of riverbed evolution and tidal flow dynamics. From 1958 to 2019, driven by strong human activities, the total area of the Yangtze River Estuary decreased from 2084 km2 to 1403 km2, with a decrease of 32.7%, while the total volume of the corresponding river channel changed slightly and remained stable. Compared with 1958, the volume of the Yangtze River Estuary in 2019 only increased by 345 million m3, with an increase of about 4.1%. The tidal dynamic change of the Yangtze Estuary is closely related to the riverbed evolution of each reach, which not only shapes the estuary landform, but also is affected by the riverbed evolution. Tidal level, tidal range and water area change are closely related. With the decrease of water area in the Yangtze River Estuary, tidal range tends to increase. Tidal prism change is closely related to channel volume. In the past 60 years, the tidal volume at the mouth of the Yangtze River has decreased by 8%. The research findings will provide technical support for enhancing flood control and tide resistance measures at the Yangtze River Estuary, as well as formulating comprehensive management plans for estuaries, contributing to the protection and sustainable development of the Yangtze River Estuary.

A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features.

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500-3700 m with slope angles of 10-30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

Predicting the Future Distribution and Habitat Suitability of Ilex latifolia Thunb. in China under Climate Change Scenarios

Ilex latifolia Thunb., a plant of significant economic and medicinal value, is both edible and medicinal. Assessing the climate suitability for I. latifolia has profound implications for advancing medical progress and enhancing the quality of human life. This study comprehensively utilized data on the field distribution of I. Latifolia, as well as corresponding climatic, topographical, and soil data at these distribution points, with the aid of future climate data predicted by global climate models, and employed the MaxEnt model to predict and analyze the climate suitability areas of I. latifolia under three greenhouse gas emission scenarios (SSP126, SSP245, and SSP585). The research covers the spatiotemporal distribution characteristics, suitable growth range, and influencing factors from the present to the end of the 21st century (2041–2100). The predictive results of the MaxEnt model indicate that, under current climatic conditions, the main suitable growth areas for I. latifolia are concentrated in the southeastern part of China, especially in the provinces of Fujian and Zhejiang. However, facing the challenges of future climate change, it is expected that the moderately high suitable growth areas for I. latifolia will show a trend of gradual reduction. The primary climatic factors crucial for I. latifolia’s growth are annual precipitation (1469.05 to 4499.50 mm), the lowest temperature in the coldest month (−18.72 to 3.88 °C), seasonal precipitation changes (11.94 to 64.69 mm), and topographic slope (0.37 to 3.00 °), with annual precipitation being the most influential. The findings of this study provide a scientific basis for the introduction of I. latifolia and offer important reference information for the artificial cultivation, resource development, and achievement of sustainable industrial development of this species.

Pemetaan Daerah Rawan Tanah Longsor di Daerah Desa Sanggang, Kecamatan Bulu, Kabupaten Sukoharjo

&lt;p&gt;Landslides are one of the frequent natural disasters occurring in various regions of Indonesia, including the Village of Sanggang in Bulu District, Sukoharjo Regency, Central Java. This area boasts diverse topography with numerous steep slopes, rendering it susceptible to landslides. The Bulu District is located in a hilly region with a high vulnerability to landslides, where from 2019 to 2020, there were recorded 11 landslide disaster events, with 10 of them occurring in the Bulu District. This study aims to map landslide-prone areas in the Village of Sanggang using a spatial analysis approach and relevant risk factors. The analytical methods include the use of topographical data, soil types, rainfall patterns, and historical landslide occurrences. The mapping results will provide a deeper understanding of locations vulnerable to landslides in this area. The outcomes of this research are expected to serve as a foundation for the development of more effective mitigation strategies, including recommendations for prevention measures and early warnings. A better understanding of these susceptible areas will assist local government authorities and stakeholders in safeguarding their communities and assets from the potential landslide hazards in the Village of Sanggang, Bulu District, Sukoharjo Regency.&lt;/p&gt;

The functional lumen imaging probe contractile response pattern is the best predictor of botulinum toxin response in esophagogastric junction outflow obstruction.

Esophagogastric junction outflow obstruction (EGJOO) is a heterogenous disorder in which the correct management strategy is unclear. We assessed whether functional lumen imaging probe (FLIP) topography data could select EGJOO, which would benefit from lower esophageal sphincter Botulinum toxin (Botox) injection. This was a single-center prospective study of adult patients meeting Chicago Classification (CC) v3.0 criteria for EGJOO. We assessed differences in pretreatment physiologic measurements on high-resolution manometry (HRM) and FLIP and other relevant clinical variables in predicting Botox response (>50% in BEDQ at 2 months). Sixty-nine patients were included (ages 33-90, 73.9% female). Of these, 42 (61%) were Botox responders. Majority of physiologic measures on HRM and FLIP and esophageal emptying were not different based on Botox response. However, a spastic-reactive (SR) FLIP contractile response (CR) pattern predicted a Botox response with OR 25.6 (CI 2.9-229.6) when compared to antegrade FLIP CR; and OR for impaired-disordered/absent CR was 22.5 (CI 2.5-206.7). Logistic regression model using backward elimination (p value = 0.0001, AUC 0.79) showed that a SRCR or IDCR/absent response and the upright IRP predicted Botox response. Response rates in tiered diagnostic groups were: (i) CCv3.0 EGJOO (60.9%), (ii) CCv4.0 EGJOO (73.1%), (iii) CCv4.0 + FLIP REO (80%), (iv) CCv4.0, FLIP REO, and abnormal FLIP CR (84.2%), and (v) CCv4.0, FLIP REO, and SR FLIP CR (90%). FLIP helps identify patients with EGJOO who are likely to response to LES Botox therapy. An abnormal FLIP contractile response pattern is the single-most important predictor of a Botox response.