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The noncentrosymmetric Weyl semimetal PtBi2−x (t-PtBi2−x) exhibits various interesting technologically important physical properties. We report the experimental investigation of PtBi1.6 via second harmonic generation (SHG), single-crystal x-ray diffraction, magnetic susceptibility, and electrical resistivity measurements. While bulk structural, magnetic, and electrical properties show no phase transitions below room temperature, the temperature dependence of the SHG intensity reveals two anomalies: one at T ∗ ∼ 60 K and another at Tx ∼ 200 K. Quantitative analysis indicates that the SHG signal results from both the buckled Bi1 surface termination with the 3m symmetry and flat Bi2 surface termination with the m symmetry. However, the anomalies are mainly driven by Bi1 on the surface: (1) T ∗ marks the onset of surface states which is also manifested in the c-axis resistivity drop and (2) Tx corresponds to the lowest thermal contraction of the structure and enhanced magnetic susceptibility. This study demonstrates that SHG is a powerful technique for probing surface properties even for noncentrosymmetric materials. 

Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988–2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments. 

Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally. 

Abstract Riverbank erosion is a common hazard in Bangladesh, posing a significant threat to homes, properties, and livelihoods. In recent years, the government of Bangladesh has intensified efforts to mitigate riverbank erosion by hardening shorelines, including the building of concrete revetments, but the local dynamics of these interventions are not well documented. To address this, we present results from a study of community-level response to a 2-mile long concrete revetment recently constructed in the administrative district of Ramgati, in the lower Meghna River basin of Bangladesh. Our study combines quantitative analysis of data from a household survey with qualitative data from semi-structured interviews to assess resident perceptions of the new revetment and its effect on the landscape of riverbank erosion hazard. The study finds, firstly, that hazard awareness is widespread but may be influenced by livelihood factors related to the dynamics of displacement and resettlement. Second, we find that that the negative impacts of riverbank erosion vary spatially. Hazard perception in Ramgati is significantly influenced by the physical location of the household, with those residing closer to the river and in unprotected zones north and south of the revetment expressing much greater worry that they will lose their homes, and believing that this will happen much sooner than residents further away or in the zone now protected by the embankment. As an empirically grounded case study, our findings add to the literature on riverbank erosion in Bangladesh and perception studies focused on water-based hazards in similar settings globally. 

Globally, coastal zones, rivers and riverine areas, and deltas carry enormous values for ecosystems, socio-economic, and environmental perspectives. These often highly populated areas are generally significantly different from interior hinterlands in terms of population density, economic activities, and geophysical and ecological processes. Geospatial technologies are widely used by scholars from multiple disciplines to understand the dynamic nature of shoreline changes globally. In this paper, we conduct a systematic literature review to identify and interpret research patterns and themes related to shoreline change detection from 2000 to 2021. Two databases, Web of Science and Scopus, were used to identify articles that investigate shoreline change analysis using geospatial technique such as remote sensing and GIS analysis capabilities (e.g., the Digital Shoreline Analysis System (DSAS). Between the years 2000 and 2021, we initially found 1622 articles, which were inspected for suitability, leading to a final set of 905 articles for bibliometric analysis. For systematic analysis, we used Rayyan—a web-based platform used for screening literature. For bibliometric network analysis, we used the CiteSpace, Rayyan, and VOSviewer software. The findings of this study indicate that the majority of the literature originated in the USA, followed by India. Given the importance of protecting the communities living in the riverine areas, coastal zones, and delta regions, it is necessary to ask new research questions and apply cutting-edge tools and technology, such as machine learning approach and GeoAI, to fill the research gaps on shoreline change analysis. Such approaches could include, but are not limited to, centimeter level accuracy with high-resolution satellite imagery, the use of unmanned aerial vehicles (UAV), and point cloud data for both local and global level shoreline change and analysis.