Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

Currently, coal resource-based cities (CRBCs) are facing challenges such as ecological destruction, resource exhaustion, and disordered urban development. By analyzing the landscape pattern, the understanding of urban land use can be clarified, and optimization strategies can be proposed for urban transformation and sustainable development. In this study, based on the interpretation of remote sensing data for three dates, the landscape pattern changes in the urban area of Huainan City, a typical coal resource-based city in Anhui Province, China were empirically investigated. The results indicate that: (1) There is a significant spatial-temporal transformation of land use, with construction land gradually replacing arable land as the dominant land use type in the region. (2) Landscape indices are helpful to reveal the characteristics of land transfer and distribution of human activities during a process. At the landscape type level, construction land, grassland, and water bodies are increasingly affected by human activities. At the landscape composition level, the number of landscape types increases, and the distribution of different types of patches becomes more balanced. In addition, to address the problems caused by the coal mining subsidence areas in Huainan city, three landscape pattern optimization strategies are proposed at both macro and micro levels. The research findings contribute to a better understanding of land use changes and their driving forces, and offer valuable alternatives for ecological environment optimization.

Using the unified theory of acceptance and use of technology (UTAUT), this study investigated the effect of perceived usefulness, perceived ease of use, social influence, facilitating condition, lifestyle compatibility, and perceived trust on both the intention to use and adoption of an e-wallet among adults. This quantitative study employed a cross-sectional research technique to collect data from 501 respondents via Google Form. The acquired data was assessed using partial least squares structural equation modelling (PLS-SEM). Therefore, perceived usefulness, perceived simplicity of use, social influence, lifestyle compatibility, and perceived trust all had a strong positive impact on both intentions to use and adoption of an e-wallet. This study demonstrated that the intention to use an e-wallet mediated the links between predictors and e-wallet adoption. Respondents’ age and gender moderated the effect of lifestyle compatibility on their intention to use an e-wallet. The study’s findings can assist managers and policymakers establish successful ways that capture customers’ intention to use and experience with employing an e-wallet amid a tumultuous market. Finally, such well-crafted policies may stimulate the digital platform and web-based apps, as well as raise e-wallet acceptance rates in undeveloped countries.

Local scour, a complex phenomenon in river flows around piers with movable beds, can damage bridge piers during high floods. Predicting scour depth accurately is vital for safety and economic reasons, especially for large bridges. This study using hydraulic flume laboratory experiments compared diamond, square, and elliptical pier models of different sizes under steady clear-water conditions considering different flow rates and discharge levels to identify the most efficient shape with less local scour. Local scour, a complex phenomenon in three-dimensional flow around piers in rivers with movable beds, can lead to detrimental effects on bridge piers due to high flood velocities. Accurate prediction of scour depth is crucial for economic and safety reasons, especially for large bridges with complex piers. Hydraulic engineers are keen on forecasting the equilibrium scour depth. To achieve this, laboratory testing compared diamond, square, and elliptical pier models under steady clear-water conditions to identify the most efficient pier shape with less local scour. This research provides valuable insights for optimizing pier design to enhance bridge stability and resilience against scour-induced risks. A variety of configurations, including different sizes and shapes of piers were experimented with in the flume using diamond, square, and elliptical shapes. The test results showed that the local scour depth around elliptical piers was around 29.16% less, and around diamond piers, it was approximately 16.05% less compared to the scour depth observed around square piers with the same dimensions. The researchers also observed distinct patterns of scouring around different pier shapes. Specifically, the square-shaped piers displayed the highest level of scouring depth, that is, 48 mm, followed by the diamond-shaped pier which experienced a scouring depth of 48 mm while the elliptical-shaped piers experienced the least amount of scouring depth, that is, 34 mm. The test results also demonstrated that pier size significantly influences scouring, with an increase in pier size from 3 × 3 cm2 to 5 × 5 cm2 leading to a rise in scour depth by 26.04%. Moreover, this study findings also elucidated that an increase in flow results in an increase of in scouring depth i.e., elevating the discharge from 0.0026 cumecs to 0.0029 cumecs led to a 28.13% increase in scouring depth for the identical pier size. These findings provide valuable insights into the hydraulic behavior of various pier shapes and can aid in the optimization of bridge design and hydraulic engineering practices. The investigations further revealed that local scouring is sensitive not only to pier dimensions but also to other critical parameters, including flow rate, time of exposure, and the size of a pier.