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Analysis of soil properties and wheat yield in relation to climate smart agricultural practices in cultivated landscape of Bona Dibero, central Ethiopia
Modelling, implementation and analysis of double-side slotted axial flux PMGs suitable to small-scale wind energy conversion systems
Evaluation of comprehensive vitality of Shanghai’s commercial centers using multi-dimensional geospatial big data
Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms
Abstract Wheat lodging is a recurrent phenomenon that significantly affects grain yield and impedes the harvesting efficiency. Therefore, the precise and rapid assessment of wheat lodging is crucial in minimizing its impact on grain yield and quality. Recently few studies related to machine learning based wheat lodging have been reported; however, the literature still lacks comprehensive assessments of machine learning algorithms for wheat lodging over Indian agricultural fields. This study presented a systematic approach for detecting the wheat lodging occurred during the end of March and April 2023 in the Ludhiana district of Punjab (India) from multi-temporal Sentinel-2 data using the machine learning algorithms. The ground control points for healthy and lodged areas were collected during March and April 2023. The temporal characteristics of crop phenology from November 2022 to April 2023 were analyzed for wheat classification. The normalized difference vegetation index (NDVI) was computed during this period followed by implementation of random forest (RF), decision tree (DT), and support vector machine (SVM) algorithms to evaluate their performance for wheat classification. It was found that RF outperformed the other models in terms of prediction accuracy and wheat area extraction. To distinguish between lodged and non-lodged wheat, eight spectral indices were computed from the visible and infrared bands of Sentinel-2. These indices were used as inputs to RF, DT, and SVM models. The optimal set of features were identified using random forest feature importance selection approach. Among the spectral indices, spectral sum index (SSI) derived from blue, green, red, and near-infrared bands followed by generalized difference vegetation index (GDVI) accurately separated lodged wheat from non-lodged wheat. Among the three algorithms, the RF model combined with SSI and GDVI achieved the highest overall accuracy of 89.2%. These results suggested that SSI and GDVI derived from Sentinel-2 data coupled with random forest model is effective for assessing the wheat lodging on spatio-temporal scale which may be helpful for developing the decision support system to assess the loss of crop yield loss.
Enhancing sustainability in mining by reducing hauling energy consumption through optimization of distance and slope with semi-mobile in-pit crushers and conveyors
Enhancing chronic wound assessment through agreement analysis and tissue segmentation
Effects and differences of various concentration techniques on the evolution of therapeutic components in Lushan geothermal water
DFT-based insights into novel Ga6N6 nanoring for pollutant gas adsorption: energetics and electronic modulations
Biosynthesis of fatty aldehydes and alcohols in the eye and their role in meibogenesis
A simple preprocessing approach for improving semantic segmentation in unsupervised domain adaptation
Simulation and experiment research on heat treatment of micro and nano BN particles modified casting aluminum copper alloys
A virtual reality game for older adults’ immersive learning
Abstract This article presents a feasibility study that assessed the effectiveness of a virtual reality (VR) game in training older adults’ cognitive and independent living skills. A home-like virtual environment, comprising six mini-games, each designed to target a specific type of learning, was created to enhance the gamified experience. A qualitative approach was used to evaluate the feasibility of the gamified approach, involving semi-structured interviews with older adults (N = 30) who had played the VR game. Findings revealed positive feedback on the VR game’s effectiveness in facilitating learning among older adults, including ease of use and perceived usefulness which informed their acceptance of the technology, as well as the competency and cognitive developments afforded by the VR game. The gaming experience also offers varying degrees of stimulation and engagement, although some negative experiences, such as cybersickness and anxiety, were identified that may require further attention. The study’s findings offer insights into the feasibility of employing digital game-based learning for older adults within an immersive learning environment and provide best practices for designing VR games that promote the development of cognitive and independent living skills.
Characterisation of gut microbiota in Malaysian cancer patients using V3-V4 region of 16S rRNA gene sequencing
Abstract Recent studies suggested a potential connection between gut microbiota changes and cancer onset. However, conflicting results make it challenging to understand the role of gut microbiota dysbiosis in cancer, particularly in underrepresented populations like those in Southeast Asia. To address this gap, we analysed the diversity and composition of gut microbiota in 65 faecal samples, which included 48 from cancer patients with various malignancies and 17 from healthy controls. Patients were categorised into four groups: symptomatic patients undergoing cancer treatment, asymptomatic pre-treatment and during cancer treatment, and healthy controls. Genomic DNA was extracted, and the V3-V4 region of the 16 S rRNA gene was sequenced. Our findings revealed significant differences in the alpha diversity ( p ≤ 0.05) between cancer patients and controls. Asymptomatic patients under treatment showed slightly lower alpha diversity than pre-treatment patients, but this difference was not statistically significant ( p = 0.06). We identified 13 genera with over 20% difference in abundance between patient groups and controls. Asymptomatic patients receiving treatment and pre-treatment patients exhibited enrichment in Enterococcus , whereas Prevotella , Faecalibacterium , Brevundimonas , and Pseudomonas were significantly reduced compared to controls. Symptomatic patients had higher levels of Enterococcus and Staphylococcus , while Ruminococcus was enriched in asymptomatic patients. These underscore the distinct differences in gut microbiota composition between cancer patients and healthy individuals, particularly in symptomatic cases with potential biomarkers such as Enterococcus , Prevotella , and Faecalibacterium . Our study suggests that cancer treatment may not significantly alter the gut profile of cancer patients. Further research is needed to comprehend the implications of these findings fully.