Convection in colloidal suspensions with particle-concentration-dependent viscosity

The onset of thermal convection in a horizontal layer of a colloidal suspension is investigated in terms of a continuum model for binary-fluid mixtures where the viscosity depends on the local concentration of colloidal particles. With an increasing difference between the viscosity at the warmer and the colder boundary the threshold of convection is reduced in the range of positive values of the separation ratio psi with the onset of stationary convection as well as in the range of negative values of psi with an oscillatory Hopf bifurcation. Additionally the convection rolls are shifted downwards with respect to the center of the horizontal layer for stationary convection (psi>0) and upwards for the Hopf bifurcation (psi<0).Comment: 8 pages, 6 figures, submitted to European Physical Journal

E-Learning approach in Teacher Education

There has been an increasing interest in e-learning in teacher training at universities during the last ten years. With the developing technology, educational methods have differed as well as many other processes. Firstly, a definition on e-learning as a new approach should be given. E-learning could shortly be defined as a web-based educational system on platform with Internet, Intranet or computer access. In this model, the lessons planned were simulations and software’s for students on polymers and metals. Nine experiments were designed on the topic. Students were interviewed and administered laboratory attitude scales at the end of the experiments.  The study concluded that the experiments in the new model were appropriate to teacher training programs and could successfully be administered to large groups

Sustainable maize production through organic amendments: Evaluating growth performance and environmental impact

The current study investigates the impact of organic and inorganic fertilizers on maize (Zea mays L.) growth and greenhouse gas (GHG) emissions. Organic amendments such as farmyard manure (FYM) and composted press mud, as well as inorganic fertilizers, were applied across various treatments to evaluate their effects on plant height, leaf production, chlorophyll content (SPAD values), leaf area index (LAI) and GHG emissions and carbon dioxide (CO?). The experiment was conducted for one crop season (September to December 2023) using a Factorial Randomized Block Design (FRBD) in Tamil Nadu, with static chamber methods employed to measure GHG emissions. The results demonstrated that treatments involving organic inputs significantly enhanced maize growth compared to inorganic fertilizers. N9 (T3 + 5 t Composted Pressmud) consistently recorded the highest plant height, leaf count and LAI, while the control (T1) had the lowest values. Organic amendments also showed reduced GHG emissions under rain-saturated conditions, although methane emissions were higher due to the anaerobic decomposition of organic matter. The study concludes that integrating organic fertilizers improves soil health and crop productivity while reducing GHG emissions, but careful management is needed to mitigate methane emissions in wet conditions. These findings support the adoption of organic inputs as part of sustainable agricultural practices to enhance productivity and environmental outcomes

Predictive soil mapping using random forest models: Applications in pH and soil organic matter assessment

Digital Soil Mapping (DSM) presents a highly scalable and efficient alternative to traditional soil analysis, which is typically limited by its labor-intensive processes, time constraints and low spatial resolution. By utilizing advanced computational techniques such as machine learning and remote sensing, DSM overcomes these limitations and improves the accuracy, efficiency and scalability of soil property assessments. This study, conducted across Tamil Nadu, India, applied DSM and Random Forest (RF) models to predict 2 key soil properties: pH and Soil Organic Matter (SOM). We employed Conditioned Latin Hypercube Sampling (cLHS) for optimized sampling point selection and utilized the Boruta algorithm to identify the most relevant covariates for accurate modeling. The RF models were fine-tuned using a comprehensive grid search, with the optimal configuration spanning from 500 to 2000 trees (ntree) and mtry from 1 to 11. The best-performing model was found with 2000 trees and mtry set to 1 yielding superior prediction for SOM and pH with Root Mean Square Error (RMSE) values of 0.71 and 0.60 respectively, showcasing a high level of predictive accuracy. Our findings emphasize the critical role that remote sensing indices play in predicting SOM, while pH was influenced by both terrain features and remote sensing data. In comparison to previous studies, this research offers novel improvements in both sampling optimization and model configuration, leading to enhanced predictive performance. These results hold significant potential for sustainable land-use planning, agricultural productivity and environmental management

Direct Numerical Simulation of Transverse Ripples: 2. Self-Similarity, Bedform Coarsening, and Effect of Neighboring Structures

Coupled bed-flow direct numerical simulations investigating the early stages of pattern formation and bedform (ripple) interactions were examined in a previous paper (Part 1), making use of the resolved flow field. In this paper (Part 2), we compare our results to published experimental data and provide an extensive quantitative analysis of the bed using spectral analysis and two-point correlations. The effect of the mobile rippled bed on the flow structure and turbulence is investigated locally (at specific streamwise locations) and over the entire computational domain. We show that developing ripples attain a self-similar profile in both the shape and the corresponding bed shear stress. We demonstrate the importance of neighboring structures, especially upstream neighbors, on bedform dynamics in terms of the growth, decay, and speed of ripples. Finally, we examine the defect-free interactions in the later stages of bed evolution, which primarily lead to wave coarsening. Key Points Isolated ripples maintain a self-similar shape and bed shear stress profiles Bedform-bedform interactions can significantly modify bedform celerity Spectra of bed height variation suggest a Reynolds number dependenc

Direct Numerical Simulation of Transverse Ripples: 1. Pattern Initiation and Bedform Interactions

We present results of coupled direct numerical simulations between flow and a deformable bed in a horizontally periodic, turbulent open channel at a shear Reynolds number of Reτ = 180. The feedback between the temporally and spatially evolving bed and the flow is enforced via the immersed boundary method. Using the near-bed flow field, we provide evidence on the role of locally intense near-bed vortical structures during the early stages of bed formation, from the emergence of quasi-streamwise streaks to the formation of incipient bedform crestlines. Additionally, we take a new look at a number of defect-related bedform interactions, including lateral linking, defect and bedform repulsion, merging, and defect creation, and show that the underlying mechanisms, in these flow-aligned interactions, are very similar to each other. Consequently, the interactions are labeled differently depending on the geometry of interacting structures and the outcome of the interaction. In the companion paper, we compare our results to published experimental data and provide an extensive quantitative analysis of the bed, where we demonstrate the importance of neighboring structures, especially upstream neighbors, on bedform dynamics (growth/decay and speed) and wave coarsening. Video files of bed evolution are available in the supporting information. Key Points Mesoscale resolved simulations show the different mechanisms for bedform-bedform interactions to be very similar to each other Similar to laminar flows over dunes and ripples, a positive phase shift is observed between bed shear stress and topology even in mesoscale-resolved turbulent flow field Simulations match Coleman and Melville (1996) theory on bedform initiation from a flat be

Synthesis and characterization of nano phosphorus fertilizer from rock phosphate

Phosphorus is a vital macronutrient required for the growth and development of plants. The major concern regarding phosphorus (P) is low availability. Fertilizers are generally supplied to increase the crop growth. Rock phosphate(RP) is mainly used as the precursor for synthesizing phosphatic fertilizers. The applied phosphatic fertilizers are usually fixed in the soil and the excess fertilizers result in eutrophication and pollute the water bodies. To address these challenges nanofertilizer technology was created. In the present study, nano phosphorus fertilizer was developed using P-solubilizing bacteria (Bacillus megaterium) from RP. The nano RP was characterized using particle size analysis (PSA), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscope (SEM). The size of nano RP using a particle size analyzer was ~450 nm with a polydispersity index of 0.803. The FTIR spectra of RP show the presence of phosphate minerals, whereas some peaks of RP were altered after bios olubilization of RP. The XRD pattern indicated the presence of apatite and calcite and the number of peaks of nano RP was 13, while RP has 25 diffraction peaks. The scanning electron microscope image of nano RP indicated the reduction in the crystalinity of RP

The AI-viticulture nexus: Robotics and precision technologies for sustainable vineyards

Automation technologies, such as Artificial Intelligence (AI), robotics, IoT and remote sensing, are transforming viticulture by addressing labour shortages, climate resilience challenges and resource optimization. AI-driven machine learning models process data from multispectral drones and IoT sensors to monitor soil health, water stress and canopy dynamics, enabling precision agriculture practices like targeted irrigation and nutrient delivery. Autonomous robotic systems perform tasks such as selective harvesting, pruning and pest management, enhancing operational efficiency while reducing manual labour. IoT networks provide real-time insights into microclimatic conditions, empowering growers to adopt climate-smart strategies that minimize chemical inputs and improve yield stability. Despite progress, key barriers persist: AI models require terroir-specific adaptation, fragmented datasets hinder interoperability and field validation of autonomous systems under diverse conditions remains limited. Future research must prioritize accessible solutions: low-cost sensor networks for smallholders, adaptive AI frameworks for climate volatility (e.g., drought or flood prediction) and edge computing for real-time analytics. Ethical concerns data privacy, algorithmic bias and technology access disparities demand inclusive governance. Additionally, user-friendly interfaces are essential for broad adoption. Addressing these gaps will unlock automation’s full potential in advancing sustainable viticulture: optimizing water/energy use, reducing agrochemical reliance, enhancing biodiversity and ensuring economic resilience for growers. Ultimately, integrated automation promises a balance between ecological stewardship, resource efficiency and sector-wide viability in a climate-constrained future