Soil pollution represents a significant global environmental challenge driven by industrial activities, agricultural practices, and urban development, which collectively contribute to the accumulation of heavy metals and organic contaminants.Current estimates indicate that approximately 52 million hectares of land worldwide are affected by heavy metal contamination, resulting in decreased agricultural productivity and posing serious risks to human health.Traditional methods for soil treatment often incur high costs and pose the risk of secondary pollution, underscoring the necessity for sustainable and efficient remediation technologies.This review examines recent advancements in biochar-based remediation of soil pollution, emphasizing biochar preparation, modification techniques, pollutant removal mechanisms, and the impacts on soil properties and microbial communities.Biochar has shown significant potential in immobilizing heavy metals through various processes, including adsorption, ion exchange, and precipitation.Modified biochar enhances adsorption efficiency.Research has demonstrated that alkali-modified biochar significantly reduces cadmium bioavailability by more than 50%, while nano-enhanced biochar demonstrates high efficiency in degrading organic pollutants such as PNP and OTC.Future research should prioritize large-scale implementation, advanced modification strategies, and comprehensive risk assessments to optimize biochar applications in sustainable soil remediation efforts.

Fused deposition modeling (FDM) technology is widely adopted in production and typically uses 0.4 mm nozzles for precision printing, but there is little research on large-bore nozzles.Therefore, this paper designs a nozzle with a nozzle diameter (d) of 3 mm, and uses orthogonal design combined

This study introduces a new approach for identifying hydraulic resistance coefficients (roughness, minor losses, and friction factor) in pipeline network sections with intermediate water withdrawals or injections, using only endpoint measurements.The approach combines the Colebrook-White equation for turbulent flow modeling with stochastic methods to address parameter variability and uses regression analysis to derive equivalent friction factor relationships, utilizing only endpoint pressure, flow rate, and temperature measurements.Analytical formulas were developed to determine resistance coefficients without solving complex nonlinear systems.The method's efficacy was validated through application to the Astrakhan-Mangyshlak pipeline (Kazakhstan), demonstrating accurate parameter estimation under real-world conditions.The developed approach enables accurate identification of resistance coefficients through simplified endpoint-only measurements, providing a computationally efficient alternative to traditional methods that can be directly applied to operational diagnostics in large-scale pipeline networks with limited sensor coverage. Table of Contents Innovative Description:An approach for identifying pipeline resistance coefficients using end-point data, enabling precise hydraulic simulations in networks with intermediate withdrawals/injections

Digital Soil Mapping involves using digital techniques to create detailed maps of soil properties across landscapes, utilizing data from remote sensing, soil sampling, and environmental variables.It is essential for effective land management, precision agriculture, and environmental monitoring, providing insights into soil characteristics such as texture, moisture, and nutrient content.However, challenges faced by existing models include inconsistent data quality and availability, the spatial variability of soil properties, difficulties in integrating multisource data, temporal dynamics of soil characteristics, computational complexity, and issues with user accessibility, which can hinder effective implementation and adoption.Hence to address the existing challenges, this research introduces the Explainable Selective Group Enhance Attention -Distributed Deep Convolutional Sequential Network (X-SGEA-D2CSN) for DSM and Crop Recommendation.The proposed approach effectively addresses the DSM issues by meticulously capturing the subtle soil features and environmental patterns while filtering out irrelevant noise, ensuring better crop recommendation.Specifically, the proposed model effectively captures the complex patterns and long-term temporal dependencies associated with the soil properties crucial in understanding the dynamic agricultural environments.The experimental results demonstrate that the X-SGEA-D2CSN model achieves better performance reporting ahigh accuracy of 94.17%, precision of 95.24%, and recall of 93.1% for 90% training due to the multi-scale feature learning with attention-based refinement, and optimizing the selection of relevant features.

Pile-driving operations in construction generate intense pulsed noise, creating safety problems at the construction site and potentially causing concern in nearby communities.This study presents field measurements of sound pressure levels (SPL) from active pile driving at various distances, modeling decay using a logarithmic attenuation function and integrating the results with geospatial visualization.The measured time history of the SPL showed periodic fluctuations (amplitude ~17 dB) corresponding to the hammer impact cycle.By applying a logarithmic model (with distances calculated using the Haversin equation) to each measurement interval, we obtained attenuation coefficients that provided a high degree of confidence (R 2 > 0.8 in 77.5% of cases; the maximum value is 0.97).The model's predictions corresponded to the observed SPL values with an average error of about 2.6% and minor deviations (underestimation over medium distances).A noise map was compiled throughout the site, and 3D visualization was performed, identifying areas exceeding 85 dB near the source that require hearing protection and showing a decrease in SPL to safe levels (~55 dB) at the site boundary.These results demonstrate a novel integration of empirical measurements with logarithmic modeling and geospatial mapping to support construction noise assessment and protective zone planning.

Ultrasound technology is swiftly progressing and extensively utilized to optimize production operations, elevate product quality, and guarantee food safety.Low-frequency ultrasound alters physical qualities, whereas high-frequency ultrasound serves as an analytical instrument to evaluate the physicochemical characteristics of food, including acidity, hardness, sugar concentration, and maturity.High-frequency ultrasound produces pressure, shear, and temperature variations, resulting in cavitation effects that suppress microbial proliferation in food.Ultrasonic waves generate air bubbles and micro-vibrations in liquid mediums, hence improving mass and energy transfer.This is advantageous in processes such as freezing, cooling, and thawing by minimizing quality deterioration.This study examines the impact of ultrasonic waves at varying frequencies on the mineral composition of agricultural products.The findings underscore the influence of ultrasonic frequency on the physical and chemical properties of food, suggesting possible applications for the enhancement of food processing methods.The experimental results confirm that ultrasound technology is a feasible method to improve food quality and safety while maintaining nutritional integrity.

Uneven class distribution in medical data sets such as stroke data, presents a substantial challenge to the classification model, resulting in suboptimal performance and a biased model toward the majority class.This research analyzes the potential of GAN-based data augmentation over traditional data augmentation methods on a 2-dimensional Convolutional Neural Network to improve sampling.GAN-based data augmentation is used to effectively explore the solution space, and these methods generate synthetic samples to balance the class distribution.The integration of these approaches aims to improve the model's robustness and predictability.Experimental analysis shows promising results in classification accuracy, showing the potential of the GAN-based data Augmentation techniques on the proposed CNN model for dealing with uneven class distribution.Deep Convolutional -Generative Adversarial Network (DCGAN), along with Image-Based ISMOTified-GAN (iSMOTified GAN) and Image-Based Markov Chain Monte Carlo (iMCMC-GAN), is compared to classic data augmentation techniques.The positive results demonstrate the potential of GAN-based data augmentation methods as viable approaches to resolving the issues associated with imbalanced datasets.While all augmentation techniques showed improvements in classification accuracy, DCGAN performed the best, achieving an accuracy of 98.01%.The results of this study provide valuable insights into the potential of GAN-generated synthetic data in enhancing classification, addressing both data limitations and privacy concerns.