Environmental risk assessment method for dense tailings ponds areas – a case study of the Yellow River Basin of Henan Province, China DOI
Han Wang, Mengshuo Liu,

Huiyuan Jiang

et al.

Human and Ecological Risk Assessment An International Journal, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22

Published: Dec. 19, 2024

Tailings ponds are the final storage site for tailings associated with mine extraction and hydrometallurgical processing, a major source of environmental risk. This study establishes comprehensive evaluation method risk in region proposes identification key factors. According to indicator scores weights, index reservoirs can be calculated, and, based on this index, level categorized into four classes(low, medium, high, super high). The highest first three indicators is recognized as factor. Subsequently, Yellow River Basin Henan Province was evaluated. gradually increases from north south. because higher total number impoundments south, excessive proximity rivers, so on. northern medium-low area centered Jiyuan southern medium-high Luanchuan have been observed. These results provide critical decision support prevention control tailing pond risks.

Language: Английский

Reimagining resources policy: Synergizing mining waste utilization for sustainable construction practices DOI Creative Commons
Haoxuan Yu, Izni Zahidi, Chow Ming Fai

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 464, P. 142795 - 142795

Published: June 5, 2024

To address the urgent need for sustainability, this paper provides a critical discussion and serves as pivotal resource stakeholders in mining construction sectors. It advocates repurposing waste into concrete aggregate, promoting eco-friendly practices. The conducts thorough review of recent developments, technological innovations, methodologies to showcase waste's potential sustainable material. Highlighting more than decade research, our analysis reveals significant environmental, economic, practical benefits, such reduced ecological footprints through minimization conservation, alongside cost-effective material alternatives. This investigation offers an in-depth look at these advantages sparks essential discussions about incorporating advanced recycling technologies conventional workflows. Promoting circular economy principles, study underscores dual gains: lessening environmental impact progressing towards efficiency. Aiming alter industry perceptions practices, work encourages shift stewardship innovation. Ultimately, aims not only disseminate knowledge but also motivate action. readers with necessary insights lead transition norms, thus establishing new benchmark addressing sustainability challenges creativity collective effort.

Language: Английский

Citations

12
TSNET: A solid waste instance segmentation model in China based on a Two-Step detection strategy and satellite remote sensing images DOI Creative Commons
Jiaqi Yu, Pan Mao, Wenfu Wu

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104366 - 104366

Published: Jan. 14, 2025

Language: Английский

Citations

0
Water Distribution Network Resilience Management Using Global Resilience Analysis-Based Index DOI Open Access

Ahmed Ismail,

Mohammod Hafizur Rahman, Md Maruf Mortula

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2353 - 2353

Published: March 7, 2025

Resilient water distribution system is crucial for sustainable urban management. Evaluating the inherent resilience of buried infrastructure key to ensuring reliable distribution. The network maintains quality and supplies sufficient users. system’s under varying failure conditions guarantee continued service delivery. This study investigates University City, Sharjah, United Arab Emirates subjected caused by pipe failure, contamination, excess demand. research quantifies corresponding performance these stressors develops an innovative index using global analysis (GRA) approach. strain in form node chlorine decay, pressure failures among all pipes throughout network. A survey was conducted with company identify recovery time designated Another experts evaluate relative significance strains contribution towards resilience. Based on index, four levels (high, moderate, low, very low) were defined. revealed Sharjah has up 40% its stress categorized as low 60% also presented a management plan improvement

Language: Английский

Citations

0
BoxRF: A New Machine Learning Algorithm for Grade Estimation DOI Creative Commons

Ishmael Anafo,

Rajive Ganguli,

Narmandakh Sarantsatsral

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4416 - 4416

Published: April 17, 2025

A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, direction, and based on inverse distance with robustness random forest (RF) methods that come forming numerous groups data. applied to a porphyry copper deposit, results were compared various ML including XGBoost (XGB), k-nearest neighbors (KNN), neural nets (NN), RF. Scikit-learn RF (SRF) performed best (R2 = 0.696) among but underperformed BoxRF 0.751). confirmed through five-fold cross-validation exercise where once again outperformed SRF. box dimensions similar in length ranges indicated by variogram modeling, thus demonstrating link between traditional methods. Numerous combinations hyperparameters similarly well, implying is robust. found better represent grade–space relationship than median values. superiority over SRF this dataset encouraging, it opens possibility improving incorporating domain knowledge (principles geology, case).

Language: Английский

Citations

0
Cross-Docking Layout Optimization in FlexSim Software Based on Cold Chain 4PL Company DOI Open Access
Augustyn Lorenc

Sustainability, Journal Year: 2024, Volume and Issue: 16(22), P. 9620 - 9620

Published: Nov. 5, 2024

The paper highlights the potential of cross-docking to reduce storage time and costs. study addresses evolving market demands that push logistics providers adopt new technologies for operational efficiency, emphasizing often-overlooked importance optimizing layouts. research, conducted in two phases, first analyzed current warehouse layout (Variant I) identify inefficiencies then designed a II) was simulated using FlexSim 2022 software. results showed significant improvements with layout, including 35% increase deliveries 3.23% reduction forklift travel distances, leading lower Even minor adjustments design proved enhance particularly during peak demand periods like holidays. demonstrates how software can be applied cold chain optimize operations, underscoring benefits cost-effective management.

Language: Английский

Citations

1
Tree species classification on images from airborne mobile mapping using ML.NET DOI Creative Commons
Maja Michałowska, Jacek Rapiński, Joanna Janicka

et al.

European Journal of Remote Sensing, Journal Year: 2023, Volume and Issue: 56(1)

Published: Nov. 7, 2023

Deep learning is a powerful tool for automating the process of recognizing and classifying objects in images. In this study, we used ML.NET, popular open-source machine framework, to develop model identifying tree species images obtained from airborne mobile mapping. These high-resolution can be create detailed maps landscape. They also analyzed processed extract information about visual features, including recognition. The deep was trained using ML.NET classify two based on combination mapping Our approach yielded impressive results, with maximum classification accuracy 93.9%. This demonstrates effectiveness combining imagery sources tools efficient accurate classification. study highlights potential framework object provide valuable insights forestry management conservation efforts. primary objective research evaluate an through generated ortho oblique captured by system.

Language: Английский

Citations

3
SMS-based Dog Detection in Residential Area using YOLOv5 and ML.Net DOI

Paolo C Galeno,

Dianne P Sale,

Engr. Melissa B Martin

et al.

Published: Dec. 16, 2023

The YOLOv5 object detection model is used in this paper to detect dogs, while the ML.Net classify dogs residential areas. A Raspberry Pi transmit live video capture be processed by machine algorithm. method allows appropriate action distinguishing between owned and stray dogs. trained on an extensive dog database analyze real-time footage taken a camera. When are detected outside their property, they categorized, SMS notifications automatically issued owners. Furthermore, alerts sent local authorities response presence of assuring protection animals community. This proactive approach tries reduce risks connected with roaming also encouraging ethical pet ownership. An optimum camera setup 1.22 meters high angle 24°, shows 87.36% accuracy maximum distance 3.05 meters. bearable latency 5 – 20 seconds observed, considering both algorithms running at same time, had average transmission time 7.92 for generation. 18.29 found ideal outpost from

Language: Английский

Citations

1
Decoding methane concentration in Alberta oil sands: A machine learning exploration DOI Creative Commons

Liubov Sysoeva,

Ilhem Bouderbala, M. Kent

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 170, P. 112835 - 112835

Published: Dec. 12, 2024

Language: Английский

Citations

0
Environmental risk assessment method for dense tailings ponds areas – a case study of the Yellow River Basin of Henan Province, China DOI
Han Wang, Mengshuo Liu,

Huiyuan Jiang

et al.

Human and Ecological Risk Assessment An International Journal, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22

Published: Dec. 19, 2024

Tailings ponds are the final storage site for tailings associated with mine extraction and hydrometallurgical processing, a major source of environmental risk. This study establishes comprehensive evaluation method risk in region proposes identification key factors. According to indicator scores weights, index reservoirs can be calculated, and, based on this index, level categorized into four classes(low, medium, high, super high). The highest first three indicators is recognized as factor. Subsequently, Yellow River Basin Henan Province was evaluated. gradually increases from north south. because higher total number impoundments south, excessive proximity rivers, so on. northern medium-low area centered Jiyuan southern medium-high Luanchuan have been observed. These results provide critical decision support prevention control tailing pond risks.

Language: Английский

Citations

0