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Innuendoes of Sterilisation Drilling in Surface Mining Operations—A Case Study

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International Journal of Geosciences 2024年第5期15卷 458-477页

作者： richard Gyebuni Festus Kunkyin-Saadaari Isaac Ekow Anaman Department of Mining Engineering University of Mine and Technology Tarkwa Ghana Mining Engineering Department Ghana Manganese Company Tarkwa Ghana

Surface mining operations play a crucial role in meeting the world’s increasing demand for mineral resources for the advancement of technology and debauched expansion of economies. The search for and exploitation of these mineral resources are therefore important for the sustainability of the mineral extraction industry. To this end, efficient mine planning must incorporate sterilisation drilling and effective waste rock management principles in the search and exploitation of these minerals. In this article, sterilisation drilling is being reviewed vis-a-vis the establishment of waste and tailings dump locations, backfilling of open pit excavations and mine closure giving critical attention to the minerals and mining laws of Ghana. Subsequently, a detailed case study of a surface mining operation that successfully incorporated sterilisation drilling in determining waste dump location in its mine planning process has been presented in this study. The findings indicate that the proposed waste dump location could present a potential mining prospect in the future based on enhanced milling capacity/technology and improved mineral commodity price;underscoring the significance of sterilization drilling in the sustainability of the mining industry.

关键词： Sterilisation Exploration Backfilling Waste Dump Cutoff Grade

Biobanks and biomarkers:Their current and future role in biomedical research

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World Journal of Methodology 2024年第4期14卷 77-83页

作者： Michael Colwill Samantha Baillie richard Pollok Andrew Poullis Department of Gastroenterology St George's University Hospital NHS Foundation TrustLondon SW170QTUnited Kingdom

The importance and utility of biobanks has increased exponentially since their inception and *** used as part of translational research,they now contribute over 40%of data for all cancer research papers in the United States of America and play a crucial role in all aspects of *** classification systems exist but a simplified approach is to either classify as population-based or disease-oriented *** historically publicly funded institutions,there has been a significant increase in industry funded entities across the world which has changed the dynamic of biobanks offering new possibilities but also new *** face legal questions over data sharing and intellectual property as well as ethical and sustainability questions particularly as the world attempts to move to a low-carbon *** collaboration is required to address some of these challenges but this in itself is fraught with complexity and *** review will examine the current utility of biobanks in the modern healthcare setting as well as the current and future challenges these vital institutions face.

关键词： Biobanks Biomarkers Biomedical research Research methodology Research ethics

A Bayesian hierarchical model for the inference between metal grade with reduced variance:Case studies in porphyry Cu deposits

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Geoscience Frontiers 2024年第2期15卷 304-314页

作者： Yufu Niu Mark Lindsay Peter Coghill richard Scalzo Lequn Zhang CSIRO Mineral Resources Lucas HeightsNew South Wales 2234Australia Mineral Exploration Cooperative Research Centre Centre for Exploration TargetingSchool of Earth ScienceThe University of Western AustraliaPerthWA 6009Australia ARC centre for Data Analytics for Resources and Environments(DARE) Perth and SydneyAustralia CSIRO Mineral Resources Australian Resources Research CentreWA 6151Australia CSIRO Data61 ClaytonVIC 3168Australia

Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining *** porphyry Cu deposits,Cu is the primary target,with ores usually containing secondary‘pay’metals such as Au,Mo and gangue elements such as Fe and *** to sensing technology limitations,secondary and deleterious materials vary in correlation type and strength with Cu but cannot be detected simultaneously via magnetic resonance(MR)ore *** the relationships between Cu and other elemental abundances is particularly critical for mineral *** variations in metal grade relationships occur due to the transition into different geological *** raises two questions-how to define these geological domains and how the metal grade relationship is influenced by these geological *** this paper,linear relationship is assumed between Cu grade and other metal *** applies a Bayesian hierarchical(partial-pooling)model to quantify the linear relationships between Cu,Au,and Fe grades from geochemical bore core *** hierarchical model was compared with two other models-‘complete-pooling’model and‘nopooling’*** blocks were split based on spatial domain to construct hierarchical *** bore core data records metal grades measured from laboratory assay with spatial coordinates of sample *** case studies from different porphyry Cu deposits were used to evaluate the performance of the hierarchical *** chain Monte Carlo(MCMC)was used to sample the posterior *** results show that the Bayesian hierarchical model dramatically reduced the posterior predictive variance for metal grades regression compared to the no-pooling *** addition,the posterior inference in the hierarchical model is insensitive to the choice of *** data is wellrepresented in the posterior which indicates a robust *** results show that the spatial dom

关键词： Bayesian hierarchical model Porphyry Cu deposit Ore sorting Metal grade Linear regression

Chemical Determination of Base Status Metals in Soil Sediments and Particulate Matter in Wellington Industrial Estate Location

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Open Journal of Applied Sciences 2024年第5期14卷 1338-1352页

作者： richard Tamba Simbo Jerry Yeke Paye Fayia Francis Nyuma Department of Chemistry School of Basic Sciences Njala University Freetown Sierra Leone Department of Science and Mathematics College of Arts and Sciences William V.S. Tubman University Harper Liberia Department of Fisheries and Science Thomas J.R Faulkner College of Science and Technology University of Liberia Monrovia Liberia

This research study explored the levels of base status metals in soil sediments and particulate matter in the wellington industrial estate location;the main objectives were to: 1) determine sodium and potassium, 2) determine calcium and magnesium, 3) determine available iron. The following hypotheses were put forward;H0a: there is no significant difference in the concentration levels between Ca and Mg in the study area, H1a: there is significant difference in the concentration levels between Ca and Mg in the study area, H0b: there is no significant difference in the concentration levels between Na and K in the study area, H1b: there is significant difference in the concentration levels between Na and K in the study area. Six locations were used to collect samples with the aid of scoop and gravel free auger (at varying depths of 0 - 5 cm and 5 - 10 cm) which are Wellington Industrial Estate Area 1 (WIEL 1), (WIEL 2), (WIEL 3), (WIEL 4), (WIEL 5), (WIEL 6);the samples were given laboratory treatment. Flame photometer, EDTA, and Spectrophotometer were used in the determinations of sodium and potassium, calcium and magnesium, and available iron respectively. The results indicated that levels of potassium were in medium range (moderately high);sodium levels were generally low when compared to Brook’s classification table. Levels of calcium were generally low and those of magnesium were moderate based on Brook’s table of classification. Levels of available iron which fall within the range of Quijano-Guerta (2003) were high;this implies such levels can lead to toxicity. In all locations, there was decrease in the levels of each metal in the samples with (5 - 10 cm) depth.

关键词： Deposition Environment Particulate Sediment Toxicity

Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach

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Journal of Data Analysis and Information Processing 2024年第2期12卷 163-188页

作者： Ayoub Djama Waberi Ronald Waweru Mwangi richard Maina Rimiru Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Computing Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya School of Computing and Information Technology (SCIT) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

关键词： LSTM XGBoost Hybrid Models Machine Learning. Deep Learning

Multifaceted signaling regulators of chondrogenesis:Implications in cartilage regeneration and tissue engineering

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Genes & Diseases 2015年第4期2卷 307-327页

作者： Jordan D.Green Viktor Tollemar Mark Dougherty Zhengjian Yan Liangjun Yin Jixing Ye Zachary Collier Maryam K.Mohammed Rex C.Haydon Hue H.Luu richard kang Michael J.Lee Sherwin H.Ho Tong-Chuan He Lewis L.Shi Aravind Athiviraham The University of Chicago Pritzker School of Medicine ChicagoIL 60637USA Department of Orthopaedic Surgery and Rehabilitation Medicine The University of Chicago Medical CenterChicagoIL 60637USA Department of Orthopaedic Surgery The Second Affiliated Hospital of Chongqing Medical UniversityChongqing 400016China School of Bioengineering Chongqing UniversityChongqingChina

Defects of articular cartilage present a unique clinical challenge due to its poor self-healing capacity and avascular *** surgical treatment options do not ensure consistent regeneration of hyaline cartilage in favor of fibrous ***,we review the current understanding of the most important biological regulators of chondrogenesis and their interactions,to provide insight into potential applications for cartilage tissue *** include various signaling pathways,including fibroblast growth factors(FGFs),transforming growth factor b(TGF-b)/bone morphogenic proteins(BMPs),Wnt/b-catenin,Hedgehog,Notch,hypoxia,and angiogenic signaling *** and epigenetic regulation of chondrogenesis will also be *** in our understanding of these signaling pathways have led to promising advances in cartilage regeneration and tissue engineering.

关键词： BMPs Cartilage Cell signaling Chondrogenesis FGF Regenerative medicine

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基于卷积神经网络预测南海海底地形

大地测量与地球动力学 2024年第3期44卷 287-292页

作者：王怀兵万晓云 richard Fiifi Annan 中国地质大学(北京)土地科学技术学院北京市100083

针对南海区域,使用3种重力信号(垂线偏差、重力异常、垂直重力梯度异常)训练卷积神经网络模型,并将预测结果与船测数据和国外模型结果进行对比分析。将3种重力信号分成4组数据:重力异常,重力异常与垂直重力梯度异常,重力异常与垂线偏差... 详细信息

针对南海区域,使用3种重力信号(垂线偏差、重力异常、垂直重力梯度异常)训练卷积神经网络模型,并将预测结果与船测数据和国外模型结果进行对比分析。将3种重力信号分成4组数据:重力异常,重力异常与垂直重力梯度异常,重力异常与垂线偏差,以及重力异常、垂线偏差和垂直重力梯度异常。4种组合方式的反演结果与船测水深之间的标准差分别为104.780 m、102.778 m、93.788 m、88.289 m,表明随着不同类型重力数据的加入,水深预测精度明显提高,并且在深度大于2000 m时,反演结果精度提升效果更为显著。将训练集占总数据集的比例分别设置为80%、70%、60%和50%,反演结果与船测水深之间的标准差分别为88.289 m、91.256 m、92.833 m、96.022 m,表明数据量的增多可以有效提高模型学习结果的精度。

关键词：卷积神经网络重力场信息深度学习海底地形频域法

Investigating environmental quality among G20 nations: The impacts of environmental goods and low-carbon technologies in mitigating environmental degradation

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Geoscience Frontiers 2024年第1期15卷 84-96页

作者： Ernest Baba Ali Samira Shayanmehr Riza Radmehr richard Bayitse Ebenezer Agbozo Department of Economics University for Development StudiesTamale P.O.Box TL1350Ghana Department of Agricultural Economics Ferdowsi University of MashhadIran Council for Scientific and Industrial Research Institute of Industrial ResearchAccra P.O.Box LG 576Ghana Department of Big Data Analytics and Methods of Video Analysis Ural Federal University19 Mira Str60002 EkaterinburgRussia

Environmental goods and low-carbon technologies have long been identified as having the potential to drive long-term economic progress without compromising environmental quality. However, their exact role in mitigating environmental degradation are yet to be unravelled. In addressing this shortfall, the extant literature relied on research funding and patent application as proxies for green technologies. Having established the weaknesses in the use of these variables as proxies for green technologies, this study explored the role of trade in environmental goods and low-carbon technologies in boosting environmental quality among G20 nation using a panel dataset from 1994 to 2018. The study employed the Method of Moment quantile regression for the model estimation and the Ridge regression, Discroll-Kraay standard error, and the Newey-West standard error estimators to test the robustness of our findings. Our findings indicate that whereas environmental goods promote environmental quality, low-carbon technologies decrease same. Also, the study found economic growth to exert an aggravating effect on environmental quality, while foreign direct investments, natural resource rents, human capital development, and renewable energy consumption exert positive influence on environmental quality. Based on the findings of the study, G20 nations are encouraged to improve green market structures to improve the trade in environmental goods and low-carbon technologies. Also the share of renewable energy sources in the overall energy basket must be improved to help improve environmental quality.

关键词： Environmental quality Environmental goods Low-carbon technology G20 nations

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

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Journal of Geographic Information System 2024年第2期16卷 115-135页

作者： Maria Assumpta Komugabe richard Caballero Itamar Shabtai Simon Peter Musinguzi Center for Information Systems & Technology Claremont Graduate University Claremont California USA Department of Information Technology Faculty of Business and ICT University of Kisubi Entebbe Uganda Faculty of Health Sciences University of Kisubi Entebbe Uganda Department of Agriculture Production Faculty of Agriculture Kyambogo University Kyambogo Uganda Department of Agriculture and Natural Sciences Faculty of Agriculture Uganda Martyrs University Kampala Uganda

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

关键词： Malaria Predictive Modeling Geospatial Analysis Climate Factors Preventive Measures