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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

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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

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Modeling Solid Waste Minimization Performance at Source in Dar es Salaam City, Tanzania

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Journal of Geoscience and Environment Protection 2024年第9期12卷 17-32页

作者： Abdon Salim Mapunda richard Joseph Kimwaga Shaaban Ally Kassuwi Department of Integrated Water Resources Engineering University of Dar es Salaam Dar es Sa-laam Tanzania Department of Molecular Biology and Biotechnology University of Dar es Salaam Dar es Salaam Tanzania

Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective management performance of municipal solid waste management underscores the interdisciplinarity strategies. Such knowledge and skills are paramount to uncover the sources of waste generation as well as means of waste storage, collection, recycling, transportation, handling/treatment, disposal, and monitoring. This study was conducted in Dar es Salaam city. Driven by the curiosity model of the solid waste minimization performance at source, study data was collected using focus group discussion techniques to ward-level local government officers, which was triangulated with literature and documentary review. The main themes of the FGD were situational factors (SFA) and local government by-laws (LGBY). In the FGD session, sub-themes of SFA tricked to understand how MSW minimization is related to the presence and effect of services such as land use planning, availability of landfills, solid waste transfer stations, material recovery facilities, incinerators, solid waste collection bins, solid waste trucks, solid waste management budget and solid waste collection agents. Similarly, FGD on LGBY was extended by sub-themes such as contents of the by-law, community awareness of the by-law, and by-law enforcement mechanisms. While data preparation applied an analytical hierarchy process, data analysis applied an ordinary least square (OLS) regression model for sub-criteria that explain SFA and LGBY;and OLS standard residues as variables into geographically weighted regression with a resolution of 241 × 241 meter in ArcMap v10.5. Results showed that situational factors and local government by-laws have a strong relationship with the rate of minimizing solid waste dumping in water bodies (local R square = 0.94).

关键词： Modeling Solid Waste Minimization Dar es Salaam City Ordinary Least Square (OLS) Regression Model Situation Factors Local Government by Laws

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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

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基于卷积神经网络预测南海海底地形

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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

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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

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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

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Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

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Open Journal of Applied Sciences 2024年第8期14卷 2187-2200页

作者： Roch Corneille Ngoubou Basile Guy richard Bossoto Régis Babindamana Faculty of Science and Technology of Marien Ngouabi University Brazzaville Republic of The Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s appr

关键词： MERISE Conceptual Data Model (MCD) Monitoring Indicators Evaluation of Training Effectiveness Training-Employment Adequacy Labor Market Information Systems Analysis Adjustment of Training Programs Employability Professional Skills

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Dual-Fields Rotational Total Skin Electron Therapy: Investigation and Implementation

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International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 2024年第1期13卷 1-15页

作者： M. Ming Xu Iris Rusu richard P. Garza Northwestern Medicine Cancer Center Warrenville and Geneva Illinois USA Department of Radiation Oncology Loyola University Medical Center Maywood Illinois USA The Austin Center for Radiation Oncology Austin Texas USA

Purpose: To present a protocol of a dual-field rotational (DFR) total skin electron therapy (TSET) and to provide an assessment of clinical implementation, dosimetry properties, and skin dose evaluation. Methods and Materials: The DFR-TSET combined the Stanford 6-field and McGill rotational methods. Dual 6 MeV electron beams in high dose total skin electron mode were used for DFR-TSET on a commercial linac. Beam profiles and dosimetric properties were measured using solid phantoms. The dose rate at expanded source-to-surface distance (SSD) was a combination of static rate and rotational rate. In vivo dosimetry of patient skin was performed on patients’ skin using film, metal oxide semiconductor field-effect transistors (MOSFET), and optically stimulated luminescent dosimeters (OSLD). Results: Dual field rotational total skin electron therapy exhibited good (≤±10%) uniformity in the beam profiles in the vertical direction at an extended SSD of 332 cm with a gantry angulation of ±20˚ deviated from the horizontal direction. In-vivo measurements confirmed acceptable uniformity of the patients’ total body surfaces and revealed anatomically self-blocked or shielded areas where underdosing occurred. Conclusions: The clinical implementation of DFR-TSET effectively utilizes the special mode on a linac. This technique provides short beam-on times, uniform dose distribution, large treatment field, and reduced dose of x-ray contamination to the patients. In-vivo measurements indicate satisfactory delivery and dose uniformity of the prescribed dose. Electron boost fields are recommended at normal SSDs to address underdosed areas.

关键词： Total Skin Electron Therapy Stanford 6 Field McGill Rotation Therapy In-Vivo Dosimetry

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Multi-Organ Pathogenesis and Therapeutic Strategies for Cystic Fibrosis

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Journal of Biosciences and Medicines 2024年第5期12卷 312-329页

作者： Oluwaseun Babatunde Ogonna William David richard Ifeanyichukwu Ikwugbado Oluwaseyi Oyewole Fetal Research and Therapy Program Wake Forest Institute for Regenerative Medicine Winston-Salem NC USA Department of Chemistry University of Houston Houston TX USA Biotechnology Graduate Program School of Sciences and Engineering The American University in Cairo New Cairo Egypt

Cystic Fibrosis (CF) is the most common lethal autosomal inherited disorder that affects all races and ethnicities in the United States. However, it is mostly predominant in the Caucasian populace accounting for about 80% of all CF cases. CF most severe complication can be referred to as pulmonary bronchiectasis and infections of the airways, nonetheless, the devastating effects of the disease have far-reaching consequences beyond lung damage. CF is a heterogeneous disease that is caused by mutations in Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. The impairment or absence of this gene can affect multiple organs and systems and is characterized not only by chronic lung blockage, infections, and inflammation but also by exocrine gland dysfunction, intestinal obstruction, liver pathology, elevated sweat chloride concentration, and in males, infertility due to the congenital bilateral absence of the vas deferens. To this end, we briefly explore the pathological effects of CF and how CF mediates the destruction of several critical organs in the body and some of the gene therapeutical approaches such as gene editing and viral-based strategies available for the treatment of this multi-organ disease.

关键词： Cystic Fibrosis Gene Therapy Organ Damage

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