The aim of the study is to comparatively assess the concentrations of lead, zinc and iron in Rivers Ase, Warri and Ethiope, in Nigeria. Monthly water samples were collected from six randomly selected sites along the r...The aim of the study is to comparatively assess the concentrations of lead, zinc and iron in Rivers Ase, Warri and Ethiope, in Nigeria. Monthly water samples were collected from six randomly selected sites along the rivers course. 72 water samples were collected from each river at 0 - 15 cm depths. Samples were analysed based on the standard methods recommended by the WHO for testing lead, zinc and iron. The assessment of the water quality was done using the Water Quality Index (WQI) of the Canadian Council of Ministers of the Environment (CCME-WQI). While hypotheses were tested using ANOVA. Findings indicated that CCME-WQI values were 47.3, 66.52 and 78.7. This meant that the water quality of River Ase is impaired and departed from desirable levels, while that of Warri and Ethiope were considered to occasionally be impaired and depart from desirable levels. The ANOVA model showed that there is a significant variation in heavy metal load in the selected rivers at P < 0.05. River water was put to domestic uses such as drinking (20.5%) preparing food (17.8%), bathing (19.8%), washing clothes and dishes (21.3%), brushing teeth (13.3%), and catering for domestic animals (7.5%). Poverty (49.5%) was the major reason for the use of river water for domestic purposes. The locals highlighted that they usually suffer from cholera (26.8%), diarrhoea (25.8%), dysentery (24%) and typhoid (23.5%) as a result of using the river water. The study recommended routine monitoring of anthropogenic and geologic activities, testing of the water regularly amongst others.展开更多
The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of G...The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.展开更多
Cancer patients are at high risk of malnutrition,which can lead to adverse health outcomes such as prolonged hospitalization,increased complications,and increased mortality.Accurate and timely nutritional assessment p...Cancer patients are at high risk of malnutrition,which can lead to adverse health outcomes such as prolonged hospitalization,increased complications,and increased mortality.Accurate and timely nutritional assessment plays a critical role in effectively managing malnutrition in these patients.However,while many tools exist to assess malnutrition,there is no universally accepted standard.Although different tools have their own strengths and limitations,there is a lack of narrative reviews on nutritional assessment tools for cancer patients.To address this knowledge gap,we conducted a non-systematic literature search using PubMed,Embase,Web of Science,and the Cochrane Library from their inception until May 2023.A total of 90 studies met our selection criteria and were included in our narrative review.We evaluated the applications,strengths,and limitations of 4 commonly used nutritional assessment tools for cancer patients:the Subjective Global Assessment(SGA),Patient-Generated Subjective Global Assessment(PG-SGA),Mini Nutritional Assessment(MNA),and Global Leadership Initiative on Malnutrition(GLIM).Our findings revealed that malnutrition was associated with adverse health outcomes.Each of these 4 tools has its applications,strengths,and limitations.Our findings provide medical staff with a foundation for choosing the optimal tool to rapidly and accurately assess malnutrition in cancer patients.It is essential for medical staff to be familiar with these common tools to ensure effective nutritional management of cancer patients.展开更多
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co...Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.展开更多
The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. I...The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.展开更多
Quantitative assessment of the impact of climate variability and human activities on runoff plays a pivotal role in water resource management and maintaining ecosystem integrity.This study considered six sub-basins in...Quantitative assessment of the impact of climate variability and human activities on runoff plays a pivotal role in water resource management and maintaining ecosystem integrity.This study considered six sub-basins in the upper reaches of the Yangtze River basin,China,to reveal the trend of the runoff evolution and clarify the driving factors of the changes during 1956–2020.Linear regression,Mann-Kendall test,and sliding t-test were used to study the trend of the hydrometeorological elements,while cumulative distance level and ordered clustering methods were applied to identify mutation points.The contributions of climate change and human disturbance to runoff changes were quantitatively assessed using three methods,i.e.,the rainfall-runoff relationship method,slope variation method,and variable infiltration capacity(Budyko)hypothesis method.Then,the availability and stability of the three methods were compared.The results showed that the runoff in the upper reaches of the Yangtze River basin exhibited a decreasing trend from 1956 to 2020,with an abrupt change in 1985.For attribution analysis,the runoff series could be divided into two phases,i.e.,1961–1985(baseline period)and 1986–2020(changing period);and it was found that the rainfall-runoff relationship method with precipitation as the representative of climate factors had limited usability compared with the other two methods,while the slope variation and Budyko hypothesis methods had highly consistent results.Different factors showed different effects in the sub-basins of the upper reaches of the Yangtze River basin.Moreover,human disturbance was the main factor that contributed to the runoff changes,accounting for 53.0%–82.0%;and the contribution of climate factors to the runoff change was 17.0%–47.0%,making it the secondary factor,in which precipitation was the most representative climate factor.These results provide insights into how climate and anthropogenic changes synergistically influence the runoff of the upper reaches of the Yangtze River basin.展开更多
Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for...Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among Saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of Saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the Statistical Package for Social Sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.展开更多
Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In thi...Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.展开更多
With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk manageme...With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.展开更多
Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to ob...Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.展开更多
Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate resul...Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.展开更多
China has always been upholding the harmonious coexistence between man and nature, and protecting eco-environment at home and abroad. Therefore, the first national standard for river ecological safety assessment, GB/T...China has always been upholding the harmonious coexistence between man and nature, and protecting eco-environment at home and abroad. Therefore, the first national standard for river ecological safety assessment, GB/T 43474-2023, Technical guidelines for river ecological security assessment, was recently released, which will come into effect on April 1, 2024.展开更多
A good quality Environmental Impact Statement (EIS) is key for the effectiveness of Environmental Impact Assessment (EIA) processes and consequently to the acceptability of projects subject to EIA. The international l...A good quality Environmental Impact Statement (EIS) is key for the effectiveness of Environmental Impact Assessment (EIA) processes and consequently to the acceptability of projects subject to EIA. The international literature has contributed to the understanding of the essential aspects to be verified regarding the quality of EIS, offering a wide spectrum of good practice examples related to the content of the studies. Even so, there is a need for empirical studies that allow the identification of specific aspects related to the context of application of the EIS, which could lead to the identification of opportunities to improve both the quality of the reports and also the effectiveness of EIA. Therefore, the present paper is focused on the quality review of a number of EIS submitted to the Brazilian Federal Environmental Agency (Ibama) to instruct the assessment of electric power transmission systems. Based on the application of the EIS quality review package as proposed by Lee and Colley (1992), the outcomes reveal opportunities for improving the scope of EIA, analysis of alternatives, prediction of magnitude and the assessment of impact significance. Finally, the development and/or adaptation of a similar tool for the systematic review of the quality of EIA reports is recommended.展开更多
Although the pediatric perioperative pain management has been improved in recent years,the valid and reliable pain assessment tool in perioperative period of children remains a challenging task.Pediatric perioperative...Although the pediatric perioperative pain management has been improved in recent years,the valid and reliable pain assessment tool in perioperative period of children remains a challenging task.Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults,but also because there is a lack of effective and specific assessment tool for children.In addition,exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae.The short-term sequelae can induce a series of neurological,endocrine,cardiovascular system stress related to psychological trauma,while long-term sequelae may alter brain maturation process,which can lead to impair neurodevelopmental,behavioral,and cognitive function.Children’s facial expressions largely reflect the degree of pain,which has led to the developing of a number of pain scoring tools that will help improve the quality of pain mana-gement in children if they are continually studied in depth.The artificial inte-lligence(AI)technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks,which can effectively identify and systematically analyze various subtle features of children’s facial expressions.Based on the construction of a large database of images of facial expressions in children with perioperative pain,this study proposes to develop and apply automatic facial pain expression recognition software using AI technology.The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.展开更多
This study purposes an in situ testing method on quality assessment of soil improvement.Factual drilling data includes the spatial distribution and in situ strength of untreated and treated soil along three different ...This study purposes an in situ testing method on quality assessment of soil improvement.Factual drilling data includes the spatial distribution and in situ strength of untreated and treated soil along three different drillholes measured by on-site drilling monitoring method.These factual drilling data can characterize the degree of soil improvement by penetration injection with permeable polyurethane.Result from on-site drilling monitoring shows that the linear zones represent constant drilling speeds shown in the plot of drill bit advancement vs.net drilling time,which indicates the spatial distributions of soil profile.The soil profile at the study site is composed of four layers,which includes fill,untreated silty clay,treated silty clay,and mucky soil.The results of soil profile are verified by the parallel site loggings.The constant drilling speeds profile the coring-resistant strength of drilled soils.By comparing with the untreated silty clay,the constant drilling speeds of the treated silty clay have been decreased by 13.0-62.8%.Two drilling-speed-based indices of 61.2%and 65.6%are proposed to assess the decreased average drilling speed and the increased in situ strength of treated silty clay.Laboratory tests,i.e.uniaxial compressive strength(UCS)test,have been performed with core sample to investigate and characterize in situ strength by comparing that with drilling speeds.Results show that the average predicted strengths of treated silty clay are 2.4-6.9 times higher than the average measured strength of untreated silty clay.The UCS-based indices of 374.5%and 344.2%verified the quality assessment(QA)results by this new in situ method.This method provides a cost-effective tool for quality assessment of soil improvement by utilizing the digital drilling data.展开更多
Follow-up of environmental impacts is an integral part of the Environmental Impact Assessment (EIA) process, closely related to the effectiveness of the instrument. EIA follow-up has been receiving a lot of interest f...Follow-up of environmental impacts is an integral part of the Environmental Impact Assessment (EIA) process, closely related to the effectiveness of the instrument. EIA follow-up has been receiving a lot of interest from scientists and practitioners, though it is recognized as one of the weakest points of EIA systems globally. Also, EIA follow-up is influenced by the context, mainly in terms of the types of projects or activities and their related impacts on the environment. Therefore, the present paper is focused on the investigation of the follow-up stage applied to the activity of seismic survey coupled with offshore oil & gas exploitation in Brazil. Research was based on a qualitative approach that included document analysis and semi-structured interviews with analysts involved in EIA processes, and sought to generate evidence of effectiveness of the EIA follow-up as conducted by the Federal Environment Agency (Ibama) in order to situate the practice of follow-up in the broader context of international best practice principles. Based on the findings, it was concluded that, due to the peculiarities of offshore seismic survey, it is necessary to promote adaptations in the procedures for monitoring impacts in order to ensure proper alignment with the principles and conceptual foundations that guide EIA practice. Specifically, the timing of the execution of the activity imposes challenges for its integration into the “conventional” cycle that has guided the monitoring of the impacts in the EIA of projects.展开更多
Niligou Reservoir,a typical reservoir in the eastern region of Jilin Province,is subordinate to Niligou River,which is located in the Changbai Mountains in eastern Jilin,with abundant plant and animal resources and we...Niligou Reservoir,a typical reservoir in the eastern region of Jilin Province,is subordinate to Niligou River,which is located in the Changbai Mountains in eastern Jilin,with abundant plant and animal resources and well protected biodiversity in the basin,but there are also some related problems such as fragile ecosystem of forest wetland.This paper carried out a health assessment of Niligou River,including water quality monitoring,aquatic organism monitoring,and riparian zone investigation.According to the requirements of the Technical Guidelines for River and Lake Health Assessment,13 assessment indexes were selected to build a river and lake health assessment index system.By sorting and analyzing the monitoring and investigation data,we got a clear picture of the ecological environment status and existing problems of Niligou Res-ervoir.Based on the actual situation of Niligou River,we evaluated the hydrologic integrity,chemical integrity,morphological and structural integrity,biological integrity and sustainability of social service function,and put forward corresponding countermeasures according to the re-sults,in order to provide a technical support for the health treatment of rivers and lakes in Jilin Province.展开更多
Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection method...Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.展开更多
Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,th...Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,the DRASTIC method was applied using Geographic Information System(GIS)to delineate groundwater vulnerability zones in the Erbil Dumpsite area,Central Erbil Basin,North Iraq.Results showed that the area was classified into four vulnerability classes:Very low(16.97%),low(27.67%),moderate(36.55%)and high(18.81%).The southern,south-eastern and northern parts of the study area exhibited the highest vulnerability potential,while the central-northern,northern and north-western regions displayed the lowest vulnerability potential.Moreover,results of the single-parameter sensitivity analysis indicated that amongst the seven DRASTIC parameters,the unsaturated zone and the aquifer media were the most influencing parameters.In conclustion,the correlation of 25 nitrate concentration values with the final vulnerability map,assessed using the Pearson correlation coefficient,yielded a satisfactory result of R=0.72.展开更多
This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed ...This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.展开更多
文摘The aim of the study is to comparatively assess the concentrations of lead, zinc and iron in Rivers Ase, Warri and Ethiope, in Nigeria. Monthly water samples were collected from six randomly selected sites along the rivers course. 72 water samples were collected from each river at 0 - 15 cm depths. Samples were analysed based on the standard methods recommended by the WHO for testing lead, zinc and iron. The assessment of the water quality was done using the Water Quality Index (WQI) of the Canadian Council of Ministers of the Environment (CCME-WQI). While hypotheses were tested using ANOVA. Findings indicated that CCME-WQI values were 47.3, 66.52 and 78.7. This meant that the water quality of River Ase is impaired and departed from desirable levels, while that of Warri and Ethiope were considered to occasionally be impaired and depart from desirable levels. The ANOVA model showed that there is a significant variation in heavy metal load in the selected rivers at P < 0.05. River water was put to domestic uses such as drinking (20.5%) preparing food (17.8%), bathing (19.8%), washing clothes and dishes (21.3%), brushing teeth (13.3%), and catering for domestic animals (7.5%). Poverty (49.5%) was the major reason for the use of river water for domestic purposes. The locals highlighted that they usually suffer from cholera (26.8%), diarrhoea (25.8%), dysentery (24%) and typhoid (23.5%) as a result of using the river water. The study recommended routine monitoring of anthropogenic and geologic activities, testing of the water regularly amongst others.
基金the National Key R&D Program of China(Grant No.2022YFF0503702)the National Natural Science Foundation of China(Grant Nos.42074186,41831071,42004136,and 42274195)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20211036)the Specialized Research Fund for State Key Laboratories,and the University of Science and Technology of China Research Funds of the Double First-Class Initiative(Grant No.YD2080002013).
文摘The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.
基金financially supported by the Guangxi Medical University 2023 Innovation and Entrepreneurship Training Program Project(No.202310598015).
文摘Cancer patients are at high risk of malnutrition,which can lead to adverse health outcomes such as prolonged hospitalization,increased complications,and increased mortality.Accurate and timely nutritional assessment plays a critical role in effectively managing malnutrition in these patients.However,while many tools exist to assess malnutrition,there is no universally accepted standard.Although different tools have their own strengths and limitations,there is a lack of narrative reviews on nutritional assessment tools for cancer patients.To address this knowledge gap,we conducted a non-systematic literature search using PubMed,Embase,Web of Science,and the Cochrane Library from their inception until May 2023.A total of 90 studies met our selection criteria and were included in our narrative review.We evaluated the applications,strengths,and limitations of 4 commonly used nutritional assessment tools for cancer patients:the Subjective Global Assessment(SGA),Patient-Generated Subjective Global Assessment(PG-SGA),Mini Nutritional Assessment(MNA),and Global Leadership Initiative on Malnutrition(GLIM).Our findings revealed that malnutrition was associated with adverse health outcomes.Each of these 4 tools has its applications,strengths,and limitations.Our findings provide medical staff with a foundation for choosing the optimal tool to rapidly and accurately assess malnutrition in cancer patients.It is essential for medical staff to be familiar with these common tools to ensure effective nutritional management of cancer patients.
基金supported by the projects of the China Geological Survey(DD20221729,DD20190291)Zhuhai Urban Geological Survey(including informatization)(MZCD–2201–008).
文摘Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.
文摘The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.
基金supported by the National Natural Science Foundation of China(52009140).
文摘Quantitative assessment of the impact of climate variability and human activities on runoff plays a pivotal role in water resource management and maintaining ecosystem integrity.This study considered six sub-basins in the upper reaches of the Yangtze River basin,China,to reveal the trend of the runoff evolution and clarify the driving factors of the changes during 1956–2020.Linear regression,Mann-Kendall test,and sliding t-test were used to study the trend of the hydrometeorological elements,while cumulative distance level and ordered clustering methods were applied to identify mutation points.The contributions of climate change and human disturbance to runoff changes were quantitatively assessed using three methods,i.e.,the rainfall-runoff relationship method,slope variation method,and variable infiltration capacity(Budyko)hypothesis method.Then,the availability and stability of the three methods were compared.The results showed that the runoff in the upper reaches of the Yangtze River basin exhibited a decreasing trend from 1956 to 2020,with an abrupt change in 1985.For attribution analysis,the runoff series could be divided into two phases,i.e.,1961–1985(baseline period)and 1986–2020(changing period);and it was found that the rainfall-runoff relationship method with precipitation as the representative of climate factors had limited usability compared with the other two methods,while the slope variation and Budyko hypothesis methods had highly consistent results.Different factors showed different effects in the sub-basins of the upper reaches of the Yangtze River basin.Moreover,human disturbance was the main factor that contributed to the runoff changes,accounting for 53.0%–82.0%;and the contribution of climate factors to the runoff change was 17.0%–47.0%,making it the secondary factor,in which precipitation was the most representative climate factor.These results provide insights into how climate and anthropogenic changes synergistically influence the runoff of the upper reaches of the Yangtze River basin.
文摘Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among Saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of Saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the Statistical Package for Social Sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.
基金This work is supported by the National Natural Science Foundation of China(Nos.72071150,71871174).
文摘Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.
基金the National Natural Science Foundation of China(U2033213)the Fundamental Research Funds for the Central Universities(FZ2021ZZ01,FZ2022ZX50).
文摘With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.
基金supported by the National Natural Science Foundation of China (No.72071150).
文摘Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.
基金This research is partially supported by grant from the National Natural Science Foundation of China(No.72071019)grant from the Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0185)grant from the Chongqing Graduate Education and Teaching Reform Research Project(No.yjg193096).
文摘Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.
文摘China has always been upholding the harmonious coexistence between man and nature, and protecting eco-environment at home and abroad. Therefore, the first national standard for river ecological safety assessment, GB/T 43474-2023, Technical guidelines for river ecological security assessment, was recently released, which will come into effect on April 1, 2024.
文摘A good quality Environmental Impact Statement (EIS) is key for the effectiveness of Environmental Impact Assessment (EIA) processes and consequently to the acceptability of projects subject to EIA. The international literature has contributed to the understanding of the essential aspects to be verified regarding the quality of EIS, offering a wide spectrum of good practice examples related to the content of the studies. Even so, there is a need for empirical studies that allow the identification of specific aspects related to the context of application of the EIS, which could lead to the identification of opportunities to improve both the quality of the reports and also the effectiveness of EIA. Therefore, the present paper is focused on the quality review of a number of EIS submitted to the Brazilian Federal Environmental Agency (Ibama) to instruct the assessment of electric power transmission systems. Based on the application of the EIS quality review package as proposed by Lee and Colley (1992), the outcomes reveal opportunities for improving the scope of EIA, analysis of alternatives, prediction of magnitude and the assessment of impact significance. Finally, the development and/or adaptation of a similar tool for the systematic review of the quality of EIA reports is recommended.
文摘Although the pediatric perioperative pain management has been improved in recent years,the valid and reliable pain assessment tool in perioperative period of children remains a challenging task.Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults,but also because there is a lack of effective and specific assessment tool for children.In addition,exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae.The short-term sequelae can induce a series of neurological,endocrine,cardiovascular system stress related to psychological trauma,while long-term sequelae may alter brain maturation process,which can lead to impair neurodevelopmental,behavioral,and cognitive function.Children’s facial expressions largely reflect the degree of pain,which has led to the developing of a number of pain scoring tools that will help improve the quality of pain mana-gement in children if they are continually studied in depth.The artificial inte-lligence(AI)technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks,which can effectively identify and systematically analyze various subtle features of children’s facial expressions.Based on the construction of a large database of images of facial expressions in children with perioperative pain,this study proposes to develop and apply automatic facial pain expression recognition software using AI technology.The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.
基金supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region,PR China(Project Nos.HKU 17207518 and R5037-18).
文摘This study purposes an in situ testing method on quality assessment of soil improvement.Factual drilling data includes the spatial distribution and in situ strength of untreated and treated soil along three different drillholes measured by on-site drilling monitoring method.These factual drilling data can characterize the degree of soil improvement by penetration injection with permeable polyurethane.Result from on-site drilling monitoring shows that the linear zones represent constant drilling speeds shown in the plot of drill bit advancement vs.net drilling time,which indicates the spatial distributions of soil profile.The soil profile at the study site is composed of four layers,which includes fill,untreated silty clay,treated silty clay,and mucky soil.The results of soil profile are verified by the parallel site loggings.The constant drilling speeds profile the coring-resistant strength of drilled soils.By comparing with the untreated silty clay,the constant drilling speeds of the treated silty clay have been decreased by 13.0-62.8%.Two drilling-speed-based indices of 61.2%and 65.6%are proposed to assess the decreased average drilling speed and the increased in situ strength of treated silty clay.Laboratory tests,i.e.uniaxial compressive strength(UCS)test,have been performed with core sample to investigate and characterize in situ strength by comparing that with drilling speeds.Results show that the average predicted strengths of treated silty clay are 2.4-6.9 times higher than the average measured strength of untreated silty clay.The UCS-based indices of 374.5%and 344.2%verified the quality assessment(QA)results by this new in situ method.This method provides a cost-effective tool for quality assessment of soil improvement by utilizing the digital drilling data.
文摘Follow-up of environmental impacts is an integral part of the Environmental Impact Assessment (EIA) process, closely related to the effectiveness of the instrument. EIA follow-up has been receiving a lot of interest from scientists and practitioners, though it is recognized as one of the weakest points of EIA systems globally. Also, EIA follow-up is influenced by the context, mainly in terms of the types of projects or activities and their related impacts on the environment. Therefore, the present paper is focused on the investigation of the follow-up stage applied to the activity of seismic survey coupled with offshore oil & gas exploitation in Brazil. Research was based on a qualitative approach that included document analysis and semi-structured interviews with analysts involved in EIA processes, and sought to generate evidence of effectiveness of the EIA follow-up as conducted by the Federal Environment Agency (Ibama) in order to situate the practice of follow-up in the broader context of international best practice principles. Based on the findings, it was concluded that, due to the peculiarities of offshore seismic survey, it is necessary to promote adaptations in the procedures for monitoring impacts in order to ensure proper alignment with the principles and conceptual foundations that guide EIA practice. Specifically, the timing of the execution of the activity imposes challenges for its integration into the “conventional” cycle that has guided the monitoring of the impacts in the EIA of projects.
文摘Niligou Reservoir,a typical reservoir in the eastern region of Jilin Province,is subordinate to Niligou River,which is located in the Changbai Mountains in eastern Jilin,with abundant plant and animal resources and well protected biodiversity in the basin,but there are also some related problems such as fragile ecosystem of forest wetland.This paper carried out a health assessment of Niligou River,including water quality monitoring,aquatic organism monitoring,and riparian zone investigation.According to the requirements of the Technical Guidelines for River and Lake Health Assessment,13 assessment indexes were selected to build a river and lake health assessment index system.By sorting and analyzing the monitoring and investigation data,we got a clear picture of the ecological environment status and existing problems of Niligou Res-ervoir.Based on the actual situation of Niligou River,we evaluated the hydrologic integrity,chemical integrity,morphological and structural integrity,biological integrity and sustainability of social service function,and put forward corresponding countermeasures according to the re-sults,in order to provide a technical support for the health treatment of rivers and lakes in Jilin Province.
文摘Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.
文摘Groundwater vulnerability assessment is a crucial step in the efficient management of groundwater resources,especially in areas with intensive anthropogenic activities and groundwater pollution.In the present study,the DRASTIC method was applied using Geographic Information System(GIS)to delineate groundwater vulnerability zones in the Erbil Dumpsite area,Central Erbil Basin,North Iraq.Results showed that the area was classified into four vulnerability classes:Very low(16.97%),low(27.67%),moderate(36.55%)and high(18.81%).The southern,south-eastern and northern parts of the study area exhibited the highest vulnerability potential,while the central-northern,northern and north-western regions displayed the lowest vulnerability potential.Moreover,results of the single-parameter sensitivity analysis indicated that amongst the seven DRASTIC parameters,the unsaturated zone and the aquifer media were the most influencing parameters.In conclustion,the correlation of 25 nitrate concentration values with the final vulnerability map,assessed using the Pearson correlation coefficient,yielded a satisfactory result of R=0.72.
基金supported by Project of Chongqing Science and Technology Bureau (cstc2022jxjl0005)。
文摘This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.
