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The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: In spite of the substantial impact of pediatric asthma on healthcare resources, there is a notable dearth of research focusing on allergen sensitization specifically in children and adolescents with asthma in Cameroon, with existing studies primarily concentrating on adult populations. We, therefore, set out to determine factors associated with sensitization to selected aero-allergens in children and adolescents with asthma followed up at Laquintinie Hospital Douala, Cameroon., Methodology: We conducted a cross-sectional analytical study on patients aged 2-17 years followed up for pediatric asthma at Laquintinie Hospital Douala for 8 months (January to August 2023). Skin prick tests were performed using selected standardized aeroallergens extracts. Factors associated with sensitization were determined using multivariable logistic regression analysis. The threshold for significance was set at p < .05., Results: In total, 126 children and adolescents with asthma were recruited, 50.79% of whom were male with an average age of 8.00 ± 3.99 years. Eight out of 10 patients (83.52%) were sensitized to common aero-allergens (20.59% monosensitized and 79.41% polysensitized). The most common aero-allergens found were the dust mites Dermatophagoïdes pteronyssinus (81.37%), Blomia tropicalis (67.64%) and Dermatophagoïdes farinae (52.94%) followed by Blatella species (25.49%). The factors independently associated with sensitization in our population were the personal history of allergic rhinitis in the patient [adjusted odds ratio, aOR: 4.07 (95% confidence interval, CI: 1.41-11.76), b = 0.54, p = .009], chest tightness at rest [aOR: 5.92 (95% CI: 1.08-32.38), b = 0.87, p = .040), and age above 5 years [aOR: 4.65 (95% CI: 1.47-14.69), b = 0.59, p = .009)., Conclusion: Sensitization to multiple aeroallergens is common in children and adolescents with asthma, especially for dust mites and cockroaches. Factors associated with sensitization are patients aged >5 years, chest tightness at rest, and a personal history of allergic rhinitis., (© 2024 Wiley Periodicals LLC.)

Purpose: Genital lymphedema is a chronic debilitating condition associated with highly impaired health-related quality of life (QoL). This prospective multicenter study evaluated the use of a new compressive garment in patients with secondary and primary genital lymphedema. Methods: Thirty-two patients prospectively enrolled were advised to wear the compressive garment for 12 weeks (day and night). The primary endpoint was change in patient-reported QoL at 12 weeks via the patient global impression of change (PGI-C) instrument. Secondary outcomes included change in other QoL measures at 12 weeks (visual analog scale, Lymphedema Quality of Life Inventory [LyQLI], and EQ-5D questionnaires), lymphedema severity (genital lymphedema score [GLS]), and physician assessment (Clinical Global Impression-Improvement [CGI-I]). Safety and tolerability were also assessed. Results: After 12 weeks, improvement was reported in 78.6% of patients (PGI-C). Physician assessment (CGI-I) indicated clinical improvement in 82.8% of patients. Patient assessment of lymphedema symptoms showed a significant decrease in discomfort ( p  = 0.02) and swelling ( p  = 0.01). Significant declines in the mean global GLS ( p  < 0.0001), and in the proportion of patients reporting heaviness, tightness, swelling, or urinary dysfunction ( p  < 0.05 for all), were also observed. LyQLI scores decreased (indicating improved QoL) in each of the physical, psychosocial ( p  = 0.05), and practical domains. The compressive garment was well tolerated with high compliance, and adverse events (due to swelling or discomfort) led to permanent discontinuation in only three patients. Conclusion: The use of a new genital compression garment over 12 weeks improves the QoL and clinical measures in patients with genital lymphedema (ClinicalTrials.gov ID: NCT04602559; Registration: October 20, 2020).

Introduction: In Cameroon, acute bronchiolitis has been reported as the third commonest lower respiratory infection and is usually associated with low mortality. Nonetheless, respiratory distress associated with non-adherence to management guidelines can prolong hospital stay. This study aimed to explore predictors of prolonged hospital stay (≥ 5 days) and mortality in patients aged < 2years hospitalised for acute bronchiolitis., Methodology: We conducted a retrospective cohort study at three paediatric units in the city of Douala, Cameroon. Factors associated with prolonged hospital stay and mortality were determined using multivariable linear regression model. Threshold for significance was set at p ≤ 0.05., Results: A total of 215 patients with bronchiolitis were included with mean age of 6.94 ± 5.71 months and M/F sex ratio of 1.39/1. Prolonged hospital stay was reported in 46.98% and mortality in 10.70% of patients hospitalised for bronchiolitis. Factors independently associated with prolonged hospital duration were oxygen administration [b = 0.36, OR = 2.35 (95% CI:1.16-4.74), p = 0.017], abnormal respiratory rate [b = 0.38, OR = 2.13 (1.00-4.55), p = 0.050] and patients presenting with cough [b = 0.33, OR = 2.35 (95% CI: 1.22-4.51), p = 0.011], and diarrhoea [b = 0.71, OR = 6.44 (95% CI: 1.6-25.86), p = 0.009] on admission. On the other hand, factors independently associated with mortality were age of the patient [b= -0.07, OR = 0.84 (95% CI: 0.74-0.97), p = 0.014] and oxygen administration [b = 1.08, OR = 9.64 (95% CI:1.16-79.85), p = 0.036] CONCLUSION: Acute bronchiolitis represented 1.24% of admissions and was common in the rainy season, in males and 3-11-month-old patients. Management guidelines were poorly respected. Prolonged length of stay was reported in half of the patients hospitalized and mortality was high, especially in younger patients and in patients receiving oxygen., (© 2024. The Author(s).)

Introduction: Pretibial myxedema is a rare manifestation of Graves' disease, and pseudotumoral forms may be confused with lower limb lymphedema., Observations: We reported 3 cases of pretibial myxedema in 2 women and 1 man, aged 72, 66, and 49 years, treated for Graves' disease 3, 25 and 32 years previously. Two patients were active smokers. Lymphedema diagnosis of the lower limbs was suspected in the presence of bilateral pseudotumoral lesions of the feet, toes and ankles and the presence of a Stemmer's sign (skin thickening at the base of the 2nd toe, pathognomonic of lymphedema). Lymphoscintigraphy in one case was normal, not confirming lymphedema., Conclusion: Pretibial pseudotumoral myxedema is a differential diagnosis of lower limb lymphedema. This diagnosis is confirmed by questioning the patient about preexisting Graves' disease, the underlying etiology, to decide the appropriate treatment and to encourage cessation of smoking, which is a risk factor for pretibial myxedema., (Copyright © 2023 Société Nationale Française de Médecine Interne (SNFMI). Published by Elsevier Masson SAS. All rights reserved.)

The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals requires the development of a robust model that represents the crude-to-chemical conversion processes. This study utilizes artificial intelligence (AI) and machine learning algorithms to develop single and ensemble learning models that predict the yields of ethylene and propylene. Four single-model AI techniques and four ensemble paradigms were developed using experimental data derived from the catalytic cracking experiments of various crude oil fractions in the advanced catalyst evaluation reactor unit. The temperature, feed type, feed conversion, total gas, dry gas, and coke were used as independent variables. Correlation matrix analyses were conducted to filter the input combinations into three different classes (M1, M2, and M3) based on the relationship between dependent and independent variables, and three performance metrics comprising the coefficient of determination ( R 2 ), Pearson correlation coefficient (PCC), and mean square error (MSE) were used to evaluate the prediction performance of the developed models in both calibration and validations stages. All four single models have very low R 2 and PCC values (as low as 0.07) and very high MSE values (up to 4.92 wt %) for M1 and M2 in both calibration and validation phases. However, the ensemble ML models show R 2 and PCC values of 0.99-1 and an MSE value of 0.01 wt % for M1, M2, and M3 input combinations. Therefore, ensemble paradigms improve the performance accuracy of single models by up to 58 and 62% in the calibration and validation phases, respectively. The ensemble paradigms predict with high accuracy the yield of ethylene and propylene in the catalytic cracking of crude oil and its fractions., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The intrusion of seawater (SWI) into coastal aquifers is a major concern worldwide, affecting the quantity and quality of groundwater resources. The region of Saudi Arabia that lies along the eastern coast has been affected by SWI, making it crucial to accurately identify and monitor the affected areas. This investigation aimed to map the degree of seawater intrusion in a complex aquifer system in the study area using an integrated clustering analysis approach. The study collected 41 groundwater samples from wells penetrating multi-layered aquifers, and the samples were analyzed for physicochemical properties and major ions. Clustering analysis methods, including Hierarchical Clustering Analysis (double-clustering) (HCA-DC), K-mean (KMC), and fuzzy k-mean clustering (FKM), were employed to evaluate the spatial distribution and association of the groundwater properties. The results revealed that the analyzed GW samples were divided into four clusters with varying degrees of SWI. Clusters A, B, C, and D contained GW samples with very low ( f sea of 1.9%), high ( f sea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability.f sea of 7.9%), and low ( f sea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability., Competing Interests: The authors declare no conflict of interest.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Fluoride and nitrate contamination of groundwater is a major environmental issue in the world's arid and semiarid regions. This issue is severe in both developed and developing countries. This study aimed at assessing the concentration levels, contamination mechanisms, toxicity, and human health risks of NO 3 - and F - in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F - was estimated to be higher than NO 3 - . Also, the health risk assessment revealed higher risks due to F - than NO 3 - . Younger populations had higher risks than elderly populations. For both F - and NO 3 . Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks.- , the order of health risk was Infants > Children > Adults. Most of the samples posed medium to high chronic risks due to F - and NO 3 - ingestion. However, negligible health risks were obtained for potential dermal absorption of NO 3 - . Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Due to the significant energy and economic losses brought on by the global oil spill, there has been an increased interest in oil-water separation. This study presents strong non-linear machine learning models (support vector regression (SVR) and Gaussian process regression (GPR)) with the Response surface method (RSM) to predict the oil flux and oil-water separation efficiency of wastewater using ceramic membrane technology. For the model development and prediction of oil flux (OF) and oil-water separation efficiency (OSE), oil concentration (mg/L), feed flow rate (mL/min), and pH were considered as input variables. The input variables are combined in three combinations to study the most contributing input features to the models' performance. Mean square error (MSE) and Nash-Sutcliffe coefficient efficiency (NSE) were used to assess the prediction performances of the developed models with the different number of input combinations considered in the study. For the two target variables (OF and OSE), GPR and SVR models were used to separately predict them. For OF, the SVR-2 [Combo-2] model (MSE = 0.9255 and NSE = 2.7976) performed better with higher prediction accuracy compared to GPR-2 [Combo-2] model (MSE = 0.763 and NSE = 6.437). In addition, for OSE, the GPR-3 [Combo-3] model (MSE = 0.995 and NSE = 0.5544) performed slightly better than SVR-3 [Combo-3] model (MSE = 0.992 and NSE = 0.8066). The results showed that the SVR model with the combo-2 and GPR-3 models for OF and OSE variables are the proposed models with the best performance and accuracy. This machine learning study will aid in better evaluating the function of materials such as ceramic in membrane performance features such as oil flux and rejection prediction, separation efficiency, water recovery, membrane fouling, and so on. As for academics and manufacturers, this machine learning (ML) strategy will boost performance and allow a better understanding of system governance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Human health and the sustainability of the socioeconomic system are directly related to water quality. As anthropogenic activity becomes more intense, pollutants, particularly potentially harmful elements (PHEs), penetrate water systems and degrade water quality. The purpose of this study was to evaluate the safety of using groundwater for domestic and drinking purposes through oral and dermal exposure routes, as well as the potential health risks posed to humans in the Nnewi and Awka regions of Nigeria. The research involved the application of a combination of the National Sanitation Foundation Water Quality Index (NSFWQI), HERisk code, and hierarchical dendrograms. Additionally, we utilized the regulatory guidelines established by the World Health Organization and the Standard Organization of Nigeria to compare the elemental compositions of the samples. The physicochemical parameters and NSFWQI evaluation revealed that the majority of the samples were PHE-polluted. Based on the HERisk code, it was discovered that in both the Nnewi and Awka regions, risk levels are higher for people aged 1 to <11 and >65 than for people aged 16 to <65. Overall, it was shown that all age categories appeared to be more vulnerable to risks due to the consumption than absorption of PHEs, with Cd > Pb > Cu > Fe for Nnewi and Pb > Cd > Cu > Fe for water samples from Awka. Summarily, groups of middle age are less susceptible to possible health issues than children and elderly individuals. Hierarchical dendrograms and correlation analysis showed the spatio-temporal implications of the drinking groundwater quality and human health risks in the area. This research could help local government agencies make informed decisions on how to effectively safeguard the groundwater environment while also utilizing the groundwater resources sustainably., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly's ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index ( f sea ) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI ( f sea ) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index ( f sea ), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation.

The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models-specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)-were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

Antiretroviral therapy (ART) is the common hope for HIV/AIDS-treated patients. Total commitments from individuals and the entire community are the major challenges faced during treatment. This study investigated the progress of ART in the Federal Teaching Hospital in Gombe state, Nigeria by using various records of patients receiving treatment in the ART hospital unit. We combined artificial intelligence (AI)-based models and correspondence analysis (CA) techniques to predict and visualize the progress of ART from the beginning to the end. The AI models employed are artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFISs) and support-vector machines (SVMs) and a classical linear regression model of multiple linear regression (MLR). According to the outcome of this study, ANFIS in both training and testing outperformed the remaining models given the R 2 (0.903 and 0.904) and MSE (7.961 and 3.751) values, revealing that any increase in the number of years of taking ART medication will provide HIV/AIDS-treated patients with safer and elongated lives. The contingency results for the CA and the chi-square test did an excellent job of capturing and visualizing the patients on medication, which gave similar results in return, revealing there is a significant association between ART drugs and the age group, while the association between ART drugs and marital status (93.7%) explained a higher percentage of variation compared with the remaining variables.

The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW salinization is paramount to safeguarding the water resources planning and management. This study presents three different scenarios viz.: real field investigation, experimental laboratory analysis (using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), etc.), and artificial intelligence (AI) based metaheuristic optimization (MO) algorithms in Saudi Arabia. The main purpose of this study is to validate the obtained experimental-based analysis using hybrid MO techniques comprising of adaptive neuro-fuzzy inference system (ANFIS) hybridized with genetic algorithm (GA), particle swarm optimization (PSO), and biogeography-based optimization (BBO) for identification of GW salinization in the coastal region of eastern Saudi Arabia. Additionally, ArcGIS 10.3 software generates the prediction map based on ANFIS-GA, ANFIS-PSO, and ANFIS-BBO. Feature selection was assessed using the PSO algorithm, and four indices evaluated the estimated models, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation (SD). The simulated results are based on three variable input combinations, which showed that the ANFIS-PSO (MAE = 0.00439) algorithm had the highest accuracy (99 %), followed by the ANFIS-GA (MAE = 0.00767) and ANFIS-BBO (MAE = 0.0132) algorithms. Besides, Ca 2+ , Na + , Mg 2+ , and Cl - were the most influential parameters. The accuracy also demonstrated the potential reliability of MO algorithms based on spatial distribution mapping. The employed approach proved to be merit and reliable tool for water resources decision-makers in the coastal aquifer of Saudi Arabia. This approach is believed to improve water scarcity as one of the essential targets for Goal 6 of Sustainable Development Vision 2030 and the Kingdom in general., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses ( 18 O and 2 H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl - , Br - ), and seawater fraction ( f sw ) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl - , Br - concentration, and an increasing f sw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low f sw and EC, TDS, Cl - , and Br - , indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18 O and 2 H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources., Competing Interests: The authors declare no conflict of interest.

It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer - Alternating Decision Tree - Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer - Deep Learning Neural Network - Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer - Multilayer Perceptron - Frequency Ratio (ICO-MLP-FR). The first stage of the manuscript consisted of the collection and processing of the geodatabase needed in the present study. The geodatabase comprises a number of 14 flood predictors and 132 known flood locations. The Correlation-based Feature Selection (CFS) method was used in order to assess the prediction capacity of the 14 predictors in terms of flood susceptibility estimation. The training and validation of the three ensemble models constitute the next stage of the scientific workflow. Several statistical metrics and ROC curve method were involved in the evaluation of the model's performance and accuracy. According to ROC curves all the models achieved high performances since their AUC had values above 0.89. ICO-DLNN-FR proved to be the most accurate model (AUC = 0.959). The outcomes of the study can be used to guide future flood risk management and sustainable land-use planning in the designated area., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials' contamination with heavy metals (HMs) was conducted. The material's representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.

This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (K s ) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L 27 orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and K S . Analysis of variance was performed to study the effect of individual parameters on the multiple responses . To predict µ and Ks, SVR was coupled with novel Harris Hawks' optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model evaluation metrics were used to appraise the prediction accuracy of the models. Validation results revealed enhancement under optimal test conditions. Hybrid SVR models indicated superior prediction accuracy to single SVR model. Furthermore, SVR-HHO outperformed SVR-PSO model. It was found that Taguchi Deng, SVR-PSO and SVR-HHO models led to optimization and prediction with low cost and superior accuracy., (© 2022. The Author(s).)

Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70-30% and 80-20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier B.V. on behalf of King Saud University.)

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

Conflict has a negative effect on immunization outcomes leading to epidemics of measles and other vaccine preventable diseases. Borno state, located in the North-Eastern parts of the country, started experiencing insurgency since 2009 by a militant group commonly referred to as "Boko Haram", leading to displacement of numerous persons from their homes. The state government created internally displaced persons (IDPs) camps with an estimated 600,000 persons and over 100,000 children aged 9 - 59 months. We explored the challenges in implementing the measles campaign in 2017 and its effect on measles elimination and control efforts in Borno State., Methods: We conducted a retrospective review of the measles surveillance data from the Integrated disease surveillance and response (IDSR), the early warning alert and response system (EWARS), Measles SIA implementation strategy reports and the Post campaign evaluation survey data., Result: Seven (26%) of the 27 LGAs had security challenges in Borno state in November 2017. Following the measles campaign, 30 EAs were surveyed as part of the post campaign evaluation with a coverage validation of 72%, the lowest reported state level coverage in the country. Reported measles cases in the age cohort <1Year increased in 2018 by 14.3% compared to 2017., Conclusion: Despite the security challenges in Borno state, the 2017 measles SIA was conducted with the post campaign evaluation highlighting some areas of suboptimal performance. There remains a need to review the required strategies for successful implementation in Borno and other states affected by security challenges. The reestablishment of holding camps and vaccination posts at all entry points into IDP camps and host communities to ensure new entrants are screened and vaccinated with the measles vaccines, as has been successful in the polio programme in Nigeria will promotes efforts towards Measles control in the state., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein-Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH., Competing Interests: Conflict of interestThe authors declared no potential conflict of interest that can hinder the submission and acceptance of this article., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

Background: Measles is a vaccine preventable, highly transmissible viral infection that affects mostly children under five years. It has been ear marked for elimination and Nigeria adopted the measles elimination strategies of the World Health Organization (WHO) African region to reduce cases and deaths. This study was done to determine trends in measles cases in Bayelsa state, to describe cases in terms of person and place, identify gaps in the case-based surveillance data collection system and identify risk factors for measles infection., Methods: We carried out a secondary data analysis of measles case-based surveillance data for the period of January 2014 to December 2018 obtained in Microsoft Excel from the State Ministry of Health. Cases were defined according to WHO standard case definitions. We calculated frequencies, proportions, estimated odds ratios (OR), 95% confidence intervals (CI) and multivariate analysis., Results: A total of 449 cases of measles were reported. There were 245(54.6%) males and the most affected age group was 1-4 years with 288(64.1%) cases. Of all cases, 289(9.35%) were confirmed and 70 (48.27%) had received at least one dose of measles vaccine. There was an all-year transmission with increased cases in the 4th quarter of the year. Yenegoa local government area had the highest number of cases. Timeliness of specimen reaching the laboratory and the proportion of specimens received at the laboratory with results sent to the national level timely were below WHO recommended 80% respectively. Predictors of measles infection were, age less than 5 years (AOR: 0.57, 95% CI: 0.36-0.91) and residing in an urban area (AOR: 1.55, 95% CI:1.02-2.34)., Conclusions: Measles infection occurred all-year round, with children less than 5 years being more affected. Measles case-based surveillance system showed high levels of case investigation with poor data quality and poor but improving indicators. Being less than 5 years was protective of measles while living in urban areas increased risk for infection. We recommended to the state government to prioritize immunization activities in the urban centers, start campaigns by the 4th quarter and continue to support measles surveillance activities and the federal government to strengthen regional laboratory capacities.

Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BOD eff ), chemical oxygen demand (COD eff ) and total nitrogen (TN eff ). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BOD eff , the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both COD eff and TN eff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.

We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

Background: Sickle cell disorder is the most important genetic hematological disease that affects people of black African descent. The years of young adulthood present a good opportunity for screening and counseling for this genetic blood disorder., Objectives: To assess effect of health education and provision of free sickle cell haemoglobin screening on knowledge of sickle cell disorder, attitude towards sickle cell haemoglobin screening, and uptake of sickle cell haemoglobin screening among students of a School of Nursing., Methods: Study design was a quasi-experimental noncontrolled study. Self-administered questionnaire was used for pre- and post-intervention data collection. Implemented interventions were seminar on sickle cell disorder combined with free sickle cell haemoglobin screening. The data was analyzed with Epi-info version 3.5.1 statistical software package., Results: Respondents who participated in all the study phases were 104. Mean knowledge score (%) was high (80.9 +/- 22.8%) at baseline and improved significantly to 91.8 +/- 9.4% (p < 0.001) at post intervention. 91.3% were willing to screen fiancée before getting married while 72.1% were willing not to go ahead with marriage if self and fiancée are carriers of sickle cell haemoglobin. Phenotype of the respondents that volunteered to be screened for sickle cell haemoglobin were: A (70.5%), AC (6.8%) and AS (22.7%)., Conclusions: Implemented interventions, seminar on sickle cell disorder combined with free sickle cell haemoglobin screening service yielded significant impact on respondents' knowledge, attitude and uptake of sickle cell haemoglobin screening.

Expression profiles of isocitrate lyase (TbICL), malate synthase (TbMLS) and fructose-1,6-bisphosphatase (TbFBP) from the mycorrhizal ascomycete Tuber borchii were investigated by real-time RT-PCR in fruiting bodies at different stages of maturation. In addition, a time course experiment was set up to determine how the transcription profile of TbICL, TbMLS and TbFBP in axenic-grown mycelia is affected by different carbon sources. The transcript levels of the three genes in the fruiting bodies were all much higher than those measured in the vegetative stage. The investigation on axenic-grown mycelia revealed that the main positive regulator of TbICL and TbMLS gene expression is the availability of acetate and ethanol, while oleic acid is a too complex substrate for the limited degradative capacities of T. borchii. Immunolabelling on axenic-grown mycelia showed a co-localization of TbICL and the peroxisomal marker protein FOX2. This result demonstrated that in T. borchii ICL is compartmentalized in peroxisomes. The high induction of TbICL, TbMLS and TbFBP transcription and the translocation of lipids in fruiting bodies let us hypothesize that glyoxylate cycle and gluconeogenesis are key metabolic pathways in the recycling of existing cell material and the channelling towards the biosynthesis of new cell components during the maturation of fruiting bodies.

Background: The expressed sequence tag M6G10 was originally isolated from a screening for differentially expressed transcripts during the reproductive stage of the white truffle Tuber borchii. mRNA levels for M6G10 increased dramatically during fruiting body maturation compared to the vegetative mycelial stage., Results: Bioinformatics tools, phylogenetic analysis and expression studies were used to support the hypothesis that this sequence, named TbDHN1, is the first dehydrin (DHN)-like coding gene isolated in fungi. Homologs of this gene, all defined as "coding for hypothetical proteins" in public databases, were exclusively found in ascomycetous fungi and in plants. Although complete (or almost complete) fungal genomes and EST collections of some Basidiomycota and Glomeromycota are already available, DHN-like proteins appear to be represented only in Ascomycota. A new and previously uncharacterized conserved signature pattern was identified and proposed to Uniprot database as the main distinguishing feature of this new group of DHNs. Expression studies provide experimental evidence of a transcript induction of TbDHN1 during cellular dehydration., Conclusion: Expression pattern and sequence similarities to known plant DHNs indicate that TbDHN1 is the first characterized DHN-like protein in fungi. The high similarity of TbDHN1 with homolog coding sequences implies the existence of a novel fungal/plant group of LEA Class II proteins characterized by a previously undescribed signature pattern.

A heavy metal tolerant strain of the ericoid mycorrhizal species Oidiodendron maius, isolated from roots of Vaccinium myrtillus growing in soil heavily contaminated with zinc, was previously shown to tolerate high concentrations of zinc and cadmium ions in the growth medium. We have investigated the genetic basis of this fungal strain tolerance to high zinc concentrations by using an untargeted approach. From a cDNA library constructed by using mRNA from Zn-treated O. maius mycelia, 444 clones were randomly selected and 318 were sequenced. Sequence analysis identified 219 unique clones: 117 showed homology to previously identified genes, 26 matched unknown protein coding regions found in other organisms, and 76 were novel. Variation in the gene expression level after a 20-day treatment with high concentrations of Zn was monitored on 130 unigenes by reverse northern blot hybridisation. Sixteen unigenes were shown to be either up- (9) or down- (7) regulated. The putative function of these genes and their involvement in stress tolerance is discussed.

Longevity in faeces, migration to and survival on herbage of mixed strongyle infective larvae (approximately 70% cyathostomes: 30% large strongyles) from experimentally deposited horse faeces was studied in the dry tropical region of North Queensland for up to 2 years. Larvae were recovered from faeces deposited during hot dry weather for a maximum of 12 weeks, up to 32 weeks in cool conditions, but less than 8 weeks in hot wet summer. Translation to herbage was mainly limited to the hot wet season (December-March), except when unseasonal winter rainfall of 40-50 mm per month in July and August allowed some additional migration. Survival on pasture was estimated at 2-4 weeks in the summer wet season and 8-12 weeks in the autumn-winter dry season (April-August). Hot dry spring weather (pre-wet season) was the most unfavourable for larval development, migration and survival. Peak counts of up to 60,000 larvae kg-1 dry herbage were recorded. The seasonal nature of pasture contamination allowed the development of rational anthelmintic control programs based on larval ecology.