Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector functional link (dRVFL), general regression neural network (GRNN), multivariate adaptive regression spline (MARS), online sequential extreme learning machine (OSELM) and extreme gradient boosting decision tree (XGBoost) when compared with observed river salinity data. Also, the KELM‐BSSADE model effectively identified optimal inputs through the Boruta‐XGBoost (B‐XGB) feature selection method. Four metaheuristic‐based KELM models were developed, utilizing grey wolf optimizer, whale optimization, slime mould algorithm and equilibrium optimizer, further illustrating the capability of KELM‐BSSADE in estimating potential salinity in river water. By accurately estimating potential salinity, KELM‐BSSADE can assist in optimizing irrigation practices, ensuring that agricultural demands are met while minimizing the risk of salinity‐related crop damage.
The risk of breast cancer development is believed to be attributed to the alterations of a number of key biological components. Within this context, elevated levels of some chemokines that act as growth factors and can promote cancer development. The current study was designed to evaluate CXCL3 (a chemokine C-X-C Motif Ligand 3) and leptin (a peptide hormone synthesized by adipose tissue with cytokine activity) serum of Iraqi breast cancer patients in comparison to healthy controls. A total of 90 participants consisted of 60 patients diagnosed with breast cancer and 30 healthy women as control group were enrolled into this case-control study. Venous blood samples were collected from all participants to evaluate CXCL3 and leptin serum levels
... Show MoreBackground: The altered status of some essential trace elements observed in diabetes could have deleterious influences on the health of the diabetics. Objectives: To estimate and study the potential role of serum Selenium in type 1, type 2 diabetics and healthy subjects; and its relation with lipid profile and glycemic index. Methods: A case control designed study was carried out at the National Diabetes Center (NDC) / Al-Mustansiria University; on a total of 94 participants formed of 32 type 1 diabetics, 32 type 2 diabetics and 30 healthy control participants. Data collected about age, sex and BMI; also, blood samples examined for FPG, HbA1C, serum total cholesterol, HDL cholesterol, non-HDL cholesterol, serum triglyceride and sera were
... Show MoreObjectives: This study aims to broaden our knowledge of the role of eDNA in bacterial biofilms and antibiotic-resistance gene transfer among isolates. Methods: Staphylococcus aureus, E. coli, and Pseudomonas aeruginosa were isolated from different non-repeated 170 specimens. The bacterial isolates were identified using morphological and molecular methods. Different concentrations of genomic DNA were tested for their potential role in biofilms formed by study isolates employing microtiter plate assay. Ciprofloxacin resistance was identified by detecting a mutation in gyrA and parC. Results: The biofilm intensity significantly decreased (P < 0.05) concerning S. aureus isolates and insignificantly (P > 0.05) concernin
... Show MoreMixed-effects conditional logistic regression is evidently more effective in the study of qualitative differences in longitudinal pollution data as well as their implications on heterogeneous subgroups. This study seeks that conditional logistic regression is a robust evaluation method for environmental studies, thru the analysis of environment pollution as a function of oil production and environmental factors. Consequently, it has been established theoretically that the primary objective of model selection in this research is to identify the candidate model that is optimal for the conditional design. The candidate model should achieve generalizability, goodness-of-fit, parsimony and establish equilibrium between bias and variab
... Show MorePrediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay
... Show MoreThe successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
... Show MoreStable isotope (?18O, ?D) values were determined along with the chemical compositions at 10 different locations along the Tigris river between Baghdad-Ammara cities of Iraq. The physico-chemical parameters and isotopic data were measured. The sampling site represents 34 % of total Tigris river in the republic of Iraq. The systematically increased in values of stable isotope as move from the downstream of the river and the most significantly appears at Kut lake. This increase occurs as a result of several factors, viz. (a) evaporation occurs low water level in the river and its tributaries, and (b) return flow water to the river from irrigation water in groundwater systems. The change in ion distribution and in the isotopic values related di
... Show MoreAn environmental study conducted on diatoms in Al Yusifiya river beyond its branching from Euphrates river. Four sites were selected along the river for the period from march 2013 to September 2013. The present study involved the measurement of physicochemical parameters, also the qualitative and quantities of diatoms. The studied parameters values ranged as follows: 19-44Cº and 16-30 Cº for air and water temperature respectively, 6.9-8.7, 595-1248 µS/cm, 6.4-8.0 mg/l for pH, electric conductivity and dissolved oxygen respectively. A total of 74 taxa were recorded for diatoms, where the pinnate diatom was the predominant and recorded 64 taxa while 10 taxa for centric diatoms. The total number of diatoms was 1197.55*104 cell /l. The tota
... Show MoreA total of 72 individuals of genus Pristina were sorted from aquatic plant, Ceratophyllum demersum L., and filamentous algae collected from three sites on Tigris River at Baghdad including: Al-Sarafiya area (S1), Al- Jadiriyah area (S2), and Al- Za´afaraniya area (S3). Four species were identified including P. longiseta, P. aequiseta, P. proboscidea and P. foreli, with percentags of 51.7 , 36.4, 1.1, and 10.5 % respectively. The first two species found in all sites , while , P. proboscidea found only in S1 and P. foreli only in S2.