Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl-, pH, and HCO3-). We applied nine different decision tree-based models and evaluated their prediction performances. The models were trained using 13 input variables: weather (2), hydrogeological conditions (4), water levels (3), groundwater usage (2), and relative distance from water sources (2). Subsequently, by employing model interpretation techniques, we quantified the significance of factors within the model prediction. Performance evaluations of the ML models demonstrated that the Extra Trees model exhibited superior performance and demonstrated generalization capabilities in predicting Cl- concentration, whereas the Bagging and Random Forest models outperformed the other models in predicting pH and HCO3- concentration. The coefficients of determination were determined to be 0.94, 0.67, and 0.78 for Cl-, pH, and HCO3-, respectively Additionally, the model interpretation effectively identified significant factors that depended on the target variables and aquifers. In particular, salinity indicators and aquifers that were strongly influenced by the artificial usage of groundwater were identified. Therefore, our research, which provides accurate spatial predictions and interpretations of groundwater salinity in the MD, has the potential to establish a foundation for formulating effective groundwater management policies to control groundwater salinization.

Psychometric Assessment of the Persian Translation of the Interpersonal Mindfulness Scale With Undergraduate Students.

Interpersonal mindfulness is a construct that significantly contributes to social interaction. To date, no validated measure assessing interpersonal mindfulness has been developed in Iran. Therefore, the aim of this study was to translate and validate the Interpersonal Mindfulness Scale (IMS) among Iranian undergraduate students. Participants in the study (370 undergraduate students; 220 females) from the Azad University completed the translated IMS, the Five Facet Mindfulness Questionnaire, and the Inventory of Interpersonal Problems Scale. The translated measure demonstrated acceptable face validity. All items had acceptable content validity and were deemed essential to the scale. The results of a Confirmatory Factor Analysis (CFA) confirmed a scale with four subscales (presence, awareness of self and others, non-judgmental acceptance, and non-reactivity), with acceptable internal consistency. The findings support the psychometric properties of the Persian translated Interpersonal Mindfulness Scale, which could be used to measure interpersonal mindfulness among undergraduate students in Iran.

Optical and Electrochemical Aptasensors Developed for the Detection of Alpha-Fetoprotein.

Early diagnosis of hepatocellular carcinoma (HCC), a leading cause of cancer mortality, is decisive for successful treatment of this type of cancer and increasing the patients' survival rate. Alpha-fetoprotein (AFP) is a glycoprotein that has been currently employed as a potential serological biomarker for determination of HCC and several other cancers. Achieving highly sensitive and specific detection of this biomarker is an effective strategy to inhibit developing issues caused by the cancer. Though, traditional procedures cannot meet the requirements due to the technical drawbacks. Recently, growing number of aptamer-based biosensors (aptasensors) attracted important attention as superior diagnostic tools because of their unique properties such as high stability, target versatility and remarkable affinity and selectivity. Nanomaterials, which broadly employed in the structure of these aptasensors, can considerably enhance the detection limit and sensitivity of analytes determination. Therefore, this review selectively investigated the recent progresses in several different optical and electrochemical aptasensors and nano-aptasensors designed for AFP assay.