Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model.

Accurate prediction of the dissolved oxygen level (DOL) is important for enhancing environmental conditions and facilitating water resource management. However, the irregularity and volatility inherent in DOL pose significant challenges to achieving precise forecasts. A single model usually suffers from low prediction accuracy, narrow application range, and difficult data acquisition. This study proposes a new weighted model that avoids these problems, which could increase the prediction accuracy of the DOL. The weighting constructs of the proposed model (PWM) included eight neural networks and one statistical method and utilized Young's double-slit experimental optimizer as an intelligent weighting tool. To evaluate the effectiveness of PWM, simulations were conducted using real-world data acquired from the Tualatin River Basin in Oregon, United States. Empirical findings unequivocally demonstrated that PWM outperforms both the statistical model and the individual machine learning models, and has the lowest mean absolute percentage error among all the weighted models. Based on two real datasets, the PWM can averagely obtain the mean absolute percentage errors of 1.0216%, 1.4630%, and 1.7087% for one-, two-, and three-step predictions, respectively. This study shows that the PWM can effectively integrate the distinctive merits of deep learning methods, neural networks, and statistical models, thereby increasing forecasting accuracy and providing indispensable technical support for the sustainable development of regional water environments.

ARTI (Adaptive Radio Tomographic Imaging): One New Adaptive Elliptical Weighting Model Combining with Tracking Estimates.

Imaging and tracking performance suffers from the mismatch between the model and the measurements in an adaptive radio tomographic imaging system. In this paper, a model-based approach is reviewed and a new adaptive elliptical weighting model is proposed, in which the coverage of ellipse and the voxels weightings can adaptively match the actual environments, and the Savitzky⁻Golay smoothing filter is presented to eliminate the influence of measurement noise and multipath interference. In our proposed model, the optimal coverage of ellipse and weightings can be obtained from voxel weightings distribution inside the ellipse and pseudo-position area and trailing phenomenon. Finally, the development efforts are evaluated and validated with real experiments conducted in indoor environments for a moving target. The results have shown that the proposed algorithm can improve the accuracy of image and location estimates compared with the normalized weight model and the const-eccentricity weight model.

Identifying the causal relationship between sedentary behavior and heart failure: Insights from a Mendelian randomization study and mediation analysis.

BACKGROUND: Observational studies have revealed that a lack of physical exercise may be linked to a higher risk of heart failure (HF). Here, the causal relationship between sedentary behavior (SB) and HF was investigated using Mendelian randomization (MR). HYPOTHESIS: SB was considered as an important risk factor of HF. METHODS: Single nucleotide polymorphisms with a genome-wide statistical significance threshold of <5 × 10-8 among the SB-proxied phenotypes (TV screen time, computer use, and driving) from genome-wide association study (GWAS) datasets were identified as instrumental variables (IVs). The MR study was performed using the inverse-variance weighting (IVW) model as a primary standard to evaluate causal relationships. Simultaneously, MR-Egger regression, weighted median, and maximum likelihood models were used as supplements. Sensitivity analysis, consisting of a heterogeneity and horizontal pleiotropy test, was performed using Cochran's Q, MR-Egger intercept, and MR-PRESSO tests to ensure the reliability of conclusions. RESULTS: The IVW model results showed that increased TV screen time correlated with a higher genetic susceptibility for HF in both HF-associated GWAS datasets, which was also supported by weighted median and maximum likelihood model results. The odds ratios with 95% confidence intervals were 1.418 (1.182-1.700) and 1.486 (1.136-1.943), respectively. Although the results of Cochran's Q test indicated certain heterogeneity among the IVs. The MR-Egger intercept and MR-PRESSO tests suggested no horizontal pleiotropy and verified the reliability of the conclusion. CONCLUSIONS: This MR study identified that increased TV screen time may predispose individuals to the development of HF.

Evaluation of selected pharmaceuticals and personal care products in water matrix using ion trap mass spectrometry: A simple weighted calibration curve approach.

A novel analytical method is presented for 12 target pharmaceutical and personal care products (PPCPs), belonging to different classes like antibiotics, non-steroid anti-inflammatory drugs, parabens, UV-filters, plasticizer, and antibacterials. The method development comprises of solid-phase extraction (SPE) with lipophilic-hydrophilic material balanced Oasis HLB cartridge, followed by reverse-phase liquid chromatography interfaced to linear ion trap tandem mass spectrometry (LC-MS/MS) with electrospray ionization. Chromatographic separation was achieved with a gradient elution of 25 min run time using 5 mM ammonium acetate buffer with pH adjustment using acetic acid. In addition, cost effective organic solvent with buffer used together as the mobile phase with Chromatopak C18 column (150 mm × 4 mm, 5-µm,) in negative ionization mode. Recoveries ranged from 61.74 % to 119.89 % for most of the compounds. Matrix-matched calibration curves were used for counterbalancing the matrix effects for all the analytes, and ibuprofen D3 internal standard was used for assessing the effectiveness of extraction technique and monitoring the recovery of sample analysis. Simple empirical weighted linear regression curve technique was adopted practically for each analysis in enhancing the analyte accuracy at lower quantification level. The 1/x2 model was selected as the best suitable model for quantification of analytes, which can be evaluated by deviation from back-calculated concentration in terms of percentage relative error (%RE). Weighted calibration curves with regression value for most of the compounds were ≥ 0.98, except triclosan with a regression value ≥ 0.93. Precision showed as % relative standard deviation (%RSD) were always below 15.0 %. Accuracy-test was evaluated by the statistical one-sample t-test and no significant difference was observed between nominal and experimental value. The limit of quantification (LOQ) ranged from 3.0 ng/mL (BP1) to 1000 ng/mL (naproxen). Finally, the validated method was used for the first time to determine target analytes in surface water samples collected from Arkavathi river flowing across southern India's Bengaluru city.