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The dissolved nitrate is one of the major essential nutrients for primary production in the tropical ocean and it is brought to the surface though mixing. The depth of nitracline determines how much of nitrate enters to the upper ocean through mixing. The depth of nitracline is traditionally estimated using nitrate concentrations measured at standard depths that introduces significant error due to interpolation of data. Based nitrate profiles measured at 5 m interval using nitrate sensors onboard Argo, the exact depth of nitracline was derived in the Bay of Bengal that displayed a significant linear relationship with depth of 26°C isotherm (D26). Based on climatological D26, the temporal and spatial variations in the depth of nitracline was estimated for the entire Bay of Bengal. The depth of nitracline varied between 5 and 80 m with large spatial and temporal variability in the Bay of Bengal and it is 5–20 m deeper than simulations of numerical models. The relationship between the depth of nitracline and photic zone integrated primary production indicates that 7.5 ± 3 mgC m−2 d−1 of primary production increases due to shallowing of 1 m of depth of nitracline. Therefore, models seem to be over estimating the photic zone integrated primary production by 5%–25% in the Bay of Bengal. The numerical models may improve the simulation of primary production and carbon cycling by accounting the accurate estimation of depth of nitracline in the model initialization. Plain Language Summary: In the tropical ocean, nitrate mainly controls the primary production and identifying the depth of nitracline, where nitrate concentration was 1 μM from that of surface, is important in simulating primary production by numerical models. All the numerical models are using nutrietns profiles collected at the standard depth and estimated depth of nitracline. Since the data collected at standard depth, and interpolation between depth may result in large error in the computation of nitracline depth. We used high depth resolution nitrate data collected by sensors and estimated nitracline depth and the same compared with modeled data. It was observed that models are estimating shallow nitracline than actual as a result higher biological response is simulated than reality. This exercise would be useful in improving model simulation in future. Key Points: The depth of nitracline is strongly controlled by depth of 20°C isothermDeeper nitracline depth was noticed during spring and shallowers during summerModels estimate shallower nitracline depth by 5–20 m than measured [ABSTRACT FROM AUTHOR]

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods. [ABSTRACT FROM AUTHOR]

Objectives: Automated results for determining colistin susceptibility tests are unreliable, micro broth dilution requires expertise, and CBDE has limited dilutions. International Consensus Document of 2022 suggested that agar dilution was unacceptable due to varying results in the literature. Methods: The study was designed to evaluate the agar dilution method for colistin susceptibility in CRE isolates compared to CBDE. In the study, 108 carbapenem-resistant isolates were tested for Colistin susceptibility by Microbroth Dilution, agar dilution, and colistin broth disc elution. The comparisons were made using various statistical parameters. Results: The results of the agar dilution method revealed an essential agreement of 75 % and a categorical agreement of 92.5 %. The method showed a sensitivity of 75 % and a specificity of 97.7 %. The positive and negative predictive values were 88.2 % and 94.5 %, respectively. Youden's index was 0.727, indicating a moderate level of accuracy. Meanwhile, CBDE diagnostic accuracy tests were better, with Youden's index at 0.939. Conclusions: While CBDE demonstrated better accuracy parameters, it did not offer a broader MIC range. In contrast, agar dilution showed reasonable specificity and reliability for isolates with high MIC. Therefore, we propose using CBDE for screening and agar dilution as a supplementary test in the approach to colistin susceptibility.

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.