A Novel Explainable Artificial Intelligence Model in Image Classification problem

In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time.Comment: Published in the Proceedings of FAIC 202

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

Correction: 3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam

Correction to: Journal of Petroleum Exploration and Production Technology (2022) 12:1–19 https://doi.org/10.1007/s13202-021-01300-

EVALUATION OF PRESCRIBING INDICATORS FOR PEADIATRIC OUTPATIENTS UNDER SIX YEARS OLD IN DISTRICT HOSPITALS OF CAN THO CITY IN THE PERIOD OF 2015-2016

Objective: Examining and comparing the primary and supplementary prescribing indicators in pediatric outpatients under six years old. Methods: We performed a comparative cross-sectional study, over nine months, from September 2015. 800 prescriptions for peadiatric patients under 6 y old were collected at 8 district hospitals in Can Tho city to evaluate the primary and supplementary prescribing indicators. The sample was collected prospectively by the systematic selection, with the interval between the patients is 5. The data was analysed and compared to the standard drug use indicators in developing countries recommended by WHO. Results: Average number of drugs per encounter: 4.1, percentage of drugs prescribed by generic name: 94.2%, percentage of encounters with an antibiotic prescribed: 85.8%, percentage of drugs prescribed from essential drugs list by Ministry of Health: 78.7%, percentage of encounters with a corticoid prescribed: 41.7%, percentage of encounters with a vitamin prescribed: 13.1%, average drug cost per encounter: 37.5 thousands VND, percentage of drug costs spent on antibiotics: 55.2%, percentage of drug costs spent on essential drugs: 75.7%, percentage of drug costs spent on corticoid: 1.9%, percentage of drug costs spent on vitamin: 1.4%. Conclusion: The results of this research have identified some issues in outpatient prescribing, which may lead to intervention studies for evaluating changes in these issues in the outpatient clinic

3D geo-cellular modeling for Oligocene reservoirs: a marginal field in offshore Vietnam

This study focuses on constructing a 3D geo-cellular model by using well-log data and other geological information to enable a deep investigation of the reservoir characteristics and estimation of the hydrocarbon potential in the clastic reservoir of the marginal field in offshore Vietnam. In this study, Petrel software was adopted for geostatistical modeling. First, a sequential indicator simulation (SIS) was adopted for facies modeling. Next, sequential Gaussian simulation (SGS) and co-kriging approaches were utilized for petrophysical modeling. Furthermore, the results of the petrophysical models were verified by a quality control process before determining the in-place oil for each reservoir in the field. Multiple geological realizations were generated to reduce the geological uncertainty of the model assessment for the facies and porosity model. The most consistent one would then be the best candidate for further evaluation. The porosity distribution ranged from 9 to 22%. The original oil place of clastic reservoirs in the marginal field was 50.28 MMbbl. Ultimately, this research found that the marginal field could be considered a potential candidate for future oil and gas development in offshore Vietnam

Application of the cut-off projection to solve a backward heat conduction problem in a two-slab composite system

The main goal of this paper is applying the cut-off projection for solving one-dimensional backward heat conduction problem in a two-slab system with a perfect contact. In a constructive manner, we commence by demonstrating the Fourier-based solution that contains the drastic growth due to the high-frequency nature of the Fourier series. Such instability leads to the need of studying the projection method where the cut-off approach is derived consistently. In the theoretical framework, the first two objectives are to construct the regularized problem and prove its stability for each noise level. Our second interest is estimating the error in -norm. Another supplementary objective is computing the eigen-elements. All in all, this paper can be considered as a preliminary attempt to solve the heating/cooling of a two-slab composite system backward in time. Several numerical tests are provided to corroborate the qualitative analysis.Peer reviewe

Application of Conductive Concrete as a Microbial Fuel Cell to Control H2S Emission for Mitigating Sewer Corrosion

Localized biogenic corrosion and extrication of annoying odors caused by hydrogen sulfide (H2S) have long been a big problem in the management of urban sewer systems. H2S emission control in sewers via chemically or biologically normal oxidation processes has also been investigated extensively and is costly. The objective of this work was to develop a new technology to mitigate the concentration of H2S in sewer pipes using conductive concrete. Experimental results after 66 days show that the concentration of hydrogen sulfide significantly decreased when conductive concrete was used as a microbial fuel cell. Both ordinary Portland cement and conductive concrete were utilized for the target experiment. Elemental sulfur was observed in the coating sludge of conductive concrete, whereas this trend was not observed for ordinary Portland cement. These observations demonstrate that conductive concrete provides an electron pathway from deposited sludge in the bottom of sewer pipes to oxygen dissolved in surface water electrons generated from hydrogen sulfide oxidation in an anaerobic environment via conductive concrete. Finally, regarding the mechanism responsible for hydrogen sulfide oxidation, chemical oxidation was the dominant process, and biological processes did not play a significant role

PRELIMINARY DETERMINATION OF POLYCYCLIC AROMATIC HYDROCARBON (PAHS) IN AIR ENVIRONMENT AT MAJOR TRAFFIC JOINTS OF HANOI

Joint Research on Environmental Science and Technology for the Eart

Angiotensin- I- converting enzyme (ACE) inhibitory peptides from Pacific cod skin gelatin using ultrafiltration membranes

Angiotensin- I- converting enzyme (ACE) is crucial in the control of hypertension and the development of type- 2 diabetes and other diseases associated with metabolic syndrome. The aim of this work was to utilize Pacific cod skin to purify ACE inhibitory peptides. First, gelatin was extracted from Pacific cod skin and hydrolyzed with several enzymes (pepsin, papain, α-chymotrypsin, trypsin, neutrase, and alcalase). The pepsin hydrolysate showed the strongest ACE inhibitory effect and was further fractionated into different ranges of molecular weight (10\ua0kDa) using ultrafiltration (UF) membranes. The peptic hydrolysate below 1\ua0kDa resulted in two potent ACE inhibitory peptides, GASSGMPG (662\ua0Da) and LAYA (436\ua0Da), with IC values (concentration required to decrease the ACE activity by 50%) of 6.9 and 14.5\ua0μM, respectively. Moreover, to explore the interaction between the peptides and ACE molecule, the tertiary structure of ACE and docking simulation to the peptides were predicted using Docking Server. Pacific cod peptides can be used as functional food ingredients to prevent hypertension and its related diseases

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), least squares support vector machine (LSSVM), and multivariate adaptive regression spline (MARS) models, were employed to reconstruct the missing daily-averaged discharge in a mega-delta from 1980 to 2015 using upstream-downstream multi-station data. The performance and accuracy of each ML model were assessed and compared with the stage-discharge rating curves (RCs) using four statistical indicators, Taylor diagrams, violin plots, scatter plots, time-series plots, and heatmaps. Model input selection was performed using mutual information and correlation coefficient methods after three data pre-processing steps: normalization, Fourier series fitting, and first-order differencing. The results showed that the ML models are superior to their RC counterparts, and MARS and RF are the most reliable algorithms, although MARS achieves marginally better performance than RF. Compared to RC, MARS and RF reduced the root mean square error (RMSE) by 135% and 141% and the mean absolute error by 194% and 179%, respectively, using year-round data. However, the performance of MARS and RF developed for the climbing (wet season) and recession (dry season) limbs separately worsened slightly compared to that developed using the year-round data. Specifically, the RMSE of MARS and RF in the falling limb was 856 and 1, 040 m3/s, respectively, while that obtained using the year-round data was 768 and 789 m3/s, respectively. In this study, the DT model is not recommended, while the GPR and SVR models provide acceptable results