373 research outputs found
Temporal and spatial variations of dissolved oxygen, pH, and chlorophyll a in waste stabilization pond system at high altitude
Generalised linear models for prediction of dissolved oxygen in a waste stabilisation pond
Due to simplicity and low costs, waste stabilisation ponds (WSPs) have become one of the most popular biological wastewater treatment systems that are applied in many places around the globe. Increasingly, pond modelling has become an interesting tool to improve and optimise their performance. Unlike process-driven models, generalised linear models (GLMs) can deliver considerable practical values in specific case studies with limited resources of time, data and mechanistic understanding, especially in the case of pond systems containing vast complexity of many unknown processes. This study aimed to investigate the key driving factors of dissolved oxygen variability in Ucubamba WSP (Ecuador), by applying and comparing numerous GLMs. Particularly, using different data partitioning and cross-validation strategies, we compared the predictive accuracy of 83 GLMs. The obtained results showed that chlorophyllahad a strong impact on the dissolved oxygen (DO) level near the water surface, while organic matter could be the most influential factor on the DO variability at the bottom of the pond. Among the 83 models, the optimal models were pond- and depth-specific. Specifically, among the ponds, the models of MPs predicted DO more precisely than those of facultative ponds; while within a pond, the models of the surface performed better than those of the bottom. Using mean absolute error (MAE) and symmetric mean absolute percentage error (SMAPE) to represent model predictive performance, it was found that MAEs varied in the range of 0.22-2.75 mg L(-1)in the training period and 0.74-3.54 mg L(-1)in the validation period; while SMAPEs were in the range of 2.35-38.70% in the training period and 10.88-71.62% in the validation period. By providing insights into the oxygen-related processes, the findings could be valuable for future pond operation and monitoring
Exploring the influence of meteorological conditions on the performance of a waste stabilization pond at high altitude with structural equation modeling
Algal photosynthesis plays a key role in the removal mechanisms of waste stabilization ponds (WSPs), which is indicated in the variations of three parameters, dissolved oxygen, pH, and chlorophyll a. These variations can be considerably affected by extreme climatic conditions at high altitude. To investigate these effects, three sampling campaigns were conducted in a high-altitude WSP in Cuenca (Ecuador). From the collected data, the first application of structure equation modeling (SEM) on a pond system was fitted to analyze the influence of high-altitude characteristics on pond performance, especially on the three indicators. Noticeably, air temperature appeared as the highest influencing factors as low temperature at high altitude can greatly decrease the growth rate of microorganisms. Strong wind and large diurnal variations of temperature, 7-20 degrees C, enhanced flow efficiency by improving mixing inside the ponds. Intense solar radiation brought both advantages and disadvantages as it boosted oxygen level during the day but promoted algal overgrowth causing oxygen depletion during the night. From these findings, the authors proposed insightful recommendations for future design, monitoring, and operation of high-altitude WSPs. Moreover, we also recommended SEM to pond engineers as an effective tool for better simulation of such complex systems like WSPs
Development of a diagnostic scar marker for Vibrio shilonii caused acute hepatopancreatic necrosis disease in whiteleg shrimp
Background: In a previous report, we showed that Vibrio shilonii was found on whiteleg shrimp (Litopenaeus vannamei) with acute hepatopancreatic necrosis disease in Thua Thien Hue province, Vietnam. This study was performed to develop a diagnostic molecular marker generated by random amplified polymorphic DNA (RAPD) for V. shilonii rapid detection.Methods: Pathogen Vibrio spp. were isolated from shrimps and fishes, and were identified by 16S rRNA sequencing. Genetic diversity of Vibrio strains was analysis by RAPD technique. Specific PCR product for V. shilonii was cloned and sequenced. SCAR marker was developed from specific PCR product.Result: Twenty random primers were evaluated for RAPD to identify DNA polymorphisms between Vibrio species. The random primer OPN-06 generated a 468-bp DNA fragment specific for V. shilonii. This was then converted into a sequence-characterized amplified region (SCAR) marker designated N6-441.Conclusion: Specific primers (Vshi-441F/Vshi-441R) amplified a unique DNA fragment in all V. shilonii isolates but not in the other Vibrio spp. This PCR assay showed significantly sensitive to the target DNA and reliably for the amplification the V. shilonii genome.Keywords: AHPND; RAPD; SCAR; Vibrio shilonii; Vietna
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation
Constructing a robust model that can effectively generalize to test samples
under distribution shifts remains a significant challenge in the field of
medical imaging. The foundational models for vision and language, pre-trained
on extensive sets of natural image and text data, have emerged as a promising
approach. It showcases impressive learning abilities across different tasks
with the need for only a limited amount of annotated samples. While numerous
techniques have focused on developing better fine-tuning strategies to adapt
these models for specific domains, we instead examine their robustness to
domain shifts in the medical image segmentation task. To this end, we compare
the generalization performance to unseen domains of various pre-trained models
after being fine-tuned on the same in-distribution dataset and show that
foundation-based models enjoy better robustness than other architectures. From
here, we further developed a new Bayesian uncertainty estimation for frozen
models and used them as an indicator to characterize the model's performance on
out-of-distribution (OOD) data, proving particularly beneficial for real-world
applications. Our experiments not only reveal the limitations of current
indicators like accuracy on the line or agreement on the line commonly used in
natural image applications but also emphasize the promise of the introduced
Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend
to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023,
Workshop on robustness of zero/few-shot learning in foundation model
Investigation of Sodium Manganese Oxide Nanowires Synthesized by Hydrothermal Method for Alkaline Ion Battery
Sodium Manganese Oxide (NaxMnO2) has attracted much attention as cathode materials for alkaline ion battery due to the ability of fast charge and discharge ion Na+, in particular in nanoscale. We report on the synthesis of NaxMnO2 nanowires via hydrothermal synthesis route from Mn2O3 and NaOH solution. The morphological observation indicates that the obtained Na0.44MnO2 nanowires with diameters of about 20-30 nm, length up to several micrometers were formed by this process. The electrochemical properties of fabricated materials were investigated by means of cyclic voltammetry technique and show that Sodium Manganese Oxide (NaxMnO2) is a promising material in the field of research and fabrication alkaline ion battery
A closer look on spatiotemporal variations of dissolved oxygen in waste stabilization ponds using mixed models
Dissolved oxygen is an essential controlling factor in the performance of facultative and maturation ponds since both take many advantages of algal photosynthetic oxygenation. The rate of this photosynthesis strongly depends on the time during the day and the location in a pond system, whose roles have been overlooked in previous guidelines of pond operation and maintenance (O&M). To elucidate these influences, a linear mixed effect model (LMM) was built on the data collected from three intensive sampling campaigns in a waste stabilization pond in Cuenca, Ecuador. Within two parallel lines of facultative and maturation ponds, nine locations were sampled at two depths in each pond. In general, the output of the mixed model indicated high spatial autocorrelations of data and wide spatiotemporal variations of the oxygen level among and within the ponds. Particularly, different ponds showed different patterns of oxygen dynamics, which were associated with many factors including flow behavior, sludge accumulation, algal distribution, influent fluctuation, and pond function. Moreover, a substantial temporal change in the oxygen level between day and night, from zero to above 20 mg O-2.L-1, was observed. Algal photosynthetic activity appeared to be the main reason for these variations in the model, as it was facilitated by intensive solar radiation at high altitude. Since these diurnal and spatial patterns can supply a large amount of useful information on pond performance, insightful recommendations on dissolved oxygen (DO) monitoring and regulations were delivered. More importantly, as a mixed model showed high predictive performance, i.e., high goodness-of-fit (R-2 of 0.94), low values of mean absolute error, we recommended this advanced statistical technique as an effective tool for dealing with high autocorrelation of data in pond systems
Structure and Electrochemical Impedance of LiNiMnO
Ni-substitution spinel LiNixMn2−xO4 (x = 0, 0.1, 0.2) materials were synthesized by the sol--gel method. The structure and morphology of the samples were characterized by the X-ray diffraction (XRD) and the scanning electron microscopy. The ac conduction of the materials was investigated by electrochemical impedance spectroscopy (EIS) measurements. The refinement results showed that the substitution of Ni decreased the lattice constant and Mn--O distance, while increased Li--O bond length and 16c octahedral volume. The EIS results confirmed the decrease of conductivity with increasing Ni substitution content. Based on XRD and EIS results, the relationship between the crystal structure and electrochemical behavior of the materials was discussed and explained
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