Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network

Sludge bulking is the most common solids settling problem in wastewater treatment plants, which is caused by the excessive growth of filamentous bacteria extending outside the flocs, resulting in decreasing the wastewater treatment efficiency and deteriorating the water quality in the effluent. Previous studies using molecular techniques have been widely used from the microbiological aspects, while the mechanisms have not yet been completely understood to form the deterministic cause-effect relationship. In this study, system identification techniques based on the analysis of the inputs and outputs of the activated sludge system are applied to the data-driven modeling. Principle component regression (PCR) and artificial neural network (ANN) were identified using the data from Chongqing wastewater treatment plant (CQWWTP), including temperature, pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SSs), ammonia (NH4+), total nitrogen (TN), total phosphorus (TP), and mixed liquor suspended solids (MLSSs). The models were subsequently used to predict the sludge volume index (SVI), the indicator of the bulking occurrence. Comparison of the results obtained by both models is also presented. The results showed that ANN has better prediction power (R2=0.9) than PCR (R2=0.7) and thus provides a useful guide for practical sludge bulking control

SYSTEMS CANCER BIOLOGY AND THE CONTROLLING MECHANISMS FOR THE J-SHAPED CANCER DOSE RESPONSE: TOWARDS RELAXING THE LNT HYPOTHESIS

The hormesis phenomena or J-shaped dose response have been accepted as a com- mon phenomenon regardless of the involved biological model, endpoint measured and chemical class/physical stressor. This paper first introduced a mathematical dose response model based on systems biology approach. It links molecular-level cell cycle checkpoint control information to clonal growth cancer model to predict the possible shapes of the dose response curves of Ionizing Radiation (IR) induced tumor transformation frequency. J-shaped dose response curves have been captured with consideration of cell cycle checkpoint control mechanisms. The simulation results indicate the shape of the dose response curve relates to the behavior of the saddle-node points of the model in the bifurcation diagram. A simplified version of the model in previous work of the authors was used mathematically to analyze behaviors relating to the saddle-node points for the J-shaped dose response curve. It indicates that low-linear energy transfer (LET) is more likely to have a J-shaped dose response curve. This result emphasizes the significance of systems biology approach, which encourages collaboration of multidiscipline of biologists, toxicologists and mathematicians, to illustrate complex cancer-related events, and confirm the biphasic dose-response at low doses

Experimental study and mass transfer modelling for extractive desulfurization of diesel with ionic liquid in microreactors

Conventional hydrodesulfurization technology was limited to treat aromatic heterocyclic sulfur compounds in ultralow-sulfur diesel. Extractive desulfurization (EDS) using ionic liquid (IL) exhibited good performance to address these issues, except for its long extraction time (15-40 min). To address this, microreactor was adopted to intensify the IL-based EDS, where dibenzothiophene was extracted from model diesel (MD) as the continuous phase to 1-butyl-3-methylimidazolium tetrafluoroborate as the dispersed phase under segmented flow (which appeared preferably at capillary numbers lower than 0.01). The effects of temperature, residence time and flow rate ratio on the desulfurization efficiency were investigated. The extraction equilibration time could be shortened from more than 15 min in conventional batch extractors to 120 s in microreactors. The extraction process was modeled according to the two-film model applied within a unit cell of the segmented flow, where the mass transfer resistance was considered primarily on the film side of the IL droplet. The mechanism for the improved EDS performance at higher temperatures or larger IL to MD flow ratios was investigated and validated, which was related to the significant increase in the diffusion coefficient or the specific interfacial area. These findings may shed important insights into the precise manipulation of IL-based EDS for a better process design and reactor optimization

ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices.Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.Comment: 11 pages, 9 figure

DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing). The project website and code can be found at https://weijiawu.github.io/DatasetDM_page/ and https://github.com/showlab/DatasetDM, respectivel

Investigation on the corrosion resistance of 3003 aluminum alloy in acidic salt spray under different processing states

3003 aluminum alloy exhibits commendable corrosion resistance, ease of processing, and good formability, rendering it extensively utilized across many industrial sectors. In this study, the corrosion behavior of 3003 aluminum alloy in a homogenized state and after hot extrusion deformation in an acidic salt spray environment for different times was studied. The microstructure of the 3003 aluminum alloy in the homogenized state and after hot extrusion was characterized using scanning electron microscopy (SEM), optical microscope (OM), laser scanning confocal microscope (LSCM) etc., while electrochemical methods were employed to study the difference in corrosion resistance between these two states. The results show that corrosion pits on the surface of the homogenized 3003 aluminum alloy increase with time, and corrosion extends along the second phase arrangement, while the hot extruded 3003 aluminum alloy mainly exhibits corrosion pit extension. The grain size of the homogenized 3003 aluminum alloy is larger than that of the hot extruded state, and the second phase is distributed in a reticular pattern. Hot extrusion deformation ensures not only a uniform distribution of the second phase in the 3003 aluminum alloy but also a reduced grain size, an increased grain boundary density, a heightened electrochemical activity in acidic environments, and an augmented pitting density. Compared with the homogenized 3003 aluminum alloy, the pitting density, maximum pitting depth, and weight loss of the hot extruded state are increased

PMVT: a lightweight vision transformer for plant disease identification on mobile devices

Due to the constraints of agricultural computing resources and the diversity of plant diseases, it is challenging to achieve the desired accuracy rate while keeping the network lightweight. In this paper, we proposed a computationally efficient deep learning architecture based on the mobile vision transformer (MobileViT) for real-time detection of plant diseases, which we called plant-based MobileViT (PMVT). Our proposed model was designed to be highly accurate and low-cost, making it suitable for deployment on mobile devices with limited resources. Specifically, we replaced the convolution block in MobileViT with an inverted residual structure that employs a 7×7 convolution kernel to effectively model long-distance dependencies between different leaves in plant disease images. Furthermore, inspired by the concept of multi-level attention in computer vision tasks, we integrated a convolutional block attention module (CBAM) into the standard ViT encoder. This integration allows the network to effectively avoid irrelevant information and focus on essential features. The PMVT network achieves reduced parameter counts compared to alternative networks on various mobile devices while maintaining high accuracy across different vision tasks. Extensive experiments on multiple agricultural datasets, including wheat, coffee, and rice, demonstrate that the proposed method outperforms the current best lightweight and heavyweight models. On the wheat dataset, PMVT achieves the highest accuracy of 93.6% using approximately 0.98 million (M) parameters. This accuracy is 1.6% higher than that of MobileNetV3. Under the same parameters, PMVT achieved an accuracy of 85.4% on the coffee dataset, surpassing SqueezeNet by 2.3%. Furthermore, out method achieved an accuracy of 93.1% on the rice dataset, surpassing MobileNetV3 by 3.4%. Additionally, we developed a plant disease diagnosis app and successfully used the trained PMVT model to identify plant disease in different scenarios

Balancing Logit Variation for Long-tailed Semantic Segmentation

Semantic segmentation usually suffers from a long-tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get squeezed into a narrow area in the feature space. Towards a balanced feature distribution, we introduce category-wise variation into the network predictions in the training phase such that an instance is no longer projected to a feature point, but a small region instead. Such a perturbation is highly dependent on the category scale, which appears as assigning smaller variation to head classes and larger variation to tail classes. In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation. It is noteworthy that the introduced variation is discarded at the inference stage to facilitate a confident prediction. Although with an embarrassingly simple implementation, our method manifests itself in strong generalizability to various datasets and task settings. Extensive experiments suggest that our plug-in design lends itself well to a range of state-of-the-art approaches and boosts the performance on top of them

Corrosion behavior of homogenized and extruded 1100 aluminum alloy in acidic salt spray

The 1100 aluminum alloy has been widely used in many industrial fields due to its high specific strength, fracture toughness, excellent thermal conductivity, and corrosion resistance. In this study, the corrosion behavior of the homogenized and hot-extruded 1100 aluminum alloy in acid salt spray environment for different time was studied. The microstructure of the 1100 aluminum alloy before and after corrosion was characterized by an optical microscope (OM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and a laser scanning confocal microscope (LSCM). The difference in corrosion resistance between the homogenized and extruded 1100 aluminum alloy was analyzed via the electrochemical method. The results indicate that after hot extrusion at 400 °C, the microstructure of the 1100 aluminum alloy changes from an equiaxed crystal structure with (111) preferentially distributed in a fibrous structure with (220) preferentially distributed. There was no obvious dynamic recrystallization occurring during extrusion, and the second-phase particles containing Al-Fe-Si were coarse and unevenly distributed. With the increase in corrosion time, corrosion pits appeared on the surface of the 1100 aluminum alloy, and a corrosion product layer was formed on the surface of the homogenized 1100 aluminum alloy, which reduced the corrosion rate. After 96 h of corrosion, the CPR of the extruded samples was 0.619 mm/a, and that of the homogenized samples was 0.442 mm/a. The corrosion resistance of the extruded 1100 aluminum alloy was affected by the microstructure and the second phase, and no protective layer of corrosion products was formed on the surface, resulting in a faster corrosion rate and deeper corrosion pits