Unit Energy Consumption, Production, and Cost of Innovative Treatment Systems of Different Wastewater Streams

Innovative technologies such as micro-sieving, Anammox, and up-flow anaerobic sludge blanket (UASB) hold the key in the sustainable design of Water Resource Recovery Facility (WRRF). In the past, assessment metrics on the effectiveness and economic feasibility of these technologies have not been systematically investigated. According to the twelve design principles of Sustainable Environmental Engineering, Unit energy and cost metrics could provide universal benchmarks in the design of WRRF. Therefore, the objectives of this study are to design innovative WRRF systems to achieve energy positive. These WRRFs were modeled by developing an Excel model to estimate the unit energy metrics. Database of different wastewater quality was developed according to literature data. An Excel model was also developed to estimate the cost due to the energy saving of innovative systems. In treating young, medium, and old leachate, systems with the innovative technologies could save the unit energy consumption by 2.24-4.07 kWh/kg Nremoved and the unit cost by $0.86-2.09/kg CODremoved than conventional technologies. Treatment of young leachate costs less than other leachate in terms of per kg COD removed. Although micro-sieving decreases CH4 production, it reduces the size of the UASB. As a result, micro-sieving could reduce the unit cost by 27$/kg CODremoved, respectively. Therefore, meat processing wastewater can be the most efficiently treated by using innovative technologies due to its high biodegradability. For WWTPs in China, anaerobic-oxic plus anaerobic-anoxic-oxic, oxidation ditch, and sequencing batch reactor were the main technologies. Due to lower energy consumption, SBR is the best technology in small and medium WWTPs in China

Optical frequency combs carrying optical angular momentum

To date, orbital angular momentum (OAM) and optical frequency combs (OFCs) are two distinct fields of research without any association. Herein, we generated OFCs with an OAM on each comb line by applying electro-optic phase modulation to the OAM beam. We verified that the OAM characteristic of the sidebands is consistent with that of the pump light. Our study bridges two distinct research fields OFCs and OAM opening the door to various fundamental research avenues and applications, including large-capacity optical communications, high-security optical encryption, multi-dimensional photon entanglement, and synthetic dimensions

Behavior analysis of juvenile steelhead trout under blue and red light color conditions based on multiple object tracking

IntroductionThe lighting environment significantly influences fish behavior. This study explores the impact of diverse lighting conditions on the behavior of steelhead trout (Oncorhynchus mykiss) to illuminate the mechanisms underlying their behavioral responses.MethodsThis experiment was set up with six treatments at a constant light intensity of 150 lx: 12h white light + 12h dark (12 W), 12h blue light + 12h dark (12B), 12h red light + 12h dark (12 R), 1.5h blue light + 9h red light + 1.5h blue light + 12h dark (3B9R), 3h blue light + 6h red light + 3h blue light + 12h dark (6B6R), total 12h of blue and red light + 12h dark (T12BR). A multiple object tracking method, YOLOv5 with SORT, was employed to capture the movement trajectory of each fish, quantifying three motion metrics: swimming velocity, swimming angular velocity, and generalized intersection over union.ResultsThe results revealed that fish exposed to 12R light environment showed significantly higher activity levels than other groups. The mixed light environments (3B9R, 6B6R) formed significant differences in behavioral metrics with 12R earlier than pure light environments (12B, 12W, T12BR), indicating sudden light color changes should be avoided. Fish in the 3B9R environment exhibited the lowest activity level but highest growth performance, with the highest specific growth rate of 1.91±0.12 d-1, a value significantly surpassing the lowest recorded rate, supported by a p-value of 0.0054, indicating it is suitable for steelhead trout cultivation.DiscussBehavioral significant differences were observed as early as week eight, much earlier than physiological differences, which became apparent by week 16. Overall, this paper employs computer vision methods to study the impact of different light colors on fish behavior, found that 3B9R is the optimal lighting condition tested and sudden light color changes should be avoided, offering a new perspective on light conditions and behavior in steelhead trout cultivation

Enzalutamide-Induced Signatures Revealed by Epigenetic Plasticity Using Single-Cell Multi-Omics Sequencing in Prostate Cancer

Prostate cancer is morphologically and molecularly heterogeneous, which poses obstacles for early diagnosis and treatment. Advancements in understanding the heterogeneity of prostate cancer will help navigate through these challenges and ultimately benefit patients. In this study, we integrated single-cell sequencing for transposase-accessible chromatin and whole transcriptome in prostate cancer cell lines, aiming to decode the epigenetic plasticity upon enzalutamide (ENZ) treatment. By comparing the cell populations representing early-treatment response or resistance to the initial tumor cells, we identified seven signature gene sets; they present consistent trends of chromatin closing co-occurred with down-regulated genes during early response and chromatin opening with up-regulated genes upon maintaining drug resistance. In the molecular signatures, we found gene

Investigating Cellular Heterogeneity at the Single-Cell Level by the Flexible and Mobile Extrachromosomal Circular DNA

Extrachromosomal circular DNA (eccDNA) is a special class of DNA derived from linear chromosomes. It coexists independently with linear chromosomes in the nucleus. eccDNA has been identified in multiple organisms, including Homo sapiens, and has been shown to play important roles relevant to tumor progression and drug resistance. To date, computational tools developed for eccDNA detection are only applicable to bulk tissue. Investigating eccDNA at the single-cell level using a computational approach will elucidate the heterogeneous and cell-type-specific landscape of eccDNA within cellular context. Here, we performed the first eccDNA analysis at the single-cell level using data generated by single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) in adult and pediatric glioblastoma (GBM) samples. Glioblastoma multiforme (GBM) is an aggressive tumor of the central nervous system with a poor prognosis. Our analysis provides an overview of cellular origins, genomic distribution, as well as the differential regulations between linear and circular genome under disease- and cell-type-specific conditions across the open chromatin regions in GBM. We focused on some eccDNA elements that are potential mobile enhancers acting in a trans-regulation manner. In summary, this pilot study revealed novel eccDNA features in the cellular context of brain tumor, supporting the strong need for eccDNA investigation at the single-cell level

Experience-Learning Inspired Two-Step Reward Method for Efficient Legged Locomotion Learning Towards Natural and Robust Gaits

Multi-legged robots offer enhanced stability in complex terrains, yet autonomously learning natural and robust motions in such environments remains challenging. Drawing inspiration from animals' progressive learning patterns, from simple to complex tasks, we introduce a universal two-stage learning framework with two-step reward setting based on self-acquired experience, which efficiently enables legged robots to incrementally learn natural and robust movements. In the first stage, robots learn through gait-related rewards to track velocity on flat terrain, acquiring natural, robust movements and generating effective motion experience data. In the second stage, mirroring animal learning from existing experiences, robots learn to navigate challenging terrains with natural and robust movements using adversarial imitation learning. To demonstrate our method's efficacy, we trained both quadruped robots and a hexapod robot, and the policy were successfully transferred to a physical quadruped robot GO1, which exhibited natural gait patterns and remarkable robustness in various terrains

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

Bounding box regression is the crucial step in object detection. In existing methods, while $\ell_n$-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation metric, i.e., Intersection over Union (IoU). Recently, IoU loss and generalized IoU (GIoU) loss have been proposed to benefit the IoU metric, but still suffer from the problems of slow convergence and inaccurate regression. In this paper, we propose a Distance-IoU (DIoU) loss by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses. Furthermore, this paper summarizes three geometric factors in bounding box regression, \ie, overlap area, central point distance and aspect ratio, based on which a Complete IoU (CIoU) loss is proposed, thereby leading to faster convergence and better performance. By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e.g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric. Moreover, DIoU can be easily adopted into non-maximum suppression (NMS) to act as the criterion, further boosting performance improvement. The source code and trained models are available at https://github.com/Zzh-tju/DIoU.Comment: Accepted to AAAI 2020. The source code and trained models are available at https://github.com/Zzh-tju/DIo