Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks

One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales

HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention

Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity of social platforms. Though existing graph-based Twitter bot detection methods achieved state-of-the-art performance, they are all based on the homophily assumption, which assumes users with the same label are more likely to be connected, making it easy for Twitter bots to disguise themselves by following a large number of genuine users. To address this issue, we proposed HOFA, a novel graph-based Twitter bot detection framework that combats the heterophilous disguise challenge with a homophily-oriented graph augmentation module (Homo-Aug) and a frequency adaptive attention module (FaAt). Specifically, the Homo-Aug extracts user representations and computes a k-NN graph using an MLP and improves Twitter's homophily by injecting the k-NN graph. For the FaAt, we propose an attention mechanism that adaptively serves as a low-pass filter along a homophilic edge and a high-pass filter along a heterophilic edge, preventing user features from being over-smoothed by their neighborhood. We also introduce a weight guidance loss to guide the frequency adaptive attention module. Our experiments demonstrate that HOFA achieves state-of-the-art performance on three widely-acknowledged Twitter bot detection benchmarks, which significantly outperforms vanilla graph-based bot detection techniques and strong heterophilic baselines. Furthermore, extensive studies confirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's ability to demystify the heterophilous disguise challenge.Comment: 11 pages, 7 figure

Fast Start-Up Microfluidic Microbial Fuel Cells With Serpentine Microchannel

Microfluidic microbial fuel cells (MMFCs) are promising green power sources for future ultra-small electronic devices. The MMFCs with co-laminar microfluidic structure are superior to other MMFCs according to their low internal resistance and relative high power density. However, the area for interfacial electron transfer between the bacteria and the anode is quite limited in the typical Y-shaped device, which apparently restricts the current generation performance. In this study, we developed a membraneless MMFC with serpentine microchannel to enhance the interfacial electron transfer and promote the power generation of the device. Owing to the merit of laminar flow, the proposed MMFC was working well without any proton exchange membrane (PEM). At the same time, the serpentine microchannel greatly increased the power density. The S-MMFC catalyzed by Shewanella putrefaciens CN32 achieves a peak power density of 360 mW/m2 with the optimal channel configuration and the flow rate of 5 ml/h. Meanwhile, this device possesses much shorter start-up time and much longer duration time at high current plateau than the previous reported MMFCs. The presented MMFC appears promising for biochip technology and extends the scope of microfluidic energy

Antibiotics in the offshore waters of the Bohai Sea and the Yellow Sea in China: Occurrence, distribution and ecological risks

The ocean is an important sink of land-based pollutants. Previous studies showed that serious antibiotic pollution occurred in the coastal waters, but limited studies focused on their presence in offshore waters. In this study, eleven antibiotics in three different categories were investigated in offshore waters of the Bohai Sea and the Yellow Sea in China. The results indicated that three antibiotics dehydration erythromycin, sulfamethoxazole and trimethoprim occurred throughout the offshore waters at concentrations of 0.10-16.6 ng L-1 and they decreased exponentially from the rivers to the coastal and offshore waters. The other antibiotics all presented very low detection rates (<10%) and concentrations (<0.51 ng L-1). Although the concentrations were very low, risk assessment based on the calculated risk quotients (RQs) showed that sulfamethoxazole, dehydration erythromycin and clarithromycin at most of sampling sites posed medium or low ecological risks (0.01 < RQ < 1) to some sensitive aquatic organisms, including Synechococcus leopoliensis and Pseudokirchneriella subcapitata. (C) 2012 Elsevier Ltd. All rights reserved.The ocean is an important sink of land-based pollutants. Previous studies showed that serious antibiotic pollution occurred in the coastal waters, but limited studies focused on their presence in offshore waters. In this study, eleven antibiotics in three different categories were investigated in offshore waters of the Bohai Sea and the Yellow Sea in China. The results indicated that three antibiotics dehydration erythromycin, sulfamethoxazole and trimethoprim occurred throughout the offshore waters at concentrations of 0.10-16.6 ng L-1 and they decreased exponentially from the rivers to the coastal and offshore waters. The other antibiotics all presented very low detection rates (<10%) and concentrations (<0.51 ng L-1). Although the concentrations were very low, risk assessment based on the calculated risk quotients (RQs) showed that sulfamethoxazole, dehydration erythromycin and clarithromycin at most of sampling sites posed medium or low ecological risks (0.01 < RQ < 1) to some sensitive aquatic organisms, including Synechococcus leopoliensis and Pseudokirchneriella subcapitata. (C) 2012 Elsevier Ltd. All rights reserved

Association between weight-adjusted-waist index and the risk of hyperuricemia in adults: a population-based investigation

ObjectiveThis investigation sought to elucidate the potential correlation between a recently characterized adiposity metric, termed the Weight-Adjusted-Waist Index (WWI) and hyperuricemia.MethodsA cross-sectional design was employed in this study, featuring both hyperuricemic and non-hyperuricemic subjects with complete WWI data, sourced from the National Health and Nutrition Examination Survey (NHANES) spanning 2017 to March 2020. WWI was calculated utilizing the formula which involves the division of waist circumference (WC) by the square root of the body weight. In order to determine the relationship between WWI and hyperuricemia, both univariate and multivariate logistic regression models, appropriately weighted, were employed in the analysis. The linearity of relationships was validated using smooth curve fitting. Additionally, subgroup evaluations and interaction assessments were conducted.ResultsThe study sample comprised 7437 subjects, yielding a hyperuricemia prevalence of 18.22%. Stratifying WWI into tertiles, a progressive rise in hyperuricemia prevalence was evident with increasing WWI (Tertile 1: 11.62%, Tertile 2: 17.91%, Tertile 3: 25.13%). The odds ratio (OR) demonstrated that individuals within the highest WWI tertile were significantly more prone to hyperuricemia than those in the lowest tertile (OR = 2.41, 95% CI: 1.88-3.08).ConclusionThis study provides evidence that an elevated WWI is correlated with an increased risk of hyperuricemia in the adult population of the United States. These results suggest that WWI may serve as a viable anthropometric indicator for predicting hyperuricemia

A novel vaccine formulation candidate based on lipooligosaccharides and pertussis toxin against Bordetella pertussis

Pertussis is a severe human respiratory tract infectious disease caused by Bordetella pertussis that primarily affects infants and young children. However, the acellular pertussis vaccine currently administered can induce antibody and Th2 immune responses but fails to prevent the nasal colonization and transmission of B. pertussis, causing a resurgence of pertussis, so improved pertussis vaccines are urgently needed. In this study, we created a two-component pertussis vaccine candidate containing a conjugate prepared from oligosaccharides and pertussis toxin. After demonstrating the ability of the vaccine to induce a mixed Th1/Th2/Th17 profile in a mouse model, the strong in vitro bactericidal activity and IgG response of the vaccine were further demonstrated. In addition, the vaccine candidate further induced efficient prophylactic effects against B. pertussis in a mouse aerosol infection model. In summary, the vaccine candidate in this paper induces antibodies with bactericidal activity to provide high protection, shorten the duration of bacterial existence, and further reduce disease outbreaks. Therefore, the vaccine has the potential to be the next generation of pertussis vaccines

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively. Expanding upon this crucial observation, we additionally unveil a significant correlation between the agents' inclination towards motorically inactive exploration and the absence of neuronal activity within their policy networks. To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network. Empirically, we also recognize that the dormant ratio can act as a standalone indicator of an agent's activity level, regardless of the received reward signals. Leveraging the aforementioned insights, we introduce DrM, a method that uses three core mechanisms to guide agents' exploration-exploitation trade-offs by actively minimizing the dormant ratio. Experiments demonstrate that DrM achieves significant improvements in sample efficiency and asymptotic performance with no broken seeds (76 seeds in total) across three continuous control benchmark environments, including DeepMind Control Suite, MetaWorld, and Adroit. Most importantly, DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains from the DeepMind Control Suite as well as three dexterous hand manipulation tasks without demonstrations in Adroit, all based on pixel observations

10.13% Efficiency All-Polymer Solar Cells Enabled by Improving the Optical Absorption of Polymer Acceptors

The limited light absorption capacity for most polymer acceptors hinders the improvement of the power conversion efficiency (PCE) of all-polymer solar cells (all-PSCs). Herein, by simultaneously increasing the conjugation of the acceptor unit and enhancing the electron-donating ability of the donor unit, a novel narrow-bandgap polymer acceptor PF3-DTCO based on an A–D–A-structured acceptor unit ITIC16 and a carbon–oxygen (C–O)-bridged donor unit DTCO is developed. The extended conjugation of the acceptor units from IDIC16 to ITIC16 results in a red-shifted absorption spectrum and improved absorption coefficient without significant reduction of the lowest unoccupied molecular orbital energy level. Moreover, in addition to further broadening the absorption spectrum by the enhanced intramolecular charge transfer effect, the introduction of C–O bridges into the donor unit improves the absorption coefficient and electron mobility, as well as optimizes the morphology and molecular order of active layers. As a result, the PF3-DTCO achieves a higher PCE of 10.13% with a higher short-circuit current density (Jsc) of 15.75 mA cm−2 in all-PSCs compared with its original polymer acceptor PF2-DTC (PCE = 8.95% and Jsc = 13.82 mA cm−2). Herein, a promising method is provided to construct high-performance polymer acceptors with excellent optical absorption for efficient all-PSCs

Amplification Refractory Mutation System (ARMS)-PCR for waxy sorghum authentication with single-nucleotide resolution

Waxy sorghum has greater economic value than wild sorghum in relation to their use in food processing and the brewing industry. Thus, the authentication of the waxy sorghum species is an important issue. Herein, a rapid and sensitive Authentication Amplification Refractory Mutation System-PCR (aARMS-PCR) method was employed to identify sorghum species via its ability to resolve single-nucleotide in genes. As a proof of concept, we chose a species of waxy sorghum containing the wxc mutation which is abundantly used in liquor brewing. The aARMS-PCR can distinguish non-wxc sorghum from wxc sorghum to guarantee identification of specific waxy sorghum species. It allowed to detect as low as 1% non-wxc sorghum in sorghum mixtures, which ar one of the most sensitive tools for food authentication. Due to its ability for resolving genes with single-nucleotide resolution and high sensitivity, aARMS-PCR may have wider applicability in monitoring food adulteration, offering a rapid food authenticity verification in the control of adulteration