FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design

Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific engineering problems yet with limited generalizability. In this paper, we show that failure evaluation provides a common kernel to improve the tractability and scalability of existing solutions. By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design. FERN represents rich problem inputs as a graph and captures both local and global views by attentively performing feature extraction from the graph. It enables a broad range of robust network design problems, including robust network validation, network upgrade optimization, and fault-tolerant traffic engineering that are discussed in this paper, to be recasted with respect to the common kernel and thus computed efficiently using neural networks and over a small set of critical failure scenarios. Extensive experiments on real-world network topologies show that FERN can efficiently and accurately identify key failure scenarios for both OSPF and optimal routing scheme, and generalizes well to different topologies and input traffic patterns. It can speed up multiple robust network design problems by more than 80x, 200x, 10x, respectively with negligible performance gap

Microwave-assisted non-thermal hemp degumming

The microwave-assisted non-thermal degumming of hemp fibre has been studied and then compared with the water bath heating under different time and temperature conditions. The results show that the residual gum content of the lean hemp using microwave-assisted heating method is lower than that obtained using water bath heating. The residual gum content gap between the two degumming processes increases first and then decreases as the heating time and temperature are increased. This proves the existence of non-thermal effects in microwave heating process besides the thermal effects in water bath heating. In addition, the structures of the lean hemp fibres obtained from these two methods are also studied by scanning electron microscopy and fourier transform infrared spectroscopy.

A New Wind Power Forecasting Approach Based on Conjugated Gradient Neural Network

Prediction of the output power of wind plants is of great significance for running a power system comprising large amount of wind generators. According to the prediction results, it is possible to determine the quotas of power generation in power generators and distribute resources in a scientific and reasonable way. In the past, the Grey Neural Network was widely applied in predicting wind power while it could hardly meet the engineering requirements due to the structure of ANN. The problem of slow convergence speed and large amount of iterations, especially in case of large scale data, would pose challenges to power prediction and the sensitivity of automatic control. This paper optimizes ANN model by applying conjugate gradient descent and creating Conjugated Gradient Neural Network (CGNN) in weights updating process. Experiments performed on different scale datasets have proved that the performance of CGNN improves substantially as the average iterations decreased by almost 90% without the sacrifice of prediction accuracy

Preparation of washable, highly sensitive and durable strain sensor based conductive double rib knitted fabric

A strain sensor based nylon/spandex double rib elastic knitted fabric has been fabricated by coating graphene and adhesive. The morphology, conductivity and sensing property of treated fabric are investigated. The coated knit fabric exhibits a good conductivity of 15.65 S/m and the resulting strain sensors could detect the small strains of about 0.2% with gauge factor of 29.15. Within a strain range of 0-20%, the gauge factor is found as 28.64. It also shows excellent performance in terms of sensitivity, stability and durability over 5000 wash cycles, and could monitor small external deformations with a response time of 0.24s. Moreover, it has good washability.

A flexible dual-mode pressure sensor with ultra-high sensitivity based on BTO@MWCNTs core-shell nanofibers

Wearable flexible sensors have developed rapidly in recent years because of their improved capacity to detect human motion in wide-ranging situations. In order to meet the requirements of flexibility and low detection limits, a new pressure sensor was fabricated based on electrospun barium titanate/multi-wall carbon nanotubes (BTO@MWCNTs) core-shell nanofibers coated with styrene-ethylene-butene-styrene block copolymer (SEBS). The sensor material (BTO@MWCNTs/SEBS) had a SEBS to BTO/MWCNTs mass ratio of 20:1 and exhibited an excellent piezoelectricity over a wide range of workable pressures from 1 to 50 kPa, higher output current of 56.37 nA and a superior piezoresistivity over a broad working range of 20 to 110 kPa in compression. The sensor also exhibited good durability and repeatability under different pressures and under long-term cyclic loading. These properties make the composite ideal for applications requiring monitoring subtle pressure changes (exhalation, pulse rate) and finger movements. The pressure sensor developed based on BTO@MWCNTs core-shell nanofibers has demonstrated great potential to be assembled into intelligent wearable devices

Textile Waste Fiber Regeneration via a Green Chemistry Approach: A Molecular Strategy for Sustainable Fashion

From Wiley via Jisc Publications RouterHistory: received 2021-07-06, rev-recd 2021-08-15, pub-electronic 2021-09-24, pub-print 2021-12-02Article version: VoRPublication status: PublishedFunder: EPSRC; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/R00661X/1, EP/P025021/1, EP/P025498/1Abstract: Fast fashion, as a continuously growing part of the textile industry, is widely criticized for its excessive resource use and high generation of textiles. To reduce its environmental impacts, numerous efforts are focused on finding sustainable and eco‐friendly approaches to textile recycling. However, waste textiles and fibers are still mainly disposed of in landfills or by incineration after their service life and thereby pollute the natural environment, as there is still no effective strategy to separate natural fibers from chemical fibers. Herein, a green chemistry strategy is developed for the separation and regeneration of waste textiles at the molecular level. Cellulose/wool keratin composite fibers and multicomponent fibers are regenerated from waste textiles via a green chemical process. The strategy attempts to reduce the large amount of waste textiles generated by the fast‐developing fashion industry and provide a new source of fibers, which can also address the fossil fuel reserve shortages caused by chemical fiber industries and global food shortages caused by natural fiber production

Causal relationships between lung cancer and sepsis: a genetic correlation and multivariate mendelian randomization analysis

BackgroundFormer research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear.MethodThis study used univariate Mendelian Randomization (MR) to explore the causal relationship between LC, its subtypes, and sepsis. Linkage Disequilibrium Score (LDSC) regression was used to calculate genetic correlations. Multivariate MR was applied to investigate the role of seven confounding factors. The primary method utilized was inverse-variance-weighted (IVW), supplemented by sensitivity analyses to assess directionality, heterogeneity, and result robustness.ResultsLDSC analysis revealed a significant genetic correlation between LC and sepsis (genetic correlation = 0.325, p = 0.014). Following false discovery rate (FDR) correction, strong evidence suggested that genetically predicted LC (OR = 1.172, 95% CI 1.083–1.269, p = 8.29 × 10−5, Pfdr = 2.49 × 10−4), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021–1.181, p = 0.012, Pfdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024–1.178, p = 0.009, Pfdr = 0.012) are linked to an increased incidence of sepsis. Suggestive evidence was also found for small cell lung carcinoma (Wald ratio: OR = 1.156, 95% CI 1.047–1.277, p = 0.004) in relation to sepsis. The multivariate MR suggested that the partial impact of all LC subtypes on sepsis might be mediated through body mass index. Reverse analysis did not find a causal relationship (p > 0.05 and Pfdr > 0.05).ConclusionThe study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients

Influence of Reynolds Number on Multi-Objective Aerodynamic Design of a Wind Turbine Blade.

At present, the radius of wind turbine rotors ranges from several meters to one hundred meters, or even more, which extends Reynolds number of the airfoil profile from the order of 105 to 107. Taking the blade for 3MW wind turbines as an example, the influence of Reynolds number on the aerodynamic design of a wind turbine blade is studied. To make the study more general, two kinds of multi-objective optimization are involved: one is based on the maximum power coefficient (CPopt) and the ultimate load, and the other is based on the ultimate load and the annual energy production (AEP). It is found that under the same configuration, the optimal design has a larger CPopt or AEP (CPopt//AEP) for the same ultimate load, or a smaller load for the same CPopt//AEP at higher Reynolds number. At a certain tip-speed ratio or ultimate load, the blade operating at higher Reynolds number should have a larger chord length and twist angle for the maximum Cpopt//AEP. If a wind turbine blade is designed by using an airfoil database with a mismatched Reynolds number from the actual one, both the load and Cpopt//AEP will be incorrectly estimated to some extent. In some cases, the assessment error attributed to Reynolds number is quite significant, which may bring unexpected risks to the earnings and safety of a wind power project