Purpose
The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a ...weighted directed network of interconnected industries.
Design/methodology/approach
First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.
Findings
The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.
Practical implications
The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.
Originality/value
Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.
Abstract
In this article, itaconic acid (IA), 2‐acrylamide‐2‐methylpropanesulfonic acid (AMPS), and hydroxypropyl acrylate (HPA) served as the reaction's monomers, deionized water served as the ...reaction's solvent, and ammonium persulfate served as the reaction's initiator, with isopropanol serving as the chain transfer agent, a terpolymer scale inhibitor (IA‐AMPS‐HPA) was synthesized and used to prevent the growth of calcium carbonate (CaCO
3
) scale in an oilfield environment. On the effectiveness of scale inhibition, the influence of temperature, scale inhibitor concentration, pH, calcium ion concentration, and salt levels are examined. The results indicate that the scale inhibition rate progressively drops with growing temperature and gradually increases with rising scale inhibitor concentration. The scale inhibition rate reaches 97.67% when the temperature is 60°C, and the scale inhibitor concentration is 60 mg/L. When the temperature is 80°C and the concentration of scale inhibitor is 40 mg/L, the inhibitor's ability to block CaCO
3
increases as the salt content rises. The scale inhibition rate is more than 85% when the pH is less than 9. Besides, the Ca
2+
concentration is less than 300 mg/L, and the scale inhibition rate declines as the Ca
2+
concentration rises, reaching a maximum value of 91.67% when the salt content is 33 g/L. With a rise in pH, the scale inhibition rate dropped. Employing a scanning electron microscope, the morphology of scale samples was described and analyzed. Molecular dynamics was utilized to simulate the interaction between IA‐AMPS‐HPA and the crystallization surface of CaCO
3
at varying temperatures. The results show that the binding energy decreases as the system temperature rises and that the reaction between polymer molecules and CaCO
3
crystals in the system can be observed by increasing the time step length and the water molecule concentration. The simulation results are with following the observed phenomena.
Pickering emulsions stabilized by polysaccharide particles have received increasing attention because of their potential applications in three-dimensional (3D) printing. In this study, the citrus ...pectins (citrus tachibana, shaddock, lemon, orange) modified with β-cyclodextrin (β-CD) were used to stabilize Pickering emulsions reaching the requirements of 3D printing. In terms of pectin chemical structure, the steric hindrance provided by the RG I regions was more conducive to the stability of the complex particles. The modification of pectin by β-CD provided the complexes a better double wettability (91.14 ± 0.14°-109.43 ± 0.22°) and a more negative ζ-potential, which was more beneficial for complexes to anchor at oil-water interface. In addition, the rheological properties, texture properties and stability of the emulsions were more responsive to the ratios of pectin/β-CD (Rβ/C). The results showed that the emulsions stabilized at a φ = 65 % and a Rβ/C = 2:2 achieved the requirements (shear thinning behavior, self-supporting ability, and stability) of 3D printing. Furthermore, the application in 3D printing demonstrated that the emulsions under the optimal condition (φ = 65 % and Rβ/C = 2:2) displayed excellent printing appearance, especially for the emulsions stabilized by β-CD/LP particles. This study provides a basis for the selection of polysaccharide-based particles to prepare 3D printing inks which may be utilized in food manufacturing.
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Spurred by the worldwide concern for forest protection and the increased log sales, most countries have standardized log volume calculations to avoid excessive timber and protect buyers. However, log ...volume is currently manually measured, suffering from high labor costs, low measurement progress, and imposing significant measurement errors. Thus, automatically obtaining the volumetric data of logs is a convenient and quick solution. Therefore, this work proposes a Mask Region Convolutional Neural Network-based (R-CNN) algorithm for Logs volume measurement, named Wood Mask (WM) R-CNN. Specifically, we employ the Res2Net structure as the backbone to obtain receptive fields that exceed the input feature size, thus improving our model's multi-scale information fusion ability. Additionally, WM R-CNN relies on the Path Aggregation Feature Pyramid Network's (PAFPN's) path enhancement structure, shortening the low-level feature map's propagation path and improving the wood contour segmentation accuracy. Extensive experiments on the Vehicle-mounted Dense Logs (VMDL) dataset demonstrate that WM R-CNN affords a highly appealing segmentation accuracy for small, medium, and large wood, improving the corresponding mAP indicators against current methods by 2.0%, 1.2%, and 4.4%, respectively. Furthermore, a quantitative method based on TensorRT compresses the proposed model to deploy the WM R-CNN to mobile embedded devices. However, to compensate for the quantization loss, we introduce the expansion convolution operation method to manipulate the mask map and control the volume calculation error of all logs on a vehicle within 1%. The experiments reveal that the proposed method offers an appealing performance, verifying the algorithm's effectiveness and implementation ability on mobile terminals.
Glaucoma is a leading cause of blindness. The measurement of vertical cup-to-disc ratio combined with other clinical features is one of the methods used to screen glaucoma. In this paper, we propose ...a deep level set method to implement the segmentation of optic cup (OC) and optic disc (OD). We present a multi-scale convolutional neural network as the prediction network to generate level set initial contour and evolution parameters. The initial contour will be further refined based on the evolution parameters. The network is integrated with augmented prior knowledge and supervised by active contour loss, which makes the level set evolution yield more accurate shape and boundary details. The experimental results on the REFUGE dataset show that the IoU of the OC and OD are 93.61% and 96.69%, respectively. To evaluate the robustness of the proposed method, we further test the model on the Drishthi-GS1 dataset. The segmentation results show that the proposed method outperforms the state-of-the-art methods.
The dimensional instability and inferior mechanical strength of radiata pine (
D. Don) limit its use in musical instruments. To improve its properties, a two-step treatment by furfuryl alcohol (FA) ...and tung oil (TO) were used involving impregnation of FA precursor solution into the wood matrix, forming a highly cross-linked resin, followed by a modification with TO. Examination using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy revealed that FA resin was attached in cell walls and lumens, and the solidified TO in cell lumens and occluded pits. The incorporation of FA resin reduced the wood swelling coefficient by over 70% and the hydrophobic solidified TO decreased wood water uptake by over 80% enhancing wood dimensional stability. Although FA resin improved the modulus of elasticity and hardness of the wood, the modulus of rupture and impact bending strength were reduced. However, the additional TO impregnation step improved the modulus of rupture, impact bending strength, and wear resistance of the furfurylated wood. The performance of FA and TO treated radiata pine wood was better than that treated with FA or TO alone, and could meet the performance requirements of wood used for fretboard of string instruments.
Predictions of the spatiotemporal distribution of fault events help the management planning and the maintenance scheduling in power transmission systems. To this end, this study proposes an early ...warning system based on the exploration of cause-attribute relationships. A comprehensive assessment of all the fault causes is conducted by incorporating entire available environmental attributes in the investigated system as inputs. To cope with the rarely occurred fault causes and environmental elements, a procedure of the weighted association rule mining is established. Then, a risk evaluation model is employed in evaluating the prediction performance and the risks of possible false predictions. Three requirements for the uncertainty are taken into account when this framework is applied in real use: technical reliability, internal reliability, and factitious effectiveness. An empirical study is conducted in a real power transmission system to verify the feasibility of the proposed framework, and the results prove that a flexible and robust prediction can be generated consequently.
Electromigration (EM) is considered to be one of the most important reliability issues for current and future ICs in 10-nm technology and below. In this article, we propose a fast analytic solution ...to compute the stress evolution in the confined multisegment interconnect wires. The new method, called the accelerated separation of variables (ASOV) method, aims to find the analytic solutions of the partial differential equations of stress in confined interconnect metals based on the SOV method. It offers several improvements over the existing plain SOV-based method. First, we show that the accuracy of the solution depends on the structure of the interconnects. As a result, the number of required eigenvalues is structure and problem dependent, instead of fixed numbers used by the existing SOV method. Second, for the straight line multisegment and star-structured multiterminal interconnects, analytical expressions are formulated to calculate the eigenvalues directly instead of using numerical methods as in the existing SOV method. Third, we propose a linear Gaussian elimination (GE) algorithm by exploiting the banded structure with the serrated-edge form of the transcendental matrix, which can significantly speed up GE process, and is the key computing step in the SOV-based solution framework. Fourth, instead of using the simple bisection search, we propose to use an enhanced determinant-based secant iterative method to find the eigenvalues of the transcendental matrix. Numerical results show that a good agreement is achieved between analytical and numerical results on two special cases, and the resulting algorithm can lead to 3-5X speedup over the existing plain SOV-based solution on a number of multisegment interconnects benchmarks.
We present a study of protein adsorption on oligo(ethylene glycol) (OEG) self-assembled monolayers (SAMs) at a range of OEG surface densities. OEG SAMs were formed in mixed ethanol and water ...solutions at different assembly temperatures to adjust the packing density of EG
4
-SAMs.
These SAMs were characterized using X-ray photoelectron spectroscopy (XPS). Fibrinogen adsorption on these surfaces was measured by a surface plasmon resonance (SPR) sensor at different temperatures. This work is aimed at addressing three important issues for protein-OEG interactions,
i.e., (i) OEG stability, (ii) the correlation between OEG surface densities and surface non-fouling properties, and (iii) protein adsorption on OEG surfaces at different temperatures.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Effective information extraction of pharmaceutical texts is of great significance for clinical research. The ancient Chinese medicine text has streamlined sentences and complex semantic ...relationships, and the textual relationships may exist between heterogeneous entities. The current mainstream relationship extraction model does not take into account the associations between entities and relationships when extracting, resulting in insufficient semantic information to form an effective structured representation. In this paper, we propose a heterogeneous graph neural network relationship extraction model adapted to traditional Chinese medicine (TCM) text. First, the given sentence and predefined relationships are embedded by bidirectional encoder representation from transformers (BERT fine-tuned) word embedding as model input. Second, a heterogeneous graph network is constructed to associate words, phrases, and relationship nodes to obtain the hidden layer representation. Then, in the decoding stage, two-stage subject-object entity identification method is adopted, and the identifier adopts a binary classifier to locate the start and end positions of the TCM entities, identifying all the subject-object entities in the sentence, and finally forming the TCM entity relationship group. Through the experiments on the TCM relationship extraction dataset, the results show that the precision value of the heterogeneous graph neural network embedded with BERT is 86.99% and the F1 value reaches 87.40%, which is improved by 8.83% and 10.21% compared with the relationship extraction models CNN, Bert-CNN, and Graph LSTM.
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