Journal of Systems Science & Complexity最新文献

With the increase of economic environment uncertainty, it is of great importance to study the linkage and spillover effects of economic policy uncertainty among countries. Especially, this article selects eight countries along the Belt and Road as the core countries (China, Korea, Croatia, India, Russia, Greece, Pakistan, and Singapore) and four countries (Germany, France, Japan, and UK) as the peripheral countries, and then copula technique and mixed-frequency global vector autoregressive model are employed to analyze the correlation and the spillover effect of the economic policy uncertainty (EPU) for the twelve selected countries, respectively. The proposed empirical findings show clearly that the EPU correlation among the eight core Belt and Road countries is stronger and the spillover effect of the core countries to the peripheral countries is statistically significant. As a result, for harmonious and win-win development, the Belt and Road countries should pay a close attention to the EPU, because the stability of the EPU promotes greatly the economy development.

China is actively upgrading its industrial structure through industries transferring between developed and undeveloped areas; however, the overall level of the national value chain is still not high, and the asymmetric competition pattern between the upstream and the downstream has not been broken. Therefore, this paper establishes a competitive equilibrium model for the production of manufacturing enterprises, with factor price distortion, under the condition of constant returns to scale. The authors derive the relative distortion coefficients of each factor price, calculate the misallocation indices of capital and labor, and construct an industry resource misallocation measure. Furthermore, this paper applies the regional value-added decomposition model to calculate the national value chain index and matches the market index of the China Market Index Database with the Chinese Industrial Enterprises Database and the Inter-Regional Input-Output Tables through quantitative analysis. From the perspective of the national value chain, the authors study the improvement effect and mechanism of the business environment on the resource allocation in industry. The study shows that industry resource allocation will be improved by 17.89% if the business environment level is improved by one standard deviation. This effect is most prevalent in the eastern and central regions, not so much in the west; the effect of downstream industries in the national value chain is higher than that of upstream industries; the improvement effect on capital allocation is higher in downstream industries than in the upstream industries; and the improvement effect on labor misallocation is basically the same in both the upstream and the downstream. Compared with labor intensive industries, capital intensive industries are more influenced by the national value chain, while the effect of upstream industries is weaker. At the same time, it is well documented that participation in the global value chain can improve the efficiency of regional resource allocation, and the construction of high-tech zones can improve resource allocation for both upstream and downstream industries. Based on the results of study, the authors propose suggestions for optimizing business environments, suiting the national value chain construction, and improving resource allocation in the future.

Panoramic radiographs can assist dentist to quickly evaluate patients' overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.

Background: Developing a screening method for mild cognitive impairment in the aging population and intervening early in the progression of dementia based on such a method, remains challenging. Electroencephalography (EEG) is a noninvasive and sensitive tool to assess the functional activity of the brain, and wireless and mobile EEG (wmEEG) could serve as an alternative screening technique that is widely tolerable in patients with dementia from the preclinical to severe stage.

Materials and methods: Using wmEEG, we recorded bioelectrical activity (BA) from the forehead in 101 individuals with dementia and nondementia controls (NCs) during 4 tasks and investigated which task could differentiate dementia from NC.

Results: We found significant differences in three power spectra of the time-frequency analysis (3-4, 5-7, and 17-23 Hz) between dementia and NC under an eyes-open condition and a significant consistent difference in a specific slow alpha power spectrum (6-8 Hz) between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) under an eyes-closed condition. These results were confirmed by classification analysis using a deep learning method based on the whole wmEEG data sets, in which the accuracy of discriminating dementia from NC under the eyes-open condition was higher than that under the eyes-closed condition (0.71 vs. 0.52, respectively). Moreover, the accuracy of discriminating AD from DLB under the eyes-closed condition was higher than that under the eyes-open condition (0.77 vs. 0.64, respectively).

Conclusion: The result of this pilot study suggests that wmEEG can be a useful tool for recording BA, and that analyzing BA may help to detect early dementia and discriminate dementia subtypes effectively and objectively.

Background: Sublingual atropine is an effective treatment of clozapine-induced hypersalivation. This study aims to investigate the pharmacokinetics of atropine after sublingual and oral administration and study the dose effect of atropine on saliva secretion.

Methods: An interventional cross-over clinical trial where participants received 0.6 mg and 1.2 mg atropine sulfate sublingual solution and 0.6 mg oral tablet. Atropine plasma concentration was measured over 9 hours with validated LC-MS/MS method. Atropine effects on saliva secretion rate, visual acuity and accommodation, and vital signs were assessed.

Results: Four clozapine-treated and three healthy participants were enrolled in the study. The area under the atropine plasma concentration-time curve (AUC_0-∞) was highest after the 1.2 mg sublingual solution administration in comparison with 0.6 mg tablet or sublingual solution (8.58±1.66 µg.L^-1.h vs. 4.65±1.29 vs. 2.98±0.73 µg.L^-1.h, respectively). The C_max for the 0.6 mg and 1.2 mg sublingual solutions was 1.11±0.99 and 1.76±0.62 µg.L^-1, and t_max was 2.18±0.59 and 1.9±0.71 h, respectively. In comparison with the 0.6 mg sublingual solution dose, the saliva secretion reduction was larger after the oral tablet administration (-40% (-59, -22%) vs. -69% (-80, -57)) and largest after the 1.2 mg sublingual solution administration (-79% (-93,-64)).

Conclusion: Both the sublingual and oral atropine are effective in reducing the saliva secretion however at a lower plasma concentration after sublingual administration, with a dose-dependent effect. Both have significantly reduced the blood pressure and pulse rate over 3 hours without significant changes in vision. No major safety concerns were reported.

This paper presents a rational expectation equilibrium model to explore how the financial contagion occurs between the unlinked markets that do not share common fundamentals. In the proposed model, the authors assume two of the three risky assets share no common fundamental factors, but are connected by one intermediate asset via cross fundamentals. Through this channel, investors transmit fundamental risk from one asset to another by dint of the cross fundamentals. This mechanism causes liquidity comovement and subsequently becomes a source of market crisis: Through the contagion mechanism, an initial liquidity shock in one asset can result in a drop tendency in liquidity and price informativeness for another asset. Such comovement in liquidity offers a new explanation for idiosyncratic assets in financial contagion.

Motivated by the practice that e-sellers cooperate with insurance companies to offer consumers the return-freight insurance (RI), this paper aims to investigate the competing e-sellers' RI strategies. Regarding the information asymmetry in the online context, reputation system is widely applied by e-platforms. In an online market with two competing e-sellers that sell the same product but are differentiated in their reputation, this paper builds an analytical model to explore the e-sellers optimal pricing and RI strategies. Combined with sellers' reputation and their RI strategies, the equilibrium outcomes under four cases are discussed. This paper reveals the conditions that e-sellers are willing to offer RI. Specifically, the findings demonstrate that low reputation e-seller is more likely to offer RI. Moreover, when the sellers are more divergent, they are more likely to co-exist in the market. Insurance premium and RI compensation play critical roles in their decisions. RI introduction tends to increase the price, thus offsets the benefits of RI, but does not affect the total consumer surplus.

Option pricing problem is one of the central issue in the theory of modern finance. Uncertain currency model has been put forward under the foundation of uncertainty theory as a tool to portray the foreign exchange rate in uncertain finance market. This paper uses uncertain differential equation involved by Liu process to dispose of the foreign exchange rate. Then an American barrier option of currency model in uncertain environment is investigated. Most important of all, the authors deduce the formulas to price four types of American barrier options for this currency model in uncertain environment by rigorous derivation.

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

The post-disaster emergency medical rescue (EMR) is critical for people's lives. This paper presents a stochastic Petri net (SPN) model based on the process of the rescue structure and a Markov chain model (MC), which is applied to the optimization of the EMR process, with the aim of identifying the key activities of EMR. An isomorphic MC model is developed for measuring and evaluating the time performance of the EMR process during earthquakes with the data of the 2008 Wenchuan earthquake. This paper provides a mathematical approach to simulate the process and to evaluate the efficiency of EMR. Simultaneously, the expressions of the steady state probabilities of this system under various states are obtained based on the MC, and the variations of the probabilities are analyzed by changing the firing rates for every transition. Based on the concrete data of the event, the authors find the most time consuming and critical activities for EMR decisions. The model results show that the key activities can improve the efficiency of medical rescue, providing decision-makers with rescue strategies during the large scale earthquake.