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The 2019 coronavirus disease (COVID-19) epidemic has caused serious disruptions in food supply networks. Based on the case of the remerging epidemic in China, this paper aims to investigate food supply network disruption and its mitigation from technical and structural perspectives. To solve the optimal policy choice problem that how to improve mitigation capability of food supply networks by using traceability technology and adjusting network structure, the occurrence mechanism of food supply network disruptions is revealed through a case study of the remerging COVID-19 outbreak in Beijing's Xinfadi market. Five typical traceability solutions are proposed to mitigate network disruptions and their technical attributes are analyzed to establish disruption mitigation models. The structure of food supply networks is also controlled to mitigate disruptions. The structural attributes of three fundamental networks are extracted to adjust the network connections pattern in disruption mitigation models. Next, simulation experiments involving the disruption mitigation models are carried out to explore the independent and joint effects of traceability technology and network structure on mitigation capability. The findings suggest that accuracy makes a more positive effect on the mitigation capability of food supply networks than timeliness due to the various technical compositions behind them; the difference between these effects determines the choice decision of supply networks on traceability solution types. Likewise, betweenness centralization makes a positive effect but degree centralization makes a negative effect on mitigation capability because intermediary firms and focal firms in food supply networks have different behavior characteristics; these effects are both regulated by supply network types and exhibit different sensitivities. As for the joint effect of technical and structural attributes on mitigation capability, the joint effect of accuracy and betweenness centralization is bigger than the independent effects but smaller than their sum; the joint effect of timeliness and betweenness centralization depends on networks type; while the positive effect of accuracy or timeliness on mitigation capability is greater than the negative effect of degree centralization; theses joint effects are caused by the complicated interactive effects between technical composition and behaviors of intermediary firms or focal firms. These findings contribute to disruption management and decision-making theories and practices.

Objective It is well established that schizophrenia patients have high cardiovascular morbidity and mortality. However, the underlying risk factors in the earliest stages of both schizophrenia illness and antipsychotics treatment are less clear. This study aimed to characterize the metabolic features of those patients. Methods We performed a retrospective cohort study in a naturalistic setting, which included antipsychotic-naive, first-episode schizophrenia (FES) inpatients with the baseline metabolic measurements and changes following a short term treatment with antipsychotic drugs. Results Although prevalence of hypertriglyceridemia, hypercholesterolemia, higher-LDL-C and hyperglycaemia in patients with FES were much lower than those of the general population (7.5% v.s. 14.9%, 9.2% v.s. 18.4%, 8.1% v.s. 14.9%, 8.6% v.s.18.3%, respectively), lower-HDL-C in patients with FES were much more prevalent than that of the general population (19.9% v.s. 6.4%). Despite significant metabolic risk profiles (as such lipid abnormalities and insulin resistance) increase, mean fasting glucose and glucosylated serum protein (GSP) were significantly decreased after the short term (median of 23 days) antipsychotics exposure, compared to baseline. There is no significant difference of the metabolic profile change between monopharmacy and polypharmacy. Conclusion These results indicated an early-onset nature of HDL-C abnormalities in drug-naive FES patients. Lipids metabolism risk may develop early and quickly after antipsychotic exposure. Early monitoring is required for the purpose of early detection and hence prevention of the initial metabolic risk which may lead to diabetes mellitus and cardiovascular disease.

Objective and method We performed a case-control study, which included antipsychotic-naive first-episode schizophrenia (FES) of adolescents and young adults and general population controls, to investigate the early-onset cardiometabolic risk factors in FES. Results FES had more frequent lower HDL-C when compared to controls. However, the distribution of BMI and the frequency of hypercholesterolemia, elevated LDL-C, hypertriglyceridemia in FES were not significantly different to controls. Conclusions The results indicated that abnormal HDL-C might be an early-onset event in drug-naive FES of adolescents and young adults who are unlikely to have other cardiometabolic risks.

Franchisee has become a powerful tool for many logistics enterprises in affiliate mode to seize market share. But affiliate mode has emerged many drawbacks that can aggravate the divergence of interests between franchisees and headquarter and then leads to the problem of franchisee loss. In this paper, the franchisee loss problem is studied from the perspective of vulnerability. The loss of a franchisee is seen as a node that has been attacked. The performance change before and after an attack is used to reflect the strength of a network’s vulnerability. Efficacy and efficiency are proposed to measure the performance change based on NK model, which specializes in studying complex adaptive system such as logistics network. Results show that efficacy and efficiency vary according to network scale. For relatively small networks, their efficacy shows more obvious fluctuation with the variation of network scale than efficiency does. In these networks, the redundancy of network nodes is high. But the percentage of redundant nodes decreases with the increase of scale and the efficacy can be improved with the reduction of redundant nodes. The network structure of them is not stable. As for relatively large networks, efficiency fluctuates more apparently with the change of complexity than efficacy does. In these networks, most nodes are influential and the percentage of influential nodes shows obvious change with complexity. The network structure tends to be stable. In conclusion, the study on the influence of franchisee loss from vulnerability can provide us with greater visibility and insight into the organization structure of logistics networks. Corresponding cases are introduced to confirm our conclusions.