Role of Logistics Integration Capability in Enhancing Performance in Omni-Channel Retailing: Supply Chain Integration as Mediator

Although the importance and benefits of logistics integration in omni-channel (OC) retailing have been discussed in the literature, the impacts of logistics integration from the dimension of internal and external logistics remain unknown. To fill this gap, this study aims to investigate the relationships among internal and external logistics integration capabilities, supply-chain integration (SCI), and financial performance (FP) in OC retailing based on the dynamic capability view. An empirical study is conducted based on a survey of 230 OC retailers in China's market. Factor analysis and regression analysis are conducted to examine the hypotheses of the proposed conceptual model. The quantitative analyses show that the internal logistics integration capability is significantly related to the external logistics integration capability, and they both have positive effects on SCI, while the external logistics integration capability generates a higher impact (i.e., almost 1.5 times that of the internal logistics integration capability). The numerical results also demonstrate that the logistics integration capabilities and SCI have similar positive effects on FP (i.e., all the relevant regression coefficients show values around 0.25), and SCI plays a partial intermediary role in the relationships between logistics integration capabilities and FP. Furthermore, the quantitative evidence addresses the fact that the FP is not influenced by OC retailers' characteristics, indicating a fair business environment in the OC retail industry.

Towards a free energy-based elastic network model and its application to the SARS-COV2 binding to ACE2.

Classical normal mode analysis (cNMA) is a standard method for studying the equilibrium vibrations of macromolecules. A major limitation of cNMA is that it requires a cumbersome step of energy minimization that also alters the input structure significantly. Variants of normal mode analysis (NMA) exist that perform NMA directly on PDB structures without energy minimization, while maintaining most of the accuracy of cNMA. Spring-based NMA (sbNMA) is such a model. sbNMA uses an all-atom force field as cNMA does, which includes bonded terms such as bond stretching, bond angle bending, torsional, improper, and non-bonded terms such as van der Waals interactions. Electrostatics was not included in sbNMA because it introduced negative spring constants. In this work, we present a way to incorporate most of the electrostatic contributions in normal mode computations, which marks another significant step toward a free-energy-based elastic network model (ENM) for NMA. The vast majority of ENMs are entropy models. One significance of having a free energy-based model for NMA is that it allows one to study the contributions of both entropy and enthalpy. As an application, we apply this model to study the binding stability between SARS-COV2 and angiotensin converting enzyme 2 (or ACE2). Our results show that the stability at the binding interface is contributed nearly equally by hydrophobic interactions and hydrogen bonds.

Why does omni-channel allow retailers to foster supply chain resilience? Evidence from sequential mixed methods research

The omni-channel (OC) approach improves efficiency under normal conditions and fosters resilience when a crisis hits, e.g. COVID-19. We adopt the sequential mixed methods research to conduct a two-stage investigation to explore how OC retailers achieve supply chain resilience (SCR). In stage 1, three key capabilities of OC retailers to foster SCR are identified qualitatively, i.e. collaboration, flexibility, and redundancy, as well as the conceptual model is developed based on the dynamic capabilities view. In stage 2, the roles of key capabilities in fostering SCR in three phases, i.e. preparedness, responsiveness, and recovery, is quantitatively examined, by using partial least squares structural equation modelling to test the hypotheses based on a sample of 225 Chinese OC retailers. The findings indicate that flexibility and redundancy are more significant for responsiveness, whilst collaboration is more crucial for recovery, offering valuable insights for supporting retailers in OC transformation and in setting a capabilities portfolio to withstand supply chain disruptions.

Global Myocardial Strain in Multisystem Inflammatory Syndrome in Children, Kawasaki Disease, and Healthy Children: A Network Meta-Analysis.

Background: Nearly 6,000 multisystem inflammatory syndrome in children (MIS-C) have been reported in the United States by November 2021. Left ventricular global myocardial strain has been proved to be one of the best evidence of the diagnostic and prognostic implications for cardiac dysfunction. The global myocardial strain change of MIS-C in the acute phase was still unclear. Methods: PubMed and other sources were searched. A network meta-analysis was conducted. MIS-C was divided into two groups according to left ventricular ejection fraction (LVEF): MIS-C with depressed ejection fraction (MIS-C dEF) and MIS-C with preserved ejection fraction (MIS-C pEF). Global longitudinal strain (GLS) and global circumferential strain (GCS) were compared among MIS-C, Kawasaki disease (KD), and healthy children. Results: In total, nine case-control studies were included, published between 2014 and 2021. These studies involved 107 patients with MIS-C, 188 patients with KD, and 356 healthy children. After Bayesian analysis, MIS-C dEF group was found to have a lower LVEF, higher GLS and GCS than the KD groups. Both MIS-C pEF and KD had similar GLS and GCS, which were higher than healthy controls. There was no difference of LVEF among MIS-C pEF, KD, and healthy controls. Conclusion: MIS-C dEF was more severe than KD, both in LVEF and global myocardial strain. MIS-C pEF and KD were similar with mild impaired left ventricular myocardial strain compared with the healthy children. Global myocardial strain may be a monitoring index for MIS-C. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42021264760].

Association of epicardial adipose tissue with the severity and adverse clinical outcomes of COVID-19: A meta-analysis.

OBJECTIVES: Epicardial adipose tissue (EAT) has been proposed to be an independent predictor of visceral adiposity. EAT measures are associated with coronary artery disease, diabetes, and chronic obstructive pulmonary disease, which are risk factors for COVID-19 poor prognosis. Whether EAT measures are related to COVID-19 severity and prognosis is controversial. METHODS: We searched 6 databases for studies until January 7, 2022. The pooled effects are presented as the standard mean difference (SMD) or weighted mean difference with 95% confidence intervals (CIs). The primary end point was COVID-19 severity. Adverse clinical outcomes were also assessed. RESULTS: A total of 13 studies with 2482 patients with COVID-19 were identified. All patients had positive reverse transcriptase-polymerase chain reaction results. All quantitative EAT measures were based on computed tomography. Patients in the severe group had higher EAT measures compared with the nonsevere group (SMD = 0.74, 95% CI: 0.29-1.18, P = 0.001). Patients with hospitalization requirement, requiring invasive mechanical ventilation, admitted to intensive care unit, or with combined adverse outcomes had higher EAT measures compared to their controls (all P < 0.001). CONCLUSIONS: EAT measures were associated with the severity and adverse clinical outcomes of COVID-19. EAT measures might help in prognostic risk stratification of patients with COVID-19.

Association of Lung Ultrasound Score with Mortality and Severity of COVID-19: A Meta-Analysis and Trial Sequential Analysis.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has rapidly spread all over the world. Lung ultrasound (LUS) has emerged as a useful tool for diagnosing many respiratory diseases. The prognostic role of LUS in COVID-19 patients has not yet been established. METHODS: Several databases were searched on 09 April 2021. The difference in LUS score between the death and survival groups, and the relationship between LUS score and COVID-19 severity were both assessed. RESULTS: The LUS score was significantly higher in the death group compared with the survival group (weighted mean difference (WMD) = 8.21, 95% CI: 4.74-11.67, P < 0.001), which was confirmed by trial sequential analysis. Those with mild/moderate, severe and critical COVID-19 had a progressively higher LUS score (critical vs. severe: WMD = 8.78, 95% CI: 4.17-13.38; P < 0.001; critical vs. mild/moderate/severe: WMD = 10.00, 95% CI: 6.83-13.17, P < 0.001; severe vs. moderate: WMD = 5.96, 95% CI: 3.48-8.44, P < 0.001; severe vs. mild/moderate: WMD = 7.31, 95% CI: 4.45-10.17, P < 0.001). CONCLUSIONS: The LUS score was associated with mortality and severity of COVID-19. The LUS score might be a risk stratification tool for COVID-19 patients.