ABSTRACT
Determining the optimal vehicle routing of emergency material distribution (VREMD) is one of the core issues of emergency management, which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events. To summarize the latest research progress, we collected 511 VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software. Subsequently, we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords, research gaps, and future works. The results show that do -mestic scholars and research organizations held an unqualified advantage regarding the number of published papers. However, these organizations with the most publications performed poorly regarding the number of literature citations. China and the US have contributed the vast majority of the literature, and there are close collaborations between researchers from both countries. The optimization model of VREMD can be divided into single-, multi-, and joint-objective models. The shortest travel time is the most common optimization objective in the single-objective optimization model. Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously. In recent literature, scholars have focused on the impact of uncertainty and special events (e.g., COVID-19) on VREMD. Moreover, some scholars focus on joint optimization models to optimize vehicle routes and central locations (or material allocation) simultaneously. So-lution algorithms can be divided into two primary categories, i.e., mathematical planning methods and intelligent evolutionary algorithms. The branch and bound algorithm is the most dominant mathematical planning algorithm, while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms. It is shown that the nondominated sorting genetic algorithm II (NSGA-II) can effectively solve the multiobjective model of VREMD. To further improve the algorithm's performance, re-searchers have proposed improved hybrid intelligent algorithms that combine the ad-vantages of NSGA-II and certain other algorithms. Scholars have also proposed a series of optimization algorithms for specific scenarios. With the development of new technologies and computation methods, it will be exciting to construct optimization models that consider uncertainty, heterogeneity, and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.(c) 2022 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ABSTRACT
Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.
ABSTRACT
The aim of this paper is to conduct a spatial correla-tion study of virus transmission in the Hubei province, China. The number of confirmed COVID-19 cases re-leased by the National Health and Construction Commis-sion, the traffic flow data provided by Baidu migration, and the current situation of Wuhan intercity traffic were collected. The Moran's I test shows that there is a posi-tive spatial correlation between the 17 cities in the Hubei province. The result of Moran's I test also shows that four different policies to restrict inter-city traffic can be issued for the four types of cities. The ordinary least squares regression, spatial lag model, spatial error model, and spatial lag error model were built. Based on the analysis of the spatial lag error model, whose goodness of fit is the highest among the four models, it can be concluded that the speed of COVID-19 spread within a certain region is not only related to the current infection itself but also as-sociated with the scale of the infection in the surrounding area. Thus, the spill-over effect of the COVID-19 is also presented. This paper bridges inter-city traffic and spa-tial economics, provides a theoretical contribution, and verifies the necessity of a lockdown from an empirical point of view.
ABSTRACT
Background: NET is offered to postmenopausal patients (pts) with clinical stage 2/3 ER+/HER2- BC to promotebreast-conserving surgery. Also limited surgical accessibility during the COVID19 pandemic has increased NETutility. Inability to identify ET-resistant disease at diagnosis risks disease progression (PD) and delays more effectivetreatments. Dowsett et al. recently demonstrated that baseline levels of ER, progesterone receptor (PR), Ki67(>15% vs ≤15%), and Ki67 (>10% vs ≤10%) 2-4 weeks (wks) after starting NET may improve appropriate patient(pt) selection for NET (PMC7280290). The ER, PR and Ki67-based prediction model divides pts with primaryER+/HER2- BC into 3 groups for appropriateness for NET: (Group 1) NET is likely to be inappropriate (Allred ER <6or ER 6 and PgR <6), (Group 2) NET may be appropriate and a biopsy for on-treatment Ki67 analysis may beconsidered after 2-4 wks of NET (2A: ER 7 or 8 and PgR <6 and 2B: ER 6 or 7 and PgR ≥6) given that on-treatment Ki67 >10% has been associated with worse outcome (PMC5455353), or (Group 3) NET is appropriate (ER 8 andPgR ≥6). The ALTERNATE trial ( NCT01953588 ) randomized postmenopausal women with clinical stage II or III,ER+ (Allred score 6-8)/HER2- BC to receive anastrozole (ANA), fulvestrant (FUL), or ANA + FUL for 6 months,unless Ki67 was >10% on wk 4 or 12 biopsy, in which case pts were triaged to receive neoadjuvant chemotherapy(NCT) or surgery. As previously reported, the ET-sensitive disease (mPEPI 0 plus pCR) rates were similar acrossthe treatment arms and overall 22% (286 of 1,299) pts had Ki67 >10% at wk 4 or 12. The ALTERNATE trialtherefore provides a large independent data set to evaluate the NET appropriateness model. Results: Among 1,299 eligible pts randomized to receive 6 months of NET, 214 were excluded due to absent HRAllred score (n=41) or absence of pre-treatment and wk 4 Ki67 determinations (n=173). The proportions of theremaining 1,085 pts in Group 1, 2 and 3 were 1% (n=10), 43% (n= 468), and 56% (n=607), respectively. On-studyKi67 >10% prompting conversion from NET to NCT/Surgery occurred in: Group 1 90% (9 of 10), Group 2 30% (141of 468), and Group 3 17% (104 of 607) ( Table 1 ). Among the 1,075 pts in Groups 2 and 3, 260 (24%) pts had Ki67≤15% at baseline (BL), among whom only 14 (5.4%) had Ki67 >10% at wk 4, compared to 231 of the 815 (28.3%)who had BL Ki67 >15% and subsequent Ki67 >10% at wk 4. 2% of pts who remained on NET due to on-treatmentKi67 <10% had PD. Response and PEPI-0 rates by group will be reported. Conclusion: ALTERNATE trial data support a model whereby levels of ER, PR and Ki67 at diagnosis can be usedfor the identification of postmenopausal pts with primary ER+/HER2- BC who are appropriate for NET. Whenbaseline ER Allred scores are >6 and Ki67 ≤15%, there is a low likelihood of ET-resistant disease. When BL Ki67 is>15%, ET sensitivity is variable, and on-treatment biopsy for Ki67 may assist in triaging regarding NETappropriateness, particularly given the extremely low local PD rates seen in ALTERNATE when on-treatment Ki67was <10%. (Table Presented).
ABSTRACT
Beginning at the end of 2019, corona virus disease 2019(COVID-19) caused by sevare acute respiratory syndrome coronavirus(SARS-CoV-2) appeared in Wuhan, China, and spread rapidly across the country. Prior to this, there had been two outbreaks in the world that caused serious consequences by different coronaviruses: severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). This article introduces the structure and classification of coronaviruses, discusses the origin, virological characteristics, and epidemiological overview of three coronaviruses-SARS-CoV, MERS-CoV, and SARS-CoV-2, and reviews the drugs that are currently on the market and are being developed to treat coronavirus infections, in order to explain the characteristics of coronavirus and provide new ideas for the prevention and control of 2019-nCoV and new coronavirus.