Understanding the adoption context of China’s digital currency electronic payment

Central banks worldwide have started researching and developing central bank digital currencies(CBDCs).In the digital economy context,concerns regarding the integrity,competition,and privacy of CBDC systems have also gradually emerged.Against this backdrop,this study aims to evaluate users’willingness to use China’s digital currency electronic payment(DCEP)system,a digital payment and processing network,and its influencing factors by comprehensively considering and comparing the characteristics of cash and third-party payment services.Combining the push-pull-mooring frame-work(PPM)and task-technology fit(TTF)theory,we discuss the scenarios and mecha-nisms that may inspire users’DCEP adoption intention through an empirical study.The results reveal that privacy concerns regarding the original payment methods and technology-task fitting level of DCEP positively impact users’willingness to adopt DCEP.The technical characteristics of DCEP,users’payment requirements,and government support positively affect users’adoption intention by influencing the task-technology fitting degree of DCEP.Switching cost significantly and negatively impacts adop-tion intention,whereas relative advantage exhibits no significant effect.This research contributes to a better understanding of the factors that influence switching intentions and the actual use of DCEP,and provides policy guidance on promoting the efficiency and effectiveness of DCEP.

Mortality associated with gastrointestinal bleeding in children: A retrospective cohort study

To determine the clinical characteristics of children with gastrointestinal bleeding (GIB) who died during the course of their admission.METHODSWe interrogated the Pediatric Hospital Information System database, including International Classification of Diseases, Current Procedural Terminology and Clinical Transaction Classification coding from 47 pediatric tertiary centers extracting the population of patients (1-21 years of age) admitted (inpatient or observation) with acute, upper or indeterminate GIB (1/2007-9/2015). Descriptive statistics, unadjusted univariate and adjusted multivariate analysis of the associations between patient characteristics and treatment course with mortality was performed with mortality as primary and endoscopy a secondary outcome of interest. All analyses were performed using the R statistical package, v.3.2.3.RESULTSThe population with GIB was 19528; 54.6% were male, overall mortality was 2.07%; (0.37% in patients with the principal diagnosis of GIB). When considering only the mortalities in which GIB was the principal diagnosis, 48% (12 of 25 principal diagnosis GIB mortalities) died within the first 3 d of admission, whereas 19.8% of secondary diagnosis GIB patients died with 3 d of admission. Patients who died were more likely to have received octreotide (19.8% c.f. 4.04%) but tended to have not received proton pump inhibitor therapy in the first 48 h, and far less likely to have undergone endoscopy during their admission (OR = 0.489, P < 0.0001). Chronic liver disease associated with a greater likelihood of endoscopy. Mortalities were significantly more likely to have multiple complex chronic conditions.CONCLUSIONGIB associated mortality in children is highest within 7 d of admission. Multiple comorbidities are a risk factor whereas early endoscopy during the admission is protective.

Clinical prognostic factors for disabling Crohn's disease: A systematic review and meta-analysis

AIM: To identify demographic and clinical factors asso-ciated with disabling Crohn's disease (CD). METHODS: A systematic review and meta-analysisof observational studies, focusing on the factors that can predict the prognosis of different outcomes of CD was undertaken. PubMed, ISI Web of Knowledge and Scopus were searched to identify studies investigat-ing the above mentioned factors in adult patients with CD. Studies were eligible for inclusion if they describe prognostic factors in CD, with inclusion and exclusion criteria defined as follows. Studies with adult patients and CD, written in English and studying association between clinical factors and at least one prognosis out-come were included. Meta-analysis of effects was un-dertaken for the disabling disease outcome, using odds ratio (OR) to assess the effect of the different factors in the outcome. The statistical method used was Mantel-Haenszel for fixed effects. The 16-item quality assess-ment tool (QATSDD) was used to assess the quality of the studies (range: 0-42). RESULTS: Of the 913 papers initially selected, sixty studies were reviewed and three were included in the systematic review and meta-analysis. The global QA-TSDD scores of papers were 18, 21 and 22. Of a total of 1961 patients enrolled, 1332 (78%) were classified with disabling disease five years after diagnosis. In two studies, age at diagnosis was a factor associated with disabling disease five years after diagnosis. Individu-als under 40 years old had a higher risk of developing disabling disease. In two studies, patients who were treated with corticosteroids on the first flare developed disabling disease five years after diagnosis. Further, perianal disease was found to be relevant in all of the studies at two and five years after diagnosis. Finally, one study showed localization as a factor associated with disabling disease five years after diagnosis, with L3 being a higher risk factor. This meta-analysis showed a significantly higher risk of developing disabling dis-ease at five years after initial diagnosis among patients younger than 40 years of age (OR=2.47, 95%CI: 1.74-3.51), with initial steroid treatment for first flare (OR=2.42, 95%CI: 1.87-3.11) and with perianal dis-ease (OR = 2.00, 95%CI: 1.41-2.85).CONCLUSION: Age at diagnosis, perianal disease, ini-tial use of steroids and localization seem to be indepen-dent prognostic factors of disabling disease.

Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction,i.e.,the reproduction of short-term(several decades)historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area,can extend the length of climatic time series and offset the shortage of observations.This can be used to assess regional climate change over a much longer time scale.Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5)dataset for the period of 1850–2000,the Climatic Research Unit(CRU)dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR)of China during the period of 1961–2011,we calibrated and validated monthly average temperature(MAT)and monthly accumulated precipitation(MAP)in the TMR using the delta,physical scaling(SP)and artificial neural network(ANN)methods.Performance and uncertainty during the calibration(1971–1999)and verification(1961–1970)periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA)method.The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables,different data sources,and/or different methods used.According to traditional performance indices,both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999.However,the results differed from those obtained by the RSPA method.This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree)during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset.Overall,the calibrated and verified MAP had a high degree of uncertainty(negative connection degree)regardless of the dataset or reconstruction method used.Therefore,the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study.The results of this study will be useful for short-term(several decades)regional climate reconstruction and longer-term(100 a or more)assessments of regional climate change.

Dynamic Routing Optimization Algorithm for Software Defined Networking

Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.

A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route problem.The model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased quantities.The obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight route.The relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport route.The mathematical model including stepped discounted fuel prices is formulated.The problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic case.The stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real situation.Many similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact methods.Therefore,metaheuristic approaches are widely used in treating such different optimization tasks.In this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical model.The algorithm basically based on the process of gaining and sharing knowledge throughout the human lifetime.The generated simulation runs of the example are solved using the proposed algorithm,and the resulting distribution outputs for the optimum purchased fuel amounts from each airport and for the total cost and are obtained.

Does the Environmental Gain of Switching to the Healthy New Nordic Diet Outweigh the Increased Consumer Cost？

The new Nordic diet （NND） was designed by gastronomic, nutritional and environmental specialists to be a palatable, healthy and sustainable diet containing 30%-40% less meat than the average Danish diet （ADD）, ≥ 75% organics, and more locally grown wholegrain products, nuts, fruit and vegetables. In this study, the NND was based on economic modelling to represent a ＂realistic NND bought by Danish consumers＂. The objective was to investigate whether the ADD-to-NND diet-shift has environmental consequences that outweigh the increased consumer cost of the diet-shift. The diet-shift reduced the three most important environmental impacts by 16%-22%, mainly caused by reduced meat content. The surcharge to consumers of the ADD-to-NND diet-shift was ∈216/capita/year. In monetary terms, the savings related to the environmental impact of the diet-shift were ∈151/capita/year. 70% of the increased consumer cost of the ADD-to-NND diet-shift was countered by the reduced socioeconomic advantage associated with the reduced environmental impact of the NND.

Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period.A nonlinear binary mathematical programming model for the problem is formulated.The decision variables are binary ones that represent whether to choose a specic consumer,and design constraints are formulated to keep track of the chosen route.To better illustrate the problem,objective,and problem constraints,a real application case study is presented.The case study involves the optimum delivery of safeguarding substances to several hospitals in the Al-Gharbia Governorate in Egypt.The hospitals are selected to represent the consumers of safeguarding substances,as they are the rst crucial frontline for mitigation against a pandemic outbreak.A distribution truck is used to distribute the substances from the main store to the hospitals in specied required quantities during a given working shift.The objective function is formulated in order to maximize the total amount of delivered quantities during the specied time period.The case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm(DBGSK),which involves two main stages:discrete binary junior and senior gaining and sharing stages.DBGSK has the ability of nding the solutions of the introduced problem,and the obtained results demonstrate robustness and convergence toward the optimal solutions.

The Optimization of Preventive Maintenance Scheduling for Production Machine of Production System in Finite Time Horizon

The recursion relation of preventive maintenance (PM) cycle is built up concerning the concept of effective age and age setback factor proposed in this paper, which illustrates the dynamic relationship between failure rate and preventive maintenance activity. And the nonlinear optimal PM policy model satisfying the reliability constraints in finite time horizon following Weibull distribution is proposed. The model built in this paper avoids the shortcoming of steady analytical PM model in infinite time horizon and can be used to aid scheduling the maintenance plan and providing decision supporting for job shop scheduling.

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

Performance Assessment of European Football Teams： Using Stochastic Data Envelopment Analysis Model

Data Envelopment Analysis （DEA） is a powerful mathematical optimization method widely used for measuring, evaluating and improving the performance of Decision Making Units （DMUs）. These used in the various forms, such as hospitals, government agencies, educational institutions, air force, bank branches, business finns, sport teams and even people including the performance of countries, regions, etc. Recently DEA has been extended to examine the performance through the different sport types. In this paper, a Stochastic Input Oriented Data Envelopment Analysis （SIODEA） Model is conducted for measuring and evaluating the relative efficiency scores of football teams selected from different European countries during 2014/2015 season each with some of inputs are stochastic with normally distributed and recent inputs are deterministic and outputs, to shed light on the professional football teams performance.

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

This study investigates different sources of uncertainty in the assessment of the climate change impacts on total monthly precipitation in the Campbell River basin, British Columbia, Canada. Four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs) and six downscaling methods (DSMs) are used in the assessment. These sources of uncertainty are analyzed separately for two future time periods (2036 to 2065 and 2066 to 2095). An uncertainty metric is calculated based on the variation in simulated precipitation due to choice of GCMs, emission scenarios and downscaling models. The results show that the selection of a downscaling method provides the largest amount of uncertainty when compared to the choice of GCM and/or emission scenario. However, the choice of GCM provides a significant amount of uncertainty if downscaling methods are not considered. This assessment work is conducted at ten different locations in the Campbell River basin.

The stance and factors of international organizations towards countries from a Chinese perspective

The degree and scope of constraints imposed by International Organizations(IOs)on States are increasing,and identifying the factors affecting the IOs’stances on States is helpful to enhance the state's discourse power and influence in the international community.First,by coding the records of Regular Press Conferences of the Speaking Office of the Chinese Ministry of Foreign Affairs during the period of 20182022,we obtained a dataset of IOs’stances on China-related events.Second,we constructed political relation,economic relation,and humanistic relation indicators to complement the influence factors,adopted the Bayesian logit model,and applied the Monte Carlo Markov chain algorithm and Gibbs sampling to analyze the probability of IOs’positive stances towards China.The result shows that IOs’category,length of establishment,functional position,and relationship with China are all related to their tendency of making a statement about China.In terms of the heterogeneity of event types,forum-type IOs are significantly inclined to give positive assessment compared to service-type IOs on events focusing on China's own development.Further analysis reveals that the model for analyzing and predicting the attitudes of IOs is more effective when the international situation is in a stable period.

Price Mechanism,Government Constraints and Carbon Trading Pilot Policy for Emission Reduction

Based on the data of 247 cities at the prefecture level in China from 2007 to 2019,this paper analyzes the impact of the carbon emissions trading(CET)pilot policy on carbon emission reduction from the perspective of the price mechanism and government constraints.The results show that the carbon emissions and carbon intensity in the pilot areas are significantly reduced by adjusting the industrial structure and promoting green technology innovation.In terms of regions,the emission reduction effect of the pilot policy in regions with a high proportion of industry is obviously weaker than that in other regions.The aim of the carbon emission trading policy in China that achieve carbon emission reduction is by coordinating the carbon emission trading price that fail to fulfill this aim independently and the degree of government punishment for enterprises.

Intelligent negotiation model for ubiquitous group decision scenarios

Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents' modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multicriterion problems, agents' reasoning, and intelligent dialogues.

Hazard Assessment and Hazard Mapping for Kuwait

Hazard maps are essential tools to aid decision makers in land-use planning,sustainable infrastructure development,and emergency preparedness.Despite the availability of historical data,there has been no attempt to produce hazard maps for Kuwait.In cooperation with the World Bank,this study investigated the natural and anthropogenic hazards that affect Kuwait.The objective was to assess the hazards that face Kuwait and map the hazards of most concern.Hazard maps depicting the spatial distribution of hazard-prone areas are discussed in this article.Hazard assessment maps were generated using multiple datasets and techniques,including meteorological data,satellite imagery,and GIS.Hazard profiling identified a total of 25 hazards,of which five“priority”hazards were explored in detail:(1)surface water flooding;(2)dust storms and sand encroachment;(3)drought;(4)air pollution;and(5)oil spills.The results of this study can aid decision makers in targeting the hazards of most concern.The developed maps are valuable tools for emergency response and hazard mitigation.

Managing Two-Echelon Remanufacturing Under Patent Protection

We propose a two-echelon remanufacturing model in the context of two periods in which the factor of patent protection of the original manufacturer is considered.Through the analysis of the Stackelberg game among the supplier,the original manufacturer and the remanufacturer we obtain the equilibrium solutions of the unit patent licensing fee of the original manufacturer,the recycling effort degree of the remanufacturer,the wholesale price of new and remanufactured core components and the retail price of new and remanufactured products in the second period which are all dependent on the retail price of new products in the first period in cases with patent protection and without patent protection.In the numerical study we analyze the impacts of the remanufacturing rate of used products and the retail price of new products in the first period on the equilibrium results of the second period,and make a comparison between the case with patent protection and the case without patent protection.

THE TIME INTERVAL ALIGNMENT (IA) POLICIES, BOUNDARY AND APPLICATIONS WITH MULTIPLE STREAM ARRIVALS

This paper discusses a class of interval alignment （IA） scheduling policies, which are particularly effective for the systems that do not have Markovian structure. The numerical results show that IA policies effectively smooth part flows, improve performance and decrease average Work-in-Process （WlP） by adding intermediate delays to the system, The boundary of IA policy is proven and the applications of IA policy in the system with multiple stream arrivals have been discussed. With the combination of release policy, it is practical to implement IA to multiple stream arrival system.

Spatiotemporal graph queries on geographic databases under a conceptual abstraction scale

Visual queries assist non-expert users to extract information from spatial databases in an intuitive and natural approach,making Geographic information systems comprehensive and efficient for a wide range of applications.A common visual means of querying takes the form of drawings or graphs,under which many spatial ambiguity and translation errors rise.In this study,common query attributes extracted from user graphs such as spatial topology,size,cardinality,and proximity are regarded under a conceptual moderation scheme.Thus,the system/user may concentrate on various conceptual combinations of information.Furthermore,time is incorporated to support spatiotemporal queries for changing scenes and moving objects.Arbitrary,relative,and absolute scaling is possible according to the data-set and application at hand.The theoretic approach is implemented under a prototype user interface system,called ShapeController.Under this prototype,a user may extract scene-based relations in an automatically inferred fashion,or include single object-oriented relations when all possible relations seem redundant.Finally,a natural language description of the query is extracted upon which the user may select the desired query relations.Experimentation on a spatial database demonstrates the concepts of predefined draw objects,scaling relaxation,conceptual abstraction,and scene,object-and textual-oriented transitions that promote query expressiveness and restrain ambiguities.