澳洲坚果主导品种Own Choice的品种配置研究

为了明确澳洲坚果园主导品种Own Choice(O.C)的最适配置品种,选择HAES863、D、D4、HAES816、HAES906、HAES294、HAES900、A4等8个品种为O.C授粉,统计分析不同授粉处理的着果率、杂交不亲和性程度指数、着果花序率、每果序着果数、落果率、壳果横径等指标。结果表明,以着果率及着果花序率统计的杂交不亲和性程度指数发现,HAES863、D、D4、HAES816、HAES906与O.C的杂交亲和性高于A4、HAES294、HAES900。供试品种处理的着果率与着果花序率之间存在正相关关系,初步推断可使用着果花序率代替着果率评价O.C与不同品种的杂交不亲和性程度;8个授粉处理的平均着果花序率为40.60%,可以此推断O.C与不同授粉品种的田间杂交不亲和性程度。综合分析推断,澳洲坚果主导品种O.C的最适配置品种为HAES863和D4,根据小气候可选择性搭配种植D、HAES906、HAES816。

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

A comparison of model choice strategies for logistic regression

Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.

Editor-in-Chief articles of choice and comments from January to June 2024

As the Editor-in-Chief of the World Journal of Gastroenterology,I carefully review all articles every week before a new issue’s online publication,including the title,clinical and research importance,originality,novelty,and ratings by the peer reviewers.Based on this review,I select the papers of choice and suggest pertinent changes(e.g.,in the title or text)to the company editors responsible for publication.This process,while time-consuming,is essential for assuring the quality of publications and highlighting important articles that readers may revisit.

Editor-in-Chief articles of choice and comments at the year-end of 2023

As the Editor-in-Chief of World Journal of Gastroenterology,every week prior to a new issue’s online publication,I perform a careful review of all encompassed articles,including the title,clinical and/or research importance,originality,novelty,and ratings by the peer reviewers.Based on this review,I select the papers of choice and suggest pertinent changes(e.g.,in the title)to the Company Editors responsible for publication.This process,while time-consuming,is very important for assuring the quality of publications and highlighting important articles that Readers may revisit.

Type 2 Diabetes—Hard to Select a Healthy Choice

Introduction: The use of foods containing high levels of sugar is increasing all the time. This is a risk factor for increased incidence of type 2 diabetes. There are few studies that have investigated the availability of low-sugar müsli products in grocery stores. Purpose: The study aims to identify which types of müsli contain high respectively low levels of sugar, and which brands are involved. Methods: The material consists of both qualitative interviews and observations from five grocery stores: City Gross, Hemköp, Ica Maxi, Stora Coop and Willy’s in Helsingborg, Sweden. The qualitative interviews had a semi-structured character and were recorded. The interviews took approx. 20 minutes and a textual analysis was conducted of the results. Data from observation was analyzed based on brand, nutritional composition and flavors, and also, where low sugar products were placed on store shelves. Results: The grocery stores provided together brands from AXA, Coop, Finax, Frebaco, Garant, ICA, Risenta, Saltå Kvarn och Urtekram, in total 24 müsli products. Of these products, 19 were high in sugar. The observation reveals that müsli products with high sugar content (17 - 29 g per 100 g müsli) are more prominently displayed than those with low sugar content. From the interviews with the store managers, it became clear that it would be valuable to highlight healthy müsli products on the shelves. However, central bureaucracy puts obstacles to such measures. Discussion: The study emphasizes the need for increased visibility of low-sugar products and proposes solutions such as negotiating with responsible person at the head office in Stockholm. Several reviews have shown that if the grocery store raises the prices of unhealthy food, the consumer is willing to purchase healthier müsli and other products. Conclusion: This study shows the need for grocery stores to upgrade healthy müsli products along with advertising to be able to influence customer’s shopping habits. Also, further research is needed how type 2 diabetes is affected by high intakes of food products with high sugar content.

Discrete Choice Analysis of Temporal Factors on Social Network Growth

Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.

Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.

The Ethical Choice of Business English Interpreters under Chesterman’s Model of Translation Ethics

This paper explores the ethical challenges encountered by business English interpreters using Chesterman’s Model of Translation Ethics,set against the context of economic globalization and the“Belt and Road”initiative.With the increasing demand for interpreters,the paper delves into the ongoing discussion about the role of AI in translation and its limitations,especially concerning cultural subtleties and ethical issues.It highlights the importance of human interpreters’cross-cultural understanding and the ethical principles that inform their work,such as the Ethics of Representation,Service,Communication,Norm-based Ethics,and Commitment.Moreover,the paper examines how these ethical models are applied in practical business situations,including business banquets,business negotiations,business talks and business visits,etc.,and investigates the cultural misunderstandings that may occur during these interactions.The study concludes that although AI provides efficiency and cost savings,human interpreters are essential for their capacity to handle the intricacies of cross-cultural communication with cultural awareness and ethical discernment.

The Influence of Social Media on College Choice among Undergraduates Majoring in Big Data Management and Application in China:Taking Guilin University of Electronic Technology as an Example

This study aims to investigate the influence of social media on college choice among undergraduates majoring in Big Data Management and Application in China.The study attempts to reveal how information on social media platforms such as Weibo,WeChat,and Zhihu influences the cognition and choice process of prospective students.By employing an online quantitative survey questionnaire,data were collected from the 2022 and 2023 classes of new students majoring in Big Data Management and Application at Guilin University of Electronic Technology.The aim was to evaluate the role of social media in their college choice process and understand the features and information that most attract prospective students.Social media has become a key factor influencing the college choice decision-making of undergraduates majoring in Big Data Management and Application in China.Students tend to obtain school information through social media platforms and use this information as an important reference in their decision-making process.Higher education institutions should strengthen their social media information dissemination,providing accurate,timely,and attractive information.It is also necessary to ensure effective management of social media platforms,maintain a positive reputation for the school on social media,and increase the interest and trust of prospective students.Simultaneously,educational decision-makers should consider incorporating social media analysis into their recruitment strategies to better attract new student enrollment.This study provides a new perspective for understanding higher education choice behavior in the digital age,particularly by revealing the importance of social media in the educational decision-making process.This has important practical and theoretical implications for higher education institutions,policymakers,and social media platform operators.

Combinatorial Model Involving Stochastic Choices of Destination, Mode and Route

Traffic assignment models are one of the basic tools for the analysis and design of transportation systems. However, the existing models have some defects. Considering the characteristics of Chinese urban mixed traffic and the randomness of transportation information, the author develops a combinatorial model involving stochastic choices of destination, mode and route. Its uniqueness and equivalance are also proved by the optimization theory.

Influence of land use characteristics and trip attributes on commuting mode choice: a case of Nanjing

The effects of socio-demographics, land use characteristics and trip attributes on the commute mode choice are studied with a nested logit （NL） model. Based on the random utility maximum theory, the NL model is formulated. The analysis is carried out in the main area of Nanjing. Direct and cross elasticities are calculated to analyze the effects of travel time and travel cost on the selection of travel mode choice. The results reveal that the non-motorized travel modes are more attractive in the areas with higher housing and employment accessibility and car owners are found to be more likely to commute to work by car. The bus and subway choice probabilities are more sensitive to changes in travel times than to changes in travel costs. In conclusion, a comprehensive public transit system and effective integration of land use and transportation policies help to relieve the traffic congestion levels caused by the increasing urban sprawl.

Curb parking pricing method based on parking choice behavior

In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.

Modeling commuters' route choice behavior under pre-trip information system

Research about the auto commuter＇s pre-trip route choice behavior ignores the combined influence of the real-time information and all respondents＇ historical information in the existing documents.To overcome this shortcoming,an approach to describing the pre-trip route choice behavior with the incorporation of the real-time and historical information is proposed.Two types of real-time information are investigated,which are quantitative information and prescriptive information.By using the bounded rationality theory,the influence of historical information on the real-time information reference process is examined first.Estimation results show that the historical information has a significant influence on the quantitative information reference process,but not on the prescriptive information reference process.Then the route choice behavior is modeled.A comparison is also made among three route choice models,one of which does not incorporate the real-time information reference process,while the others do.Estimation results show that the route choice behavior is better described with the consideration of the reference process of both quantitative and prescriptive information.

An empirical study on travel demand management modeling based on discrete choice method

In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.

Logit-based exit choice model of evacuation in rooms with internal obstacles and multiple exits

A logit-based discrete choice model is proposed to study the exit choice behaviour of evacuees in rooms with internal obstacles and multiple exits. Several factors influencing the exit choice behaviour, including the information obtained by evacuees, the tendency of following others, the visibility and familiarity of exits and the physical conditions of nearby exits, are considered. Evacuees are allowed to re-select their target exits for minimizing the perceived disutility during evacuation process. Numerical results from applying the model to cellular automata simulation of evacuation are presented and the effects of some model parameters on evacuation time are investigated.

农户经济视角下的我国农村土地流转影响因素——基于Multiple Choice Model的调查研究

利用农户土地流转问卷调查所得到的微观数据,采用统计分析、Probit multiple choicemodel方法,将农户经济行为中的保障性行为、投资行为、就业行为和产业选择行为引入土地流转的实证研究领域。研究结果显示:目前的土地流转多为暂时性流转,土地流转结构性供需矛盾严重;农户对土地的需求目的更多来自于保障性需要;农户的投资行为、就业行为、社会保障行为对土地流转有着重要影响。

Joint analysis of urban shopping destination and travel mode choice accounting for potential spatial correlation between alternatives

In recent years,there have been important developments in the joint analysis of the travel behavior based on discrete choice models as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class.The objective of this work is to describe the simultaneous choice of shopping destination and travel-to-shop mode in downtown area by making use of the cross-nested logit(CNL) structure that allows for potential spatial correlation.The analysis uses data collected in the downtown areas of Maryland-Washington,D.C.region for shopping trips,considering household,individual,land use,and travel-related characteristics.The estimation results show that the dissimilarity parameter in the CNL model is 0.37 and significant at the 95% level,indicating that the alternatives have high spatial correlation for the short shopping distance.The results of analysis reveal detailed significant influences on travel behavior of joint choice shopping destination and travel mode.Moreover,a Monte Carlo simulation for a group of scenarios arising from transportation policies and parking fees in downtown area,was undertaken to examine the impact of a change in car travel cost on the shopping destination and travel mode switching.These findings have important implications for transportation demand management and urban planning.

Investigation on transformation process of trip mode choice for planned special events

This paper aims to provide a decision-making method for the transportation management strategies in guiding the transformation of trip mode choice during planned special events. The Expo 2010 Shanghai is taken as an example, and a structural equation model is employed to analyze the dynamic mechanism of trip mode choice behavior and the effectiveness of the transportation management measures at different stages. Based on the difference between the objective-oriented stated preference （SP） survey results and the objectives, together with the feedback from the previous stage survey, some adjustments on the transportation management measures are made in the next stage of the planning process until the objectives are eventually achieved. The results indicate that the adjustments on transportation management measures at different stages can effectively raise the transit share to 88.6%. Nonlocal visitors are inclined to choose nonstop modes of transportation and the companion attributes have the most significant effects on the trip mode choices of visitors. The research method is proved to be an effective way to support the decision making process of transportation management measures during planned special events in the future.

Application of discrete choice model in trip mode structure forecast:a case study of Bengbu

In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics are obtained from statistical data,while the trip mode split data is collected through a trip survey in Bengbu.In addition,the discrete choice model is adopted to build the functional relationship between the mode choice and the travelers＇ personal characteristics,as well as family characteristics and trip characteristics.The model shows that the relationship between the mode split and the personal,as well as family and trip characteristics is stable and changes little as the time changes.Deduced by the discrete model,the mode split result is relatively accurate and can be feasibly used for trip mode structure forecasts.Furthermore,the proposed model can also contribute to find the key influencing factors on trip mode choice,and restructure or optimize the urban trip mode structure.