The Strategic Analysis of Demand Forecast-Sharing in a Hybrid-Format Online Platform Supply Chain

This paper examines the optimal forecast-sharing strategy in a hybrid-format online platform supply chain where a supplier sells a product through agency format and reselling format provided by a platform retailer who possesses demand forecasts from two channels.Forecast asymmetry and co-opetitive relationship arise between the platform retailer and the supplier,which affect their operational decisions and the supply chain’s performance.To improve supply chain efficiency,we compare different forecast-sharing strategies(i.e.,no forecast sharing,sharing a single forecast,and sharing two forecasts),and analyze the effects of co-opetitive parameters on the optimal forecast-sharing strategy.Our analysis shows that forecast sharing is always beneficial to the supplier,and sharing two forecasts is more beneficial than sharing a single forecast.Whereas for the platform retailer and the whole supply chain,forecast sharing is beneficial only under certain conditions,depending on the co-opetitive parameters.The optimal forecast-sharing strategy is the result of a combination of the negative effect of double marginalization in reselling channel and the positive effect of responding pricing to demand uncertainty in agency channel.We illustrate the parameter regions of the platform retailer’s voluntary sharing,contract sharing,and no sharing,and also find that higher channel competition intensity,higher market share of agency channel,and higher commission rate can promote the platform retailer’s voluntary sharing.Our study extends the research scope of demand forecast-sharing and sheds light on the decision-making processes for managing a hybrid-format online platform supply chain.

Computation Analysis of Brand Experience Dimensions: Indian Online Food Delivery Platforms

Online Food Delivery Platforms(OFDPs)has witnessed phenomenal growth in the past few years,especially this year due to the COVID-19 pandemic.This Pandemic has forced many governments across the world to give momentum to OFD services and make their presence among the customers.The Presence of several multinational and national companies in this sector has enhanced the competition and companies are trying to adapt various marketing strategies and exploring the brand experience(BEX)dimension that helps in enhancing the brand equity(BE)of OFDPs.BEXs are critical for building brand loyalty(BL)and making companies profitable.Customers can experience different kinds of brand experiences through feeling,emotions,affection,behavior,and intellect.The present research work is taken up to analyze the factors affecting BEX and its impact on BL and BE of the OFDPs and analyze the mediating role of BL in the relationship between BEX and BE of the OFDPs in the Indian context.A survey of 457 Indian customers was carried out.A questionnaire was used for data collection and a mediation study was used to test hypothesized relationships.Our computational analysis reveals that BEX influences the BL and BE of OFDPs.The study further indicates that BL mediates the relationship between BEX and BE of OFDPs.The effective marketing and relationship management practices will help company to enhance BEX that will enable in enhancing BL and raising BE of their product.It therefore provides a more thorough analysis of BEX constructs and their consequences than previous research.Some of the managerial implication,limitations,and scope of future research are also presented in the study.

Design and Implementation of Online Experimental Platform for Computer Networks Course

Practice training is very important for students learning Computer networks.But building a real laboratory is constrained and expensive.In this paper,we present an online experimental platform for computer networks course based on Dynamips simulator.Instructors and students can access the platform by IE Browser to manage and take router experiments.On the basis of deployment and testing,the platform is effective and flexible.

Leveraging Zibo Barbecue’s Success to Develop Hebei’s Culinary Brand:A Strategic Path

In 2023,Zibo barbecue culture exploded across the entire country,allowing people nationwide to experience and enjoy Zibo barbecue.This phenomenon injected new vitality into the economic development of Zibo.To promote the economic development of Hebei Province,this paper fully analyzes the reasons behind the popularity of Zibo barbecue,combines these insights with the characteristics of traditional cuisine in Hebei,and draws lessons from Zibo barbecue’s success as a business card for Zibo.The paper then outlines a strategy for building a culinary business card for Hebei.

Learning Mathematics Online Using Information Technology Tools during COVID-19 in Kosove

The COVID-19 pandemic has brought significant challenges to higher education worldwide. Due to the COVID-19 pandemic, e-learning has begun to be widely used and applied in the teaching and learning processes. However, learning under technological circumstances has proven not always to be a proper solution in education. A highlight challenge, in this regard, is considered to be learning Mathematics online. While some support its positive impact, others greatly oppose it by arguing that neither teaching nor learning has proven successful. Thus, this study examines Kosovo selected universities to see the effectiveness of learning Mathematics online as a case study. Further, it compares the online and traditional learning methods and explores how teachers in higher education in Kosova Universities apply and integrate technology into learning mathematics. This study employed a methodology encompassing questionnaires for students. The results show that students are not overall satisfied with learning Mathematics online leading to the conclusion that online learning is not an effective educational method for learning Mathematics.

Development of Online Based Smart House Renting Web Application

The house rental issue is one of the elemental parts of society. Nowadays, it is extremely difficult to find suitable accommodation in city areas if people search for it physically. On the other hand, the land owner also needs to rent the house. It can be difficult to find tenants just to hang a lease sign on a building, and as a result, they lose money. An online common platform can play a vital role in this case. The purpose of the study is to develop a common web-based online platform for both tenants and house owners so that both tenants and landowners will mutually benefit from the system. This paper presents the development of web applications for the people of Bangladesh where both house owners and tenants can register and tenants can have houses for rent via sophisticated contact with the house owner. In this paper, a common online-based smart house rental web application has been developed both for tenants and for house owners. This web application is very user-friendly, efficient and it has got many unique features that are not offered by other currently available house rental websites here in Bangladesh. Tenants can register using their phone number, store information about their identity, search for available houses, send messages to house owners, and choose a suitable house using developed web applications. House owners can also register for the system, which will manually verify and authenticate the knowledge provided by the house owner can view a tenant’s information history whenever a tenant makes contact through text and supply house-related information accordingly. The proposed online smart house system has been tested and validated. It works very efficiently with many features. The application provided faster and improved opportunities to get houses, as well as ensuring the availability of houses for rent in the greatest number of areas. The system will help to spread trustworthy services nationwide and supply users with the chance to speak and improve the house rent in Bangladesh. Because it has many smart features, this developed online smart house rental web application will make it very easy for tenants to find a house to rent. House owners, on the other hand, can easily rent out their properties.

Current Trends in Online Programming Languages Learning Tools: A Systematic Literature Review

Students face difficulties in programming languages learning (PLL) which encourages many scholars to investigate the factors behind that. Although there a number of positive and negative factors found to be effective in PLL procedure, utilising online tools in PLL were recognized as a positive recommended means. This motivates many researchers to provide solutions and proposals which result in a number of choices and options. However, categorising those efforts and showing what has been done, would provide a better and clear image for future studies. Therefore, this paper aims to conduct a systematic literature review to show what studies have been done and then categorise them based on the type of online tools and the aims of the research. The study follows Kitchenham and Charters guidelines for writing SLR (Systematic Literature Review). The search result reached 1390 publications between 2013-09/2018. After the filtration which has been done through selected criteria, 160 publications were found to be adequate to answer the review questions. The main results of this systematic review are categorizing the aims of the studies in online PLL tools, classifying the tools and finding the current trends of the online PLL tools.

Research on The Blended Teaching Mode of College English Based on Cloud Platform

With the development of domestic higher education towards the popularization of education and the integration of global economy,as a basic course in colleges and universities,college English has been playing an important role in the process of higher education.Therefore,under the guidance of scientific and technological innovation and information technology,it is necessary and worthy trying to explore and establish a practical English blended teaching mode to improve college students’English comprehen⁃sive ability.This essay attempts to discuss the problems and reflections of blended teaching mode,explore the college English teaching strategy under the blended teaching mode,which establishes the practical English blended teaching mode,promote teach⁃ing equity and benefit teachers and students.

The influence of different types of satisfaction on loyalty on C2C online shopping platform:From the perspective of sellers and the platform

With the rise and development of major types of platforms,the competition for resources has become extremely fierce,and the market share of C2C platforms has been seriously threatened by the loss of resources.Therefore,building and maintaining buyers’satisfaction and loyalty to C2C platforms is critical to the survival and sustainability of C2C platforms in China.However,the current knowledge on how platform satisfaction and loyalty are constructed in the C2C e-commerce environment is incomplete.In this study,seller-based satisfaction and platform-based satisfaction are constructed separately.We further distinguish seller-based transaction satisfaction into economic and social satisfaction and explore their antecedents and consequences.To test our research hypotheses,we conduct a survey and collect data from a real online market(Taobao website).The results show that seller-based transaction satisfaction positively affects platform-based overall satisfaction and loyalty,and that perceived product quality,perceived assurance,and perceived price fairness all have a significant effect on economic satisfaction,whereas perceived relationship quality and perceived empathy significantly influence social satisfaction.These findings help us understand the literature related to customer satisfaction in the context of C2C in China and provide inspiration for online sellers and platforms.

The CatMath: an online predictive platform for thermal + electrocatalysis

The catalytic volcano activity models are the quantified and visualized tools of the Sabatier principle for heterogeneous catalysis, which can depict the intrinsic activity optima and trends of a catalytic reaction as a function of the reaction descriptors, i.e., the bonding strengths of key reaction species. These models can be derived by microkinetic modeling and/or free energy changes in combination with the scaling relations among the reaction intermediates. Herein, we introduce the CatMath—an online platform for generating a variety of common and industrially important thermal + electrocatalysis. With the CatMath, users can request the volcano models for available reactions and analyze their materials of interests as potential catalysts. Besides, the CatMath provides the function of the online generation of Surface Pourbaix Diagram for surface state analysis under electrocatalytic conditions, which is an essential step before analyzing the activity of an electrocatalytic surface. All the model generation and analysis processes are realized by cloud computing via a user-friendly interface.

Legislation to Regulate the Working Hours and Labor Intensity of Practitioners in the Platform Economy

Under the employment model of online car-hailing platforms,the extension of working hours and increase of labor intensity for practitioners are driven by the orders from these platform companies and labor-related third parties.Also,they are more attributable to the internal driving force of practitioners themselves who hope to have more orders and earn more.It is impossible to apply the regulation system of the existing labor law to protect the rights and interests of employees in terms of working hours and labor intensity.The author suggests that the state should make special labor legislation to make the following targeted provisions:Online car-hailing platform companies and labor-related third parties that implement labor management and control over practitioners shall,on the premise of abiding by labor standards and industry rules,set various reasonable indicators of working hours and labor intensity through rules and take effective technical measures to control these two dimensions,so that practitioners can avoid serious overwork.Besides,by encouraging the provision of certain convenience for practitioners to take intermittent breaks,jointly building a compatibility system for different online car-hailing platforms,and establishing relevant operating rules,we can restrain practitioners from being seriously overworked.This is actually a multi-cooperative boosting legislative regulation approach.

Facilitating College English Teaching with the Aids of On-line Platforms from the Perspective of SLA

With the development of information technology and under the effect of epidemic Covid-19,college English teaching has been considerably influenced and enriched by the support of on-line learning platforms apart from traditional classroom instruction.The paper discusses how these online learning platforms combined with textbooks can facilitate English learning by addressing some crucial factors from the point ofview ofsecond language acquisition(SLA).

面向在线医疗平台的医生能力综合评价体系构建

目的基于服务质量模型、知识贡献理论、结构-过程-结果三维质量评价模型构建面向在线医疗平台的医生能力综合评价指标体系,以期助力平台打造具有公信力的医生榜单,促进患者精准评估医生能力和服务水平。方法采用文献分析法建立初始评价指标体系框架,邀请专家对指标进行筛选和评估,最后通过层次分析法对各级评价指标的权重赋值。结果建立的医生能力综合评价体系包含3个一级指标、7个二级指标、29个三级指标,其中一级指标(及其权重)分别为医生基础条件(0.420)、医生知识贡献(0.219)、医生服务表现(0.361)。结论研究构建了一套科学、可靠的面向在线医疗平台的医生能力综合评价体系,可为平台建立系统化的医生评价标准提供信息基础和参考依据。

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.

An Overview of Autonomous Learning and Reflections on EFL Teaching

This paper mainly focuses on the development of autonomous learning worldwide and its reflections on English teachingin China.Autonomous learning,which is beneficial to one’s lifelong journey,is the ability to take responsibility for one’s learning.The final goal of education is to cultivate students with autonomous learning skills.As for EFL teachers,three steps are as followed.Firstly,EFL teachers need to be trained to learn autonomously.Secondly,EFL teachers should construct some online autonomouslearning platforms.Thirdly,EFL teachers need to motivate learners to listen and watch more about English to arouse students’ in-terests in the target language.

Proliferation of Cartographic Education in the Age of Big Data

Maps have long been a part of everyday life for the general public,and even more so in today’s knowledge society.No doubt,cartography as a profession of map design is assuming a more important role in the formation of intellectual skills in terms of spatial reasoning.Since its emergence as an academic discipline about 100 years ago,cartography has undergone many paradigm shifts.Its interaction with other disciplines has also constantly unfolded.These changes have left traces in cartographic education programs.In the age of big data,however,we are facing four fundamental challenges:(1) cartographic courses are being marginalized or even disappearing from degree programs in geospatial sciences;(2) the role of cartographers is increasingly eclipsed as a side effect of participatory cartography;(3) cartographers are blamed whenever something goes wrong with map use;and(4) professional map publishers can hardly compete with online mapping platforms dominated by Internet giants.Based on a contextual analysis of this seemingly gloomy situation,the paper reveals a number of proliferation points for the design of future cartographic curricula.First,cartography,once dedicated to supporting geospatial sciences,is thriving in the soil of data science,mapping not only the earth or other celestial bodies,but literally any kind of virtual space.Second,cartography has benefited from theoretical and technological advances in cognitive sciences,especially non-intrusive user studies,so that spatial cognition is becoming an integral component of cartographic education.Third,the role of scapegoat for wrongdoing of maps has accentuated cartographer’s overarching responsibility for quality and ethical issues in the geodata value chain.Finally,the diversification of the labor market requires new approaches to prepare future talents for a coopetition-oriented ecosystem in the marketplace.

DeepPredict:A Zone Preference Prediction System for Online Lodging Platforms

Online lodging platforms have become more and more popular around the world.To make a booking in these platforms,a user usually needs to select a city first,then browses among all the prospective options.To improve the user experience,understanding the zone preferences of a user's booking behavior will be helpful.In this work,we aim to predict the zone preferences of users when booking accommodations for the next travel.We have two main challenges:(1)The previous works about next information of Points Of Interest(Pals)recommendation are mainly focused on users'historical records in the same city,while in practice,the historical records of a user in the same city would be very sparse.(2)Since each city has its own specific geographical entities,it is hard to extract the structured geographical features of accommodation in different cities.Towards the difficulties,we propose DeepPredict,a zone preference prediction system.To tackle the first challenge,DeepPredict involves users'historical records in all the cities and uses a deep learning based method to process them.For the second challenge,DeepPredict uses HERE places API to get the information of pals nearby,and processes the information with a unified way to get it.Also,the description of each accommodation might include some useful information,thus we use Sent2Vec,a sentence embedding algorithm,to get the embedding of accommodation description.Using a real-world dataset collected from Airbnb,DeepPredict can predict the zone preferences of users'next bookings with a remarkable performance.DeepPredict outperforms the state-of-the-art algorithms by 60%in macro Fl-score.

A Study of Detecting Student Engagement Modes in an Online Learning Platform

In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and easy.This study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.

Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer

Triple-negative breast cancer(TNBC)is the most challenging breast cancer subtype.Molecular stratification and target therapy bring clinical benefit for TNBC patients,but it is difficult to implement comprehensive molecular testing in clinical practice.Here,using our multi-omics TNBC cohort(N=425),a deep learning-based framework was devised and validated for comprehensive predictions of molecular features,subtypes and prognosis from pathological whole slide images.The framework first incorporated a neural network to decompose the tissue on WSIs,followed by a second one which was trained based on certain tissue types for predicting different targets.Multi-omics molecular features were analyzed including somatic mutations,copy number alterations,germline mutations,biological pathway activities,metabolomics features and immunotherapy biomarkers.It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation,germline BRCA2 mutation and PD-L1 protein expression(area under the curve[AUC]:0.78,0.79 and 0.74 respectively).The molecular subtypes of TNBC can be identified(AUC:0.84,0.85,0.93 and 0.73 for the basal-like immune-suppressed,immunomodulatory,luminal androgen receptor,and mesenchymal-like subtypes respectively)and their distinctive morphological patterns were revealed,which provided novel insights into the heterogeneity of TNBC.A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes(log-rank P<0.001).Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA(N=143)and appeared robust to the changes in patient population.For potential clinical translation,we built a novel online platform,where we modularized and deployed our framework along with the validated models.It can realize real-time one-stop prediction for new cases.In summary,using only pathological WSIs,our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making.It had the potential to be clinically implemented and promote the personalized management of TNBC.

Analysis of the Factors Contributing to the Nationwide Spread of Zibo Barbecue

Zibo is a city with a long history and rich culture.Its unique geographical location and cultural environment have created favorable conditions for its development.In 2023,the barbecue culture in Zibo became popular nationwide.The reason for this situation is not only due to the special geographical location and traditional culture of Zibo but also due to many human factors that contributed to this prosperity,allowing Zibo’s economy to take off in 2023.This article will explore the reasons why Zibo barbecue is so popular from several aspects,such as the unique barbecue culture in Zibo,the cooperation and promotion between the government and enterprises,the kindness and hospitality with Shandong people,and the promotion by the media and the internet.