Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

Perceptions of older consumers regarding food packaging as a prerequisite for its improvement: A case study of Polish market

In many societies, the scale of demographic change associated with increased longevity has the potential to influence the market for consumer goods. As a result, continuous improvement in packaging design that takes into account the needs of older people should become an important challenge for both packaging producers and product manufacturers. Due to the fact that there is limited research concerning the design of food packaging for older people, the aim of the study was to identify the expectations of this group of consumers in this respect. Milk packaging was selected as a model subject of study. Factors influencing older consumers and their buying patterns when purchasing milk have been thoroughly examined. The research was conducted among people over the age of 60 with the use of three methods: a questionnaire (600 subjects), an in‐depth interview (60 subjects) and eye tracking (30 subjects). The results of the study show that older consumers have specific preferences regarding packaging type and design. When considering milk packaging, for example, they prefer glass bottles, simple graphics and font and subdued colours. They most frequently look for information concerning the product's expiration date, its producer's name and its composition. Eye tracking revealed that the older the consumers are, the more problems with finding expiration date they have. The time required to find such information, however, strongly depends on where exactly the expiration date is printed on the packaging. The preferred place in this case is the top of the bottle. Older consumers identify product's brand with the name of the producer. Moreover, the absence of the term ‘ingredients’: on the milk packaging oftentimes causes confusion among older people, who are concerned about potential food additive content.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

摩托车单向器自动装配机气动系统设计

针对当前摩托车单向器组件主要采用手工装配、装配效率低、随机性大、无法对装配过程实现有效管理控制的情况,为了易于对零件装配过程进行实时监测和提高生产效率,开发了一种智能摩托车单向器自动装配机。介绍了摩托车单向器自动装配机的上料系统、装配系统、检测系统和落料系统的机械结构,重点阐述了设备的气动系统设计,并进行了实验研究。结果表明,该设备可实现组成单向器的5种零件自动上料和装配,同时降低了操作人员的劳动强度,生产效率提高约50%。其气动系统噪音小、工作可靠,可应用于摩托车单向器的大批量生产。研究结果可为其他相似异形零件自动装配机的开发提供参考。     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

The Influences of Portion Size,Context and Package Characteristics on Snack Food Consumption: Evidence from a U.S. Rolling Cross-Sectional Survey

Using a rolling cross-sectional sample of over 40,000 U.S. snack food consumers during 2004–2010, we build a three-dimensional model to study how portion sizes, consumption context, and package characteristics jointly affect the amount of food consumed. As with prior research, we find that consumption does increase with larger portions. However, this effect diminishes significantly when accounting for the consumption context and packaging characteristics as consumption drivers. Most important is how the snack is presented to consumers at the point of consumption (type, size, and quantity of packages). We also find that snacking on impulse, alone, with a beverage, or while watching television increases the amount consumed, whereas socializing or snacking at home decrease consumption. We test our model over seven snack types (potato chips, crackers, cheese puffs, nuts and seeds, tortilla chips, pretzels, popcorn), and find that our results hold even for products with different cross-category granularity, palatability, and satiety.     

控制技术在钢管自动化生产中的应用

钢管自动化控制系统在钢管生产中的有效应用极大地提升了钢管的生产效率和生产质量,减少了人力资源的投入,降低了钢管的生产成本,对于钢铁行业的发展具有至关重要的作用。论文主要针对控制技术在钢管自动化生产中的实际应用进行探究,指出控制技术在钢管自动化生产中应用的效果和作用,希望为钢铁行业的运行提供一定的参考。