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Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

In this study, the mediating effects of perceived behavior control and attitudes toward being an entrepreneur were investigated in the relationship between family business experience and entrepreneurial intentions of university students. First, the variables of perceived behavioral control and attitude toward being an entrepreneur were defined as the mediators used in explaining the entrepreneurial intention. Then, the process of investigating the mediation effects with the structural equation modeling (SEM) approach in two cases with one and two mediating latent variables is explained.

In this study, the process of investigating the mediation effects in two situations where there is one and two mediating latent variables by SEM is presented. In addition, the decomposition of the effects for the model consisting of two mediating latent variables is given in detail with matrix notation.

It has been determined that the latent variable of perceived behavior control functions as a “full mediator” in the relationship between the family ownership story and the entrepreneurial intention. The study also revealed that students whose family's business ownership score is high and who are self-confident in the process of becoming an entrepreneur have stronger entrepreneurial intentions.

In the research, the distinction between the model used in determining the entrepreneurial intentions of university students and their mediation and indirect effects is explained in detail with matrix notations with the SEM approach.

The purpose of this project was to determine if there was a difference in the longevity of the latent fingerprints of children vs adults. It is generally believed that a subject’s age does not affect the evaporation rates of fingerprints. However, based upon a recent criminal investigation of child abduction, it was hypothesized that children’s latent fingerprints do not last as long as those of adults. Participation in this study was voluntary and informed consent obtained. A total of 97 subjects pressed their fingers on glass slides and their latent fingerprints were lifted one, three, five and seven days later. A comparison was then made between the longevity of the prints of children vs adults. Almost all of the adult prints were still present on day seven. Of the children’s prints, 20 percent were unclear on day three; 54 percent were unclear on day five; and 76 percent were unclear on day seven. This has implications for law enforcement and forensic science in that time may become a critical variable in criminal investigations requiring the lifting of latent fingerprints of children.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

The purpose of this paper is to incorporate explicitly consumer heterogeneity into market response models estimated with store‐level scanner‐data.

Latent structures in market response to a product category using aggregated scanner data registered by a supermarket are identified. Specifically, latent consumer segments with diverse preferences towards brands and different responses to marketing stimuli from data consisting of daily marketing actions (i.e. price, promotions, advertising, etc.) and sales of competing brands are identified.

The existence of different latent segments with diverse preferences and response patterns to marketing stimuli were detected. More specifically, the fit of the statistical analysis for the different model possibilities made it possible to identify four market segments. It was also found that the intrinsic brand attractiveness as a measure of consumer brand preference is different between segments. Finally, the price sensitivity is also different between segments.

The time cost necessary to obtain the parameter estimates is too high, which is usual in the models estimated with iterative EM algorithms.

This work deepens one's knowledge of the identification and selection of latent market structures, specifically latent segments with different purchase patterns and behaviours. The possibility of developing the analysis with aggregated data at the store level increases the potential utility for academics and marketing managers.

Although most applications use weekly data, this proposal models daily fluctuations in sales – as a result, making it possible to obtain consumer segments based on daily changes.

Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation.

The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments.

The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market.

The proposed statistical models are new in the field of financial markets.

Flow control has a great potential to contribute to a sustainable society through mitigation of environmental burden. However, the high dimensional and nonlinear nature of fluid flows poses challenges in designing efficient control laws using the control theory. This paper aims to propose a hybrid method (i.e. machine learning and control theory) for feedback control of fluid flows, by which the flow is mapped to the latent space in such a way that the linear control theory can be applied therein.

The authors propose a partially nonlinear linear system extraction autoencoder (pn-LEAE), which consists of convolutional neural networks-based autoencoder (CNN-AE) and a custom layer to extract low-dimensional latent dynamics from fluid velocity field data. This pn-LEAE is designed to extract a linear dynamical system so that the modern control theory can easily be applied, while a nonlinear compression is done with the autoencoder (AE) part so that the latent dynamics conform to that linear system. The key technique is to train this pn-LEAE with the ground truths at two consecutive time instants, whereby the AE part retains its capability as the AE, and the weights in the linear dynamical system are trained simultaneously.

The authors demonstrate the effectiveness of the linear system extracted by the pn-LEAE, as well as the designed control law’s effectiveness for a flow around a circular cylinder at the Reynolds number of Re_D = 100. When the control law derived in the latent space was applied to the direct numerical simulation, the lift fluctuations were suppressed over 50%.

To the best of the authors’ knowledge, this is the first attempt using CNN-AE for linearization of fluid flows involving transient development to design a feedback control law.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain such performance results.

To do so, a restricted latent structure finite mixture model is devised that can quantify the contribution of these three potential sources of heterogeneity in the formulation of latent competitive groups within an industry. The paper illustrate this modeling framework with respect to COMPUSTAT strategy and performance data collected for public banks in the USA.

The paper shows how traditional conceptualizations via strategic or performance groups are inadequate to fully represent intra‐industry heterogeneity.

This research paper proposes a new class of restricted finite mixture‐based models, which fit a variety of alternative forms/models of heterogeneity. Information heuristics are developed to indicate “best model.”

The purpose of this paper is to integrate a thermophysiological human body model into a CFD simulation to predict the dry and latent body heat loss, the clothing, skin and core temperature, skin wettedness and periphery blood flow distribution. The integration of the model allows to generate more realistic boundary conditions for the CFD simulation and allows to predict the room distribution of temperature and humidity originating from the occupants.

A two-dimensional thermophysiological body model is integrated into a CFD simulation to predict the interaction between the human body and room environment. Parameters varied were clothing insulation and metabolic activity and supply air temperature. The body dry and latent heat loss, skin wettedness, skin and core temperatures were predicted together with the room air temperature and humidity.

Clothing and metabolic activity were found to have different level of impact on the dry and latent heat loss. Heat loss was more strongly affected by changes in the metabolic rate than in the clothing insulation. Latent heat loss was found to exhibit much larger variations compared to dry heat loss due to the high latent heat potential of water.

Unlike similar studies featuring naked human body, clothing characteristics like sensible resistance and vapor permeability were accommodated into the present study. A method to ensure numerical stability of the integrated simulation was developed and implemented to produce robust and reliable simulation performance.