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The purpose of this paper is provide a broad view for the standardization efforts of color quality evaluation of color 3D printing techniques. Further, this review paper demonstrates the processes and color properties of most color 3D printing techniques with specific devices and applications to extend the range of possible memberships of standardization group.

Six color 3D printing techniques including plastic-based, paper-based, powder-based, organism-based, food-based and metal-based color 3D printing have been introduced and illustrated with colorization principles and forming features in detail. Moreover, for printed 3D color objects, literature about color measurement, color specification and color reproduction are described and analyzed, respectively.

Four color 3D printing techniques including plastic-based, paper-based, powder-based and food-based color 3D printing show great affinity toward standardization of color quality evaluation, while their colorization principles indicate that it is difficult with a single standard frame. It is possible to develop a completed color quality evaluation standard for color 3D printing based on approaches in color 2D printing when color measurement method and devices are standardized together.

The paper provides an important guide focusing on the efforts to standardize the colorization processes and color quality evaluation of the color 3D printing techniques.

To examine whether upstream firms in the food chain are knowledgeable about end‐users or consumers and to identify factors that might influence their acquisition of such knowledge.

A cross‐disciplinary approach is used to gain theoretical insights into the research questions. Consumer knowledge and influencing factors are revealed through an empirical study of producer and exporter firms in the Norwegian salmon farming industry. The study focuses on managerial knowledge of how consumers perceive the importance of the colour and texture of flesh and the fat content of salmon.

A relatively large proportion of the managers are not knowledgeable about consumers' quality perceptions. Closeness to consumers in the form of sales to the respective markets, or downstream location in the food chain, do not appear to improve knowledge. A differentiation strategy is not associated with more accurate consumer knowledge.

The study focuses on knowledge of a limited aspect of consumers' quality evaluation only and important chain actors such as retailers were not included in the study. Upstream firms pursuing a quality‐based differentiation strategy must take into account the fact that not all consumers are willing to pay a price premium for high quality, and that those who do might use surrogate indicators to infer quality.

The paper addresses the important but little researched area of distribution of end‐user knowledge in the food chain. It investigates reasons why and when upstream actors will benefit from end‐user knowledge.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.

The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.

Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this paper is to examine the impact of arousal fit between music (auditory cues) and plate color (visual cues) on food evaluations.

A 2 (background music: high vs low arousal) ×2 (plate color: high vs low arousal) between-subjects experimental design was employed to test the hypotheses. Participants were recruited for a taste test in a food sensory lab located in a university in the northeastern USA. The dependent variable, food evaluation, was analyzed using analysis of covariance (ANCOVA).

The findings of this study indicate that when the two stimuli had congruent arousal qualities, participants rated the liking and perceived quality of a food item higher than those in the incongruent arousal conditions. More specifically, when the background music was high in arousal quality, participants gave higher evaluations for a food item served on a red (vs blue) plate. Conversely, when a low arousal music piece was played, a blue (vs red) plate resulted in higher food evaluations.

This study contributes to the growing literature on cross-modal correspondence on consumers’ product evaluations. Extending previous research on the congruity effect to the food sensory literature, this study demonstrates the joint effects of background music and plate color on taste perceptions. The results suggest that restaurant operators should be strategic in their choice of physical stimuli.

The present study measured participants taste perceptions while they were exposed to visual and auditory cues. To the best of the authors’ knowledge, this is the first study that empirically demonstrates the impact of multiple environmental factors on taste evaluation.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Building upon humans’ trichromatic vision systems, dual-process theory and halo effects, this paper aims to examine the effects of red and green color brightness of food pictures on customers’ evaluations and purchase intention of restaurant food.

The proposed hypotheses were tested across three experimental designed studies on a total of 575 participants. Multilevel analysis, analysis of variance and multivariate analysis of variance were applied for data analysis.

This paper provides empirical evidence of the effects of red brightness and green brightness on customers’ affective and cognitive evaluation of food and purchase intention in Study 1. Study 2 validates the effects of red and green brightness on food evaluation with the presence of nutrition information. Study 3 further elaborates on the halo effects of color brightness on customers’ favorable intentions to patronize a restaurant and willingness to pay for a meal in a controlled lab experiment.

One main limitation is that this paper focuses on unveiling the role of color brightness and does not consider other picture properties, which opens an avenue for future research.

This paper includes implications for food promotion and management of customers’ experience via food pictures.

This paper is one of the first attempts to reveal the effects of red and green brightness of food pictures on customers’ food evaluation and food consumption behavioral intentions.

The aim of this paper, using the intuitionistic fuzzy (IF)–set based Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method, is to create multicriteria decision-making (MCDM) mechanism that evaluates the papers according to the offset print quality, separately for each of the CMYK colors. Thanks to the mechanism, the most suitable and the most unsuitable paper for printing is determined.

With the IF PROMETHEE method, respectively, deviations are calculated by linear criteria type; the overall IF preference relation matrix is obtained. Finally, the positive and negative outranking flows are calculated; by obtaining net outranking flows, all papers are ranked from best to worst for printability.

Based on the results of the MCDM algorithm; the best offset printing quality is Matte Coated 115 gr. for cyan, magenta and black and Glossy Coated 115 gr. for yellow. The worst offset printing quality for all papers is 70 gr. III. low grade paper. According to the findings, when all colors are examined, the best paper for offset printing quality is coated papers; the worst paper is III. low grade papers.

The creation of mechanism that evaluates all criteria together leads to the most accurate result. Cases of hesitation are also addressed using an IF-based algorithm; all criteria were assigned individual importance levels, and a single result was obtained by activating all of the criteria simultaneously. Therefore reasons, this is paper that will bring innovation and shed light on studies in this field.