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Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.

The racialized gendered body in consumer culture invokes the construction of identities and representation of beauty through embodiment practices. As companies hone in on consumer racial marker distinctions to enhance uniqueness as otherness for profit, an analysis of hair products marketed for Black hair textures provides an opportunity to analyze how products use these at the intersection of race, gender, and beauty. This study uses constructivist grounded theory to analyze the product names and long descriptions for 124 shampoos marketed for Black hair textures on the websites of two major retailers to answer the following questions: What message does the discourse of shampoo product descriptions marketed to Black hair textures communicate about beauty? What message does the discourse of shampoo product descriptions marketed to Black hair textures communicate about Blackness? How does this discourse define the embodiment of Black beauty through hair? The results reveal that the racialized gendered body in consumer culture invokes the construction of identities and representations of beauty through embodiment practices. Through advertisements and product descriptions, beauty companies create ideal images of the lived experiences achieved through consuming beauty products. By constructing visual interpretations of racial signifiers through text, marketing strategies encourage the consumption of otherness, creating a racialized space for Blackness consumption. Combining the five senses with the descriptions of shampoo products marketed for Black hair textures helps us see how beauty embodiment practices reinforce racialized and gendered practices to subjugate the Black body.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

This qualitative study investigated the relationship between beauty standards and identity in the United States from the perspective of 20 self-identified millennial Black women. During semi-structured virtual interviews, participants defined Black, American, and millennial beauty standards distinctly. American beauty was associated with Eurocentrism and mainstream media representation. Interpretations of a millennial beauty standard were aligned with perceptions of the generation as tolerant and politically conscious. Black American beauty standards embraced the range of hair textures and skin tones present in the African diaspora. While participants were cognizant of the different beauty ideals present, their interaction with beauty standards was ambivalent. Interviewees found beauty knowledge accessible through social media. However, they remain confined to a restrictive beauty standard due to workplace expectations around professionalism. Participants negotiated where and when to express their intracultural beauty ideals but participated in the beauty industry through processes of learning how to care for their hair in its natural state. Even though they have autonomy and flexibility in expressing their cultural styles, personal and professional repercussions are still plausible. Future studies can expand on these findings by exploring perceptions of American beauty standards from a different generation, region, or identity.

This study (1) assessed restaurant consumers' perceived importance of better food quality versus larger portion sizes, (2) classified restaurant consumers into different segments based on their perceived importance of seven food quality attributes (taste, texture, aroma, appearance and the use of natural, local and organic ingredients) relative to portion size and (3) compared the identified market segments.

An online survey assessed 613 USA adult participants' perceived importance of seven food quality attributes relative to portion size. A K-means cluster analysis classified participants into different segments based on their perceptions.

Five restaurant consumer segments were identified: sensory-oriented consumers, taste-oriented organic food consumers, local and natural food consumers, quantity-oriented consumers and quality-oriented consumers. In general, quality-oriented consumers were the least likely to visit fast-food restaurants, had the highest average per-meal spending and were the least likely to eat out alone. Local and natural food consumers and taste-oriented organic food consumers had higher total restaurant spending than other groups. Quantity-oriented and sensory-oriented consumers visited fast-food restaurants more often and had lower total restaurant spending and per-meal spending than other groups. Age, sex and income were significantly associated with the segmentation outcomes.

Findings suggest promising opportunities for restaurants to enhance consumer perceptions of value by prioritizing the factors that hold the greatest significance to their target customers.

This study is the first attempt to segment restaurant consumers based on their perceived importance of food quality attributes relative to portion size, effectively identifying five distinct consumer segments.

This paper seeks to understand how a new elite, known as the cork aristocracy, emerged in the Bordeaux wine field, France, between 1850 and 1929 as wine merchants replaced aristocrats. Classic class and status perspectives, and their distinctive social closure dynamics, are mobilized to illuminate the individual and organizational transformations that affected elite wineries grouped in an emerging classification of the Bordeaux best wines. We build on a wealth of archives and historical ethnography techniques to surface complex status and organizational dynamics that reveal how financiers and industrialists intermediated this transition and how organizations are deeply interwoven into social change.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

This study emphasizes the growing significance of destination attributes in shaping the experiences and behaviours of gastronomy and culinary tourists. Understanding both core and complementary attributes of a gastronomy tourism destination is crucial for its sustainable development. However, there is a lack of a reliable measurement for tourists’ perceived attributes of gastronomy tourism destinations (PAGTD). Therefore, the purpose of this study is to develop a scale to measure tourists’ PAGTD.

The study systematically followed the scale development and validation process, using three samples (Sample 1, n = 200; Sample 2, n = 204; and Sample 3, n = 204) to establish the psychometric properties of the proposed PAGTD scale.

The validated scale comprises five dimensions, namely, food culture, food sensations, food cooking, service environment, and food price & quality, encompassing a total of 22 measurement items.

This research significantly contributes to the gastronomy tourism literature by elucidating the dimensionality of PAGTD and validating the PAGTD scale.

目的地属性对美食游客的体验和行为的影响日渐凸显。理解美食旅游目的地的核心属性和补充属性对目的地可持续发展至关重要。然而, 目前还缺乏一份测量游客感知的美食旅游目的地属性（PAGTD）的量表。因此, 本文旨在开发并验证游客感知的美食旅游目的地属性量表（PAGTD量表）。

本文严格遵循量表开发与验证的程序, 利用三份样本（样本1, n=200; 样本2, n=204; 样本3, n=204）建立了PAGTD量表的信度和效度。

经过验证, PAGTD量表包括5个维度：美食文化、美食感官因素、美食烹饪、服务环境、美食价格与质量, 共计22个测项。

本文不仅阐明了游客感知的美食旅游目的地属性的多维结构, 还开发并验证了一份具有良好信效度的PAGTD量表。

Este estudio hace hincapié en la creciente importancia de los atributos de los destinos a la hora de configurar las experiencias y los comportamientos de los turistas gastronómicos y culinarios. Comprender tanto los atributos principales como los complementarios de un destino turístico gastronómico es crucial para su desarrollo sostenible. Sin embargo, se carece de una medición fiable de los atributos percibidos por los turistas de los destinos de turismo gastronómico (PAGTD). Por lo tanto, el objetivo de este estudio es desarrollar una escala para medir los PAGTD de los turistas.

El estudio siguió sistemáticamente el proceso de desarrollo y validación de la escala, utilizando tres muestras (Muestra 1, n=200; Muestra 2, n=204; y Muestra 3, n=204) para establecer las propiedades psicométricas de la escala PAGTD propuesta.

La escala validada comprende cinco dimensiones: saber, cultura alimentaria, sensaciones alimentarias, cocina de los alimentos, entorno del servicio y precio y calidad de los alimentos, que abarcan un total de 22 ítems de medición.

Esta investigación contribuye significativamente a la literatura sobre turismo gastronómico al dilucidar la dimensionalidad del PAGTD y validar la escala PAGTD.