Search

Line Extensions are among the most common form of product launch in packaged goods markets. As part of this process, brand managers must decide the visual design of the new variant's packaging. To inform this decision making, this research aims to empirically quantify the efficacy of using colors versus images as signals of product variety on pack. We compare the use of color on 576 packs with perceptions of 1,853 category buyers across three categories in the USA. We find that for 84% of variant types, marketers use common colors to signal variety on pack, while consumers perceive that only 56% of variant types are represented by a particular color. Of greater concern, the colors used in practice and those expected by consumers align in only 16% of cases. By comparison, images are linked to variant types to a significantly greater extent (39% of cases). This suggests images are a stronger and more explicit signal of product variety than color. There are multiple implications arising from this study. It expands scholarly research on the use of colors in product extensions and, at the same time, provides a series of valuable benchmarks for industry practice in the portfolio management domain. [ABSTRACT FROM AUTHOR]

Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media. Pharmaceutical drug dosage forms are traditionally determined through extensive physical experimentation. Here, the authors present a generative AI method that creates digital drug products from images, matching and improving critical quality attributes such as particle size and drug loading. [ABSTRACT FROM AUTHOR]

Various fluorescence imaging agents are currently under clinical studies. Despite significant benefits, phototoxicity is a barrier to the clinical translation of fluorophores. Current regulatory guidelines on medication‐based phototoxicity focus on skin effects during sun exposure. However, with systemic and local administration of fluorophores and targeted illumination, there is now possibility of photochemical damage to deeper tissues during intraoperative imaging procedures. Hence, independent knowledge regarding phototoxicity is required to facilitate the development of fluorescence imaging products. Previously, we studied a cell‐free assay for initial screening of reactive molecular species generation from fluorophores. The current work addresses a safety test method based on cell viability as an adjunct and a comparator with the cell‐free assay. Our goal is to modify and implement an approach based on the in vitro 3T3 neutral red uptake assay of the Organization for Economic Co‐Operation and Development Test Guideline 432 (OECD TG432) to evaluate the photocytotoxicity of clinically relevant fluorophores. These included indocyanine green (ICG), proflavine, methylene blue (MB), and IRDye800, as well as control photosensitizers, benzoporphyrin derivative (BPD) and rose bengal (RB). We performed measurements at agent concentrations and illumination parameters used for clinic imaging. Our results aligned with prior studies, indicating photocytotoxicity in RB and BPD and an absence of reactivity for ICG and IRDye800. DNA interactive agents, proflavine and MB, exhibited drug/light dose–response curves like photosensitizers. This study provides evidence and insights into practices useful for testing the photochemical safety of fluorescence imaging products. [ABSTRACT FROM AUTHOR]

NeRF can render photorealistic 3D scenes. It is widely used in virtual reality, autonomous driving, game development and other fields, and quickly becomes one of the most popular technologies in the field of 3D reconstruction. NeRF generates a realistic 3D scene by emitting light from the camera's spatial coordinates and viewpoint, passing through the scene and calculating the view seen from the viewpoint. However, when the brightness of the original input image is low, it is difficult to recover the scene. Inspired by the ambient illumination in the Phong model of computer graphics, it is assumed that the final rendered image is the product of scene color and ambient illumination. In this paper, we employ Multi-Layer Perceptron (MLP) network to train the ambient illumination tensor I , which is multiplied by the color predicted by NeRF to render images with normal illumination. Furthermore, we use tiny-cuda-nn as a backbone network to simplify the proposed network structure and greatly improve the training speed. Additionally, a new loss function is introduced to achieve a better image quality under low illumination conditions. The experimental results demonstrate the efficiency of the proposed method in enhancing low-light scene images compared with other state-of-the-art methods, with an overall average of PSNR: 20.53 , SSIM: 0.785, and LPIPS: 0.258 on the LOM dataset. [ABSTRACT FROM AUTHOR]

Product design and attractiveness are pivotal factors that determine people's positive reactions when viewing a product and may eventually affect their purchasing choices. Comprehending how people assess product design is crucial. Various studies have explored the link between product attractiveness and consumer behavior, but these were predominantly behavioral studies that offered limited insight into the neural processes underlying perceptions of product attractiveness. Gaining a deeper understanding of these neural mechanisms is valuable, as it enables the formulation of more objective design guidelines based on brain activity, enhancing product appeal and, ultimately, spurring consumer purchases. In our study, we sought to (1) elucidate the neural network engaged when individuals evaluate highly attractive product images, (2) delineate the neural network activated during the evaluation of less attractive product images, and (3) contrast the differences in neural networks between evaluations of highly and less attractive images. We utilized fMRI to investigate the neural activation patterns elicited by viewing product images of varying attractiveness levels. The results indicated distinct neural activations in response to the two types of attractive images. Highly attractive product images elicited activity in the anterior cingulate cortex (ACC) and the occipital pole, whereas less attractive product images stimulated the insula and the inferior frontal gyrus (IFG). The findings of this project provide some of the first insights of its kind and valuable insights for future product design, suggesting that incorporating more positive and rewarding elements could enhance product appeal. This research elucidates the neural correlates of people's responses to product attractiveness, revealing that highly attractive designs activate reward-related brain regions, while less attractive designs engage areas associated with emotional processing. These findings offer a neuroscientific basis for further studies on developing design strategies that align with consumers' innate preferences, potentially transforming product design and marketing practices. By leveraging this knowledge, designers can craft products that not only meet functional needs but also resonate more deeply on an esthetic level, thereby enhancing consumer engagement and purchase likelihood. [ABSTRACT FROM AUTHOR]

Advertising plays an important role in influencing consumer perceptions and attitudes towards products, especially for well-known brands like Wardah. Advertising strategies help build a positive image and increase consumer interest. Research shows that consumers are more likely to buy products from well-known brands than lesser-known brands. Wardah, which is widely known to Germasa GPIB Petra, allows consumers to recognize and create their own image. Wardah's advantages include the halal guarantee from MUI and its availability in various online and offline stores. Quantitative research methods This study examines the influence of advertising on consumer purchase decisions based on syringe theory. The validity and reliability of the questionnaire were adequate (Cronbach's alpha 0.749). The analysis shows that the impact of the ad is very small (R² 0.030%). Although the T-test showed a significant influence (tcal 4.384 Ttable 2.0106), overall the influence of advertising was not significant (0.02958%). Internal and interpersonal factors are more dominant, so the syringe theory is not fully proven. Research shows that Wardah product advertising has a significant effect on purchase decisions (reliability 0.766 and 0.749). The hypothesis is accepted, but the R Square value of 0.030% shows very little influence. Consumers of GPIB petra mothers rely more on personal experience and other factors than advertising, so advertising does not play a significant role in purchasing decisions. [ABSTRACT FROM AUTHOR]

With the rapid development of additive manufacturing (AM) technology, quality inspection has become one of the most crucial research topics in additive manufacturing. Although numerous image-based deep learning methods have been successfully developed to monitor and inspect AM product quality effectively, many require substantial labels in order to achieve satisfactory training, which is often impractical in real-life AM processes. In this article, a novel image feature-based self-supervised learning (IFSSL) model is proposed for effective quality inspection in AM. Through a feature-based image fusion approach based on defect-relevant feature extraction, the IFSSL model is able to guide machine vision to focus on highlighted defect-relevant regions in the AM product image. In addition, the defect-relevant features are used to generate pseudo-labels for self-supervised learning. With self-supervision, the IFSSL model leverages the advantages of supervised learning and unsupervised learning by requiring no sample label while retaining defect-relevant information. The effectiveness of the proposed IFSSL method is demonstrated through a real case study of fused deposition modeling product image dataset. Results show that the IFSSL model can guide machine vision to pay more attention to potential defective regions, enabling it to detect and locate faults effectively and automatically for machine vision guided quality inspection. [ABSTRACT FROM AUTHOR]

Introduction: The coverage of a makeup foundation is a perceived attribute which is not captured by opacity or any other single optical property. As previous instrumental measurements do not allow us to consistently compare one product to another, we have begun exploring new parameters and analysis methods made available by hyperspectral imaging. Presumably, the coverage of makeup comes from the change in color, homogeneity, and evenness over the face after application, and the ability of the product to hide spots and other blemishes. Methods: As a starting point to unravelling this complex topic, we define a homogeneity factor αHF which measures the change in the homogeneity of the spectra using the distribution of spectral angles in the face. We likewise define a spectral shift factor βSF which indicates the degree of spectral change after product application. To test these new parameters and the overall analysis method, we applied them to the HSI validation dataset which contains data for three makeup foundation products of different coverage levels applied to 9 models. Results: We find that αHF correlates with the sensory ranking of coverage. Similarly, the parameter βSF correlates with the visible color change induced by the product, and we can map the three products into distinct categories based on their effect on αHF and βSF. Discussion: Nevertheless, the homogeneity factor αHF does not fully describe coverage, and in the variability in the product effect from model to model we find evidence that we must also account for the relative color difference between the model's skin tone and the product shade among other factors. [ABSTRACT FROM AUTHOR]

Recommender systems have been a kind of powerful tool to improve e-commerce benefits. Existing recommender systems mostly employ explicit features of products, such as description and attributes. However, visual characteristics contain fruitful implicit and intuitive information, and are always ignored by existing works. To deal with this issue, this paper proposes a novel intelligent recommender system via visual Transformer (ViT) model and convolutional neural network (CNN) structure. First, the ViT part is utilized to extract visual feature representation. By learning the visual similarity among products, it is expected to obtain a better scene understanding. Then, an improved CNN part is utilized to extract hidden association information from historical behaviors of users. It is expected to better perceive user preference and purchasing characteristics. The combination of two parts constructs the proposed recommender system. Finally, we make performance evaluation for the proposal on real-world e-commerce dataset. The results indicate that the proposal exhibits high recommendation accuracy and efficiency. Compared with other typical algorithms, our proposal can better understand product images and user behaviors, providing more personalized recommendation results. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural networks, which can be costly and impractical. One-shot learning techniques mitigate this issue by enabling classification from a single prototype image per product class, thus reducing data annotation efforts. We introduce the Variational Prototyping Encoder (VPE), a novel deep neural network for one-shot classification. Utilizing a support set of prototype SKU images, VPE learns to classify query images by capturing image similarity and prototypical concepts. Unlike metric learning-based approaches, VPE pre-learns image translation from real-world object images to prototype images as a meta-task, facilitating efficient one-shot classification with minimal supervision. Our research demonstrates that VPE effectively reduces the need for extensive datasets by utilizing a single image per class while accurately classifying query images into their respective categories, thus providing a practical solution for product classification tasks. [ABSTRACT FROM AUTHOR]

Long-strip imaging is an important way of improving the coverage and acquisition efficiency of remote sensing satellite data. During the agile maneuver imaging process of the satellite, the LuoJia3-01 satellite can obtain a sequence of array long-strip images with a certain degree of overlap. Limited by the relative accuracy of satellite attitude, there will be relative misalignment between the sequence frame images, requiring high-precision geometric processing to meet the requirements of large-area remote sensing applications. Therefore, this study proposes a new method for the geometric correction of long-strip images without ground control points (GCPs) through GPU acceleration. Firstly, through the relative orientation of sequence images, the relative geometric errors between the images are corrected frame-by-frame. Then, block perspective transformation and image point densified filling (IPDF) direct mapping processing are carried out, mapping the sequence images frame-by-frame onto the stitched image. In this way, the geometric correction and image stitching of the sequence frame images are completed simultaneously. Finally, computationally intensive steps, such as point matching, coordinate transformation, and grayscale interpolation, are processed in parallel using GPU to further enhance the program's execution efficiency. The experimental results show that the method proposed in this study achieves a stitching accuracy of less than 0.3 pixels for the geometrically corrected long-strip images, an internal geometric accuracy of less than 1.5 pixels, and an average processing time of less than 1.5 s per frame, meeting the requirements for high-precision near-real-time processing applications. [ABSTRACT FROM AUTHOR]

Lightning is a rapidly evolving phenomenon, exhibiting both mesoscale and microscale characteristics. Its prediction significantly relies on timely and accurate data observation. With the implementation of new generation weather radar systems and lightning detection networks, radar reflectivity image products, and lightning observation data are becoming increasingly abundant. Research focus has shifted towards lightning nowcasting (prediction of imminent events), utilizing deep learning (DL) methods to extract lightning features from very large data sets. In this paper, we propose a novel spatio-temporal fusion deep learning lightning nowcasting network (STF-LightNet) for lightning nowcasting. The network is based on a 3-dimensional U-Net architecture with encoder-decoder blocks and adopts a structure of multiple branches as well as the main path for the encoder block. To address the challenges of feature extraction and fusion of multi-source data, multiple branches are used to extract different data features independently, and the main path fuses these features. Additionally, a spatial attention (SA) module is added to each branch and the main path to automatically identify lightning areas and enhance their features. The main path fusion is conducted in two steps: the first step fuses features from the branches, and the second fuses features from the previous and current levels of the main path using two different methodsthe weighted summation fusion method and the attention gate fusion method. To overcome the sparsity of lightning observations, we employ an inverse frequency weighted cross-entropy loss function. Finally, STF-LightNet is trained using observations from the previous half hour to predict lightning in the next hour. The outcomes illustrate that the fusion of both the multi-branch and main path structures enhances the network's ability to effectively integrate features from diverse data sources. Attention mechanisms and fusion modules allow the network to capture more detailed features in the images. [ABSTRACT FROM AUTHOR]

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females. [ABSTRACT FROM AUTHOR]

In order to solve the problem of recognizing multiple product images, the author proposes the research of image recognition technology based on deep learning in automated control systems. Firstly, the FasterRCNN method is improved by proposing a non class specific FasterRCNN, which can be used for pre annotation of product images by training only on publicly available datasets. Due to the use of position correction networks, the pre annotation effect is more accurate than that of candidate region networks. Then, combining Grabcut with non class specific FasterRCNN, a sample enhancement method was proposed to synthesize a large number of training images containing multiple products and use them for model training. In addition, based on non class specific FasterRCNN, a re identification layer was proposed to improve detection accuracy. In the end, the recognition and positioning of multiple products achieved a recall rate of 93.8% and an accuracy of 96.3%. [ABSTRACT FROM AUTHOR]

Image-based virtual try-on aims to fit a target garment to a reference person. Most existing methods are limited to solving the Garment-To-Person (G2P) try-on task that transfers a garment from a clean product image to the reference person and do not consider the Person-To-Person (P2P) try-on task that transfers a garment from a clothed person image to the reference person, which limits the practical applicability. The P2P try-on task is more challenging due to spatial discrepancies caused by different poses, body shapes, and views between the reference person and the target person. To address this issue, we propose a novel Keypoints-Driven Flow Based Virtual Try-On Network (KF-VTON) for handling both the G2P and P2P try-on tasks. Our KF-VTON has two key innovations: (1) We propose a new keypoints-driven flow based deformation model to warp the garment. This model establishes spatial correspondences between the target garment and reference person by combining the robustness of Thin-plate Spline (TPS) based deformation and the flexibility of appearance flow based deformation. (2) We investigate a powerful Context-aware Spatially Adaptive Normalization (CSAN) generative module to synthesize the final try-on image. Particularly, CSAN integrates rich contextual information with semantic parsing guidance to properly infer unobserved garment appearances. Extensive experiments demonstrate that our KF-VTON is capable of producing photo-realistic and high-fidelity try-on results for the G2P as well as P2P try-on tasks and surpasses previous state-of-the-art methods both quantitatively and qualitatively. Our code is available at https://github.com/OIUIU/KF-VTON. [ABSTRACT FROM AUTHOR]

Environmental damage caused by non-recyclable materials is an opportunity for business people to create environmentally friendly products. This study aims to explain the influence of green products on purchasing decisions, the influence of green products on brand image, the influence of brand image on purchase decisions, and the role of brand image in mediating the impact of green products on the purchase decision of PT. Karya Pak Oles Tokcer. The method of determining the sample in this study is non-probability sampling using the purposive sampling technique so that the sample involved in the study is 100 people. The analysis techniques used are path analysis and mediation role testing. The results show that green products have a positive and significant effect on purchasing decisions; Green products have a positive and significant impact on the brand image; the brand image has a positive and significant effect on product purchase decisions; the Brand image can mediate part of the influence of green products on purchasing decisions. The practical implications of this research can make a valuable contribution to the management of PT. Mr. Oles Tokcer in improving consumer purchasing decisions for PT. Karya Pak Oles Tokcer. [ABSTRACT FROM AUTHOR]

New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi‐modal information related to a brand‐new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network‐based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non‐autoregressive manner, avoiding the compounding effect of large first‐step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5,577 real, new products sold between 2016 and 2019 from Nunalie, an Italian fast‐fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state‐of‐the‐art alternatives and several baselines, showing that our neural network‐based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available online (at https://github.com/HumaticsLAB/GTM-Transformer). [ABSTRACT FROM AUTHOR]

This paper aims to derive the images of the product of the generalized q-analogue of Mittag-Leffler functions and the basic binomial function, under the fractional q-integral operators of Kober and Weyl type. The derived findings are of a general nature and include, as specific examples, the Weyl q-integral operator, the q-derivative operators, and the Riemann–Liouville q-integral operator. [ABSTRACT FROM AUTHOR]

Delineating the distribution of tidal flats in the Yangtze River Delta region is of considerable significance for the ecological environment research and sustainable development of the region. However, the tidal flat ecology is greatly threatened by rapid economic development and human disturbance. Information on the tidal flat ecology is necessary for tidal flat extraction studies in Jiangsu Province and Shanghai City. Therefore, using imagery acquired by Sentinel-2 sensors along the coasts of Jiangsu Province and Shanghai City and the Google Earth Engine Cloud Platform, we established a multilayer automatic decision tree classification model based on the maximum spectral index composite algorithm and Otsu algorithm to extract tidal flats. A total of 1011 scene images, acquired from January 1 to December 31, 2021, were collected to generate tidal flat maps of Jiangsu Province and Shanghai City. The overall accuracy obtained by establishing the confusion matrix was 96.1%. The tidal flat areas of Jiangsu Province and Shanghai City in 2021 were 222347 and 41097 hectares, respectively, among which the tidal flat area of Jiangsu Province was 5.4 times that of Shanghai City. Moreover, we compared the results with real images and existing products, which further explained the rationality of the tidal flat results of this study. The 10-m-resolution tidal flat map of Jiangsu Province and Shanghai City obtained in this study can provide support for the sustainable policy management of tidal flats in the Yangtze River Delta region. [ABSTRACT FROM AUTHOR]

In existing image retrieval algorithms, negative samples often appear at the forefront of retrieval results. To this end, in this paper, we propose a feature fusion-based re-ranking method for home textile image retrieval, which utilizes high-level semantic similarity and low-level texture similarity information of an image and strengthens the feature expression via late fusion. Compared with single-feature re-ranking, the proposed method combines the ranking diversity of multiple features to improve the retrieval accuracy. In our re-ranking process, Markov random walk is used to update the similarity metrics, and we propose local constraint diffusion based on contextual similarity. Finally, the fusion–diffusion algorithm is used to optimize the sorted list via combining multiple similarity metrics. We set up a large-scale home textile image dataset, which contains 89 k home textile product images from 12 k categories, and evaluate the image retrieval performance of the proposed model with the Recall@k and mAP@K metrics. The experimental results show that the proposed re-ranking method can effectively improve the retrieval results and enhance the performance of home textile image retrieval. [ABSTRACT FROM AUTHOR]

When more than one product influences the image of a tourist destination, there may be confusion between image and product competition, or it may generate an integrative impact rather than competition. In such destinations, products might be perceived as touristic, some as cultural with symbolic implications, or simply as commercial or industrial products. The study focuses on two major products in Isparta province, namely rose and lavender. It seeks to assess the relationship among rose and lavender via the lens of competition, destination image, and symbolic value. Throughout the evaluation, netnography and Multi-Criteria Decision-Making techniques are used. The opinions of locals and tourists are examined using MCDM, while TripAdvisor evaluations of Kuyucak Village are evaluated using netnography. According to the study's key finding, rose is emphasized over lavender in the destination image, with rose being classified as a cultural product and lavender as a touristic product. [ABSTRACT FROM AUTHOR]

To enhance the scientific rigor of design decisions and develop new rosewood furniture that aligns with user emotions, this study integrates the strengths of the Hesitant Fuzzy Analytic Hierarchy Process (HFAHP) and Hesitant Fuzzy Quality Function Deployment (HFQFD) within the framework of Kansei Engineering (KE). This method accurately translates Consumer Requirements (CRs) into Engineering Characteristics (ECs). First, the KJ Method was used to screen and categorize Kansei words, create product sample images, and deconstruct the form of rosewood furniture using morphological analysis. Second, after collecting valid questionnaires using a 7-point Likert scale, Factor Analysis (FA) was employed to extract three key Kansei factors. Third, HFAHP was utilized to calculate the weights of the Kansei words. Fourth, HFQFD was applied to construct a hesitant fuzzy correlation matrix between CRs and ECs, determining the priority of design elements for rosewood furniture. Finally, using a square table as an example in the design practice, the optimal Scheme No. 9, which highly meets consumer emotional needs and features harmonious form combinations, was selected. This study enhances the emotional value of rosewood furniture, optimizes the design decision-making process, and improves contemporary consumer satisfaction. [ABSTRACT FROM AUTHOR]

Peatlands have suffered significant degradation globally due to human impacts, which has increased the need to monitor the condition and changes in peatland ecosystems. With remote sensing, point-based in-situ observations can be upscaled to larger areas but there is a need to develop scalable monitoring methods. We predicted wet flark area extent, a key ecological indicator for patterned flark fens, in five sites in central Finland. Our primary aim was to test how the spatial and spectral resolution of different optical satellite image products (Landsat 8–9, Sentinel-2, PlanetScope) affect flark area coverage prediction. We also tested if there were seasonal or site-specific differences in prediction accuracy. Lastly, we upscaled the flark area coverage to entire mire areas. We used unmanned aerial vehicle (UAV)-derived flark area classification as a ground reference data to compare satellite sensors' prediction accuracies. We predicted flark area coverage using spectral bands and indices as predictor variables using random forest regression. All sensors provided accurate results with some differences between Landsat (pseudo-R2 32−84%, root-mean squared error (RMSE) 10 − 18%), Sentinel-2 (R2 61−92%, RMSE 6−14%), and PlanetScope (R2 46 − 92%, RMSE 6 − 17%). The shortwave infrared bands of Landsat and Sentinel-2 did not increase the prediction accuracy. There were notable site-specific variations in prediction accuracy despite all the sites having typical open aapa mire wet flark – dry string patterns. With single-site models, the prediction accuracies were similar for early and late summer data, but when transferring the models to the other sites, performance significantly decreased, especially with the models using the late-summer imagery. Finally, we successfully upscaled the flark area coverage across entire mire areas. Our results demonstrate that the combination of UAV and satellite imagery can be used successfully to monitor peatland conditions and changes in them. [ABSTRACT FROM AUTHOR]

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

The Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries. Plain Language Summary: We generated a global mosaic of Mars using Context Camera satellite imagery at 5.0 m/pixel, a substantial improvement in spatial resolution over the previous global mosaic at 100 m/pixel. This paper describes a new technique for merging overlapping images together in a way that preserves all information from each component image and produces a map of all image boundaries that is included with the mosaic. This makes scientific analyses that are facilitated by the mosaic fully traceable and reproducible. With this mosaic, scientists can now readily analyze landforms without the overhead of downloading individual images and perform complete global analyses of Mars at a resolution capable of seeing rock outcrops on the surface. Key Points: A global mosaic of Mars at 5.0 m/pixel has been created from 86,571 Context Camera images that are co‐registered to each other and seam‐correctedNon‐destructive image processing was used to preserve all image metadata and map seamsThe new mosaic will facilitate systematic studies of Mars at 5.0 m/pixel spatial resolution [ABSTRACT FROM AUTHOR]

Introduction: As people increasingly migrate to online shopping platforms, hard‐won improvements in requirements for consumer information provision at the point of sale are being eroded. An example is the alcohol pregnancy warning label for packaged alcoholic beverages that has been recently introduced in Australia and New Zealand. The aim of the present study was to assess the extent to which the pregnancy warning was visible at the online point of sale when the requirement became mandatory in August 2023. Methods: Data for alcohol products sold on the websites of the two largest alcohol retailers in Australia were web‐scraped from 1 to 3 August 2023. The captured data for 8343 alcoholic beverages were inspected to determine whether the pregnancy warning was visible. Results: Virtually no products (0.1%) had the mandatory warning visible on the main sales page, and only 7% enabled visibility of the warning via optional product image rotation functionality. Discussion and Conclusions: The almost complete absence of the mandatory pregnancy warnings on the main product pages of major alcohol retailers' websites highlights the regulatory problems posed by the emerging shift to online shopping. The very low prevalence of visible pregnancy warnings is likely to be an overestimate of the extent to which consumers would be exposed to warnings due to images being counted as being present regardless of their quality or readability. New regulation is needed to ensure that mandatory information requirements for harmful products are applied to online shopping contexts. [ABSTRACT FROM AUTHOR]

The market share of e‐commerce grocery is swiftly rising, but online product listings may present different environmental sustainability and public health information. We analyzed product marketing content available on retailer websites and product images, comparing plant‐based (PBFs) and animal‐sourced foods (ASFs). Over sixteen thousand marketing and labeling content observations were gathered from seven leading U.S. e‐commerce grocery retailers for top‐selling ASFs and closely‐matched PBFs (N = 134; n = 68 PBFs, n = 66 ASFs), including sustainability, mandatory information, health, sourcing, and hedonic/emotional claims. PBFs averaged 47 total claims, ASFs 28. On‐web claims were substantially higher than on‐pack (PBFs 34 on‐web, 12 on‐pack; ASFs 19 and nine, respectively). On‐web information for the same products varied considerably across retailers, especially for sustainability and mandatory information. PBFs presented mostly sustainability claims on‐pack and health claims on‐web. ASFs led emotional claims and made almost no sustainability claims across settings. Across settings, mandatory information compliance was greater among PBFs whereas ASFs notably lacked disclosures for foods with major allergens. Online grocery lacks standardization—businesses should regard differences as opportunities for marketing sustainable foods. [ABSTRACT FROM AUTHOR]

Recently people often purchase their daily needs at retail stores. Therefore, crowds might happen due to a manual queueing system. To overcome the problem, the smart system based on object detection has been conducted using several object detection methods. This study proposed YOLOv8 combined with transformer Real-Time Detection Transformer (RT-DETR) model to enhance the method performance in detecting the detail products. The intra-Class Variation method has been used to recognize the characteristics of the products such as size, color, and variant of the product. To validate the proposed model, three different datasets have been applied that is grocery dataset that displays products one by one in the training and validation process, the RPC-dataset that has many products in one image, and the D2S dataset with products that have varying lighting and stacked products. Results showed that the proposed model outperformed compared to other models, with a mean Average Precision (mAP) of 99.5% for the grocery dataset, 99.3% RPC-dataset, and 85.5% D2S dataset, respectively. [ABSTRACT FROM AUTHOR]

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ekonomi Manajemen Sistem Informasi (JEMSI) is the property of Dinasti Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When shopping online, it is difficult to estimate size from visual cues in the form of product images. While package shape and color have been discussed as factors influencing perceptions of product size, this research focuses on the influence of visual cues, such as the number of dimensions of the product image (i.e., two‐dimensional image vs. three‐dimensional image) on perceptions of product size. This study involved three surveys whose results revealed that three‐dimensional product images are perceived as larger products than two‐dimensional product images. Moreover, three‐dimensional product images positively influenced perceived product weight, suggesting a downstream effect from perceived weight on willingness to pay (WTP, Study 3). The results of this study contribute to a better understanding of how consumers perceive product size from product images in online retailing. Specifically, the results of this study extend our understanding of the mechanisms behind product size perception by including the influential factor of product image dimensionality in size perception bias. In addition, these findings have practical implications. In recent years, food packaging has become smaller, but consumers may react negatively to smaller packages, even when the volume of the contents remains unchanged. For these practical concerns, it may be possible to mitigate the negative effects of downsizing by presenting product images in a three‐dimensional format. [ABSTRACT FROM AUTHOR]

To share images securely in the network, this paper proposes a multiparty sharing of quantum images scheme based on the basic theory of quantum state sharing. According to the number of qubits within the shared quantum image and involved participants, the product state of a certain number of maximal entanglement GHZ state with specific qubits has been chosen to act as quantum channel. To achieve the process of quantum image sharing, the dealer Alice performs local unitary operations of the Controlled-NOT gate and Hadamard gate as well as single-qubit measurement operation, and other assistant participants perform single-qubit measurement operation. On basis of Alice's and other participants' measurement results, the assigned participant can reconstruct the shared quantum image on his local qubits via performing local unitary operations. Since all participants in present scheme only need perform the single-qubit measurement operations, it makes the present scheme more realizable than former similar schemes. The comparisons illustrate that the present scheme possesses higher qubit efficiency. Security analysis proves that the present scheme is immune to the intercept-resend and entangled probe attacks of a third party. [ABSTRACT FROM AUTHOR]

Chinese beef imports have been increasing in recent years. At the same time, Chinese public sentiment toward foreign countries, including those who export beef to China, has been changing. Therefore, this research uses a discrete choice experiment (DCE) to investigate the role of ethnocentrism, country image, and product image on consumer willingness to pay for country‐of‐origin labeled beef. Results indicate, on average, Chinese consumers prefer domestic beef most, and value beef from Australia and the US somewhat similarly. Their willingness to pay varies based on their perceived image of the country that the beef originates from and based on their perception of the safety of the beef, that is, product image. The more ethnocentric consumers are, the more they prefer domestic beef and discount foreign beef. Importantly, the effects of ethnocentrism, country image, and product image are stronger on the willingness to pay for domestic beef than for imported beef. More generally, findings indicate that controlling for ethnocentrism, country image, and product image contributes to understanding consumer willingness to pay for products originating from foreign countries. Overall, findings suggest that stronger ethnocentric tendencies lead to lower willingness to pay for imported beef (with some exceptions), and positive country image and product image increase the willingness to pay for imported beef. Thus, it is important to consider these constructs when estimating consumer willingness to pay for imported products, especially for countries where public sentiment toward exporting countries may be prone to change in a dynamic global environment. [ABSTRACT FROM AUTHOR]

While polarizer‐free (Pol‐less) OLED displays increase the efficiency of the panels with lowering the thickness, there comes an issue of the screen quality drop due to diffraction. We have investigated phenomena and invented a novel model for diffraction, since the color‐difference‐based evaluation alone has limitations in responding to customer needs in various environments and reflecting situations in which actual users embrace the screen quality. The main spatial features of the diffraction appearance were classified into the size and complexity of the pattern, and each factor was quantified through a specialized model. Diffraction features can be automatically calculated as numerical values from the invented model reflecting perceptual levels, which acts as a 'ruler' to achieve a good 'grade' of image quality in the product development stage. In this paper, we demonstrate what plays vital roles in diffraction appearance on Pol‐less displays and how to evaluate the diffraction in representative values. [ABSTRACT FROM AUTHOR]

The modern QD backplane production line deploys deep learning‐based defect classifier, which uses backplane images, to the auto repair system. However, one key challenge in this application is the lack of defect images available for training when a new product is first introduced. To address this issue, generative models like GAN have been used to generate defect images for new products but the image quality and diversity are not optimal due to unstable training and mode collapse. In this paper, the newest diffusion model is applied for defect image generation. It is trained on both normal (defect‐free) and defect images from new Product A and existing Product B. This allows the model to learn defects from Product B and generate synthetic defect images of Product A from normal images. Using an innovative method to automatically apply masks in diffusion sampling, the generated synthetic images are demonstrated to achieve high image quality and good defect diversity while preserving image fidelity outside the defective region when compared to the input normal image. Experiments are also conducted to train classifiers with generated defect images from different sampling methods. The proposed automatic masking method outperforms other sampling methods in the experiments. [ABSTRACT FROM AUTHOR]

This paper addresses the phenomenon of sloshing and the issues that arise during liquid handling at visual inspection stations. The pharmaceutical industry, recently put under pressure by the pandemic, has long adopted modular solutions consisting mainly of robotic islands. This work focuses on a visual inspection island for glass vials and ampules called VRU. This machine uses robotic arms to optimize the inspection process and enables automated control of a wide range of products using image recognition techniques and AI algorithms. However, the handling of containers in the presence of liquids requires special precautions to avoid the occurrence of bubbles inside the fluid that can prevent the cameras from correctly capturing any defects present. The banal solution involves a drastic reduction in the speeds and accelerations to which the liquids are subjected. However, using appropriate techniques makes it possible to achieve performance values similar to those obtainable when manipulating solid materials. The developed algorithms were tested using multibody simulations in the Mathworks Simscape environment and then validated using a six-axis Fanuc robot. In this study, however, the analysis conducted aimed to determine the correlations between trajectories, laws of motion, and sloshing in containers handled at high speed in industrial applications. In this study a multibody model was developed using a CFD analysis. The container consisted of a glass vial for pharmaceutical uses containing a liquid inside. The results obtained from the CFD analysis allowed us to calibrate the multibody model for the next phase of optimization of the laws of motion to be followed by the manipulator. [ABSTRACT FROM AUTHOR]

The pre-loved luxury market has attracted more attention from both practitioners and researchers, yet empirical research on this market remains scarce. Since information asymmetry is a bigger challenge in the pre-loved luxury digital market, it is essential to understand the effectiveness of different information disclosure strategies. This paper finds that the information disclosure effect is important yet complicated in the pre-loved luxury digital market. Not only does it depend on how much information to disclose, it also depends on what type of information to share. While providing more product images helps to sell preowned luxury products faster, providing benchmark price information may backfire. Further exploring the mechanism, we find that information disclosure influences sales also through other marketing mix. Interestingly, more product image information reduces consumers' price sensitivity, but extra benchmark price information increases consumers' price sensitivity. Actionable managerial implications are provided to guide sellers on leveraging information disclosure strategies in this market. [ABSTRACT FROM AUTHOR]

In this paper, we prove that every matrix over a division ring is representable as a product of at most 10 traceless matrices as well as a product of at most four semi-traceless matrices. By applying this result and the obtained so far other results, we show that elements of some algebras possess some rather interesting and nontrivial decompositions into products of images of non-commutative polynomials. [ABSTRACT FROM AUTHOR]

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts. [ABSTRACT FROM AUTHOR]

As one of wavefield separation methods in elastic reverse time migration, the decoupled wave equation succeeds to separate the particle velocity of P‐ and S‐wave without mode crosstalk. However, it fails to decouple the stress of P‐ and S‐wave which encounters with the problem of crosstalk. To overcome the crosstalk, the paper has proposed the quasi‐stress equations to decouple the stress tensor wavefields of P‐ and S‐wave based on the decoupled stress–strain relationship. Because P‐ and S‐wave stress wavefields are second‐order tensors, different from the imaging method of vector decoupled particle velocity wavefields, a double dot product imaging condition is proposed to obtain scalar images. Based on the total source‐stress tensor wavefields and separated receiver‐stress tensor wavefields, we obtain the scalar images of quasi‐PP and quasi‐PS from the stress information by the double dot product imaging condition. Several numerical examples are evaluated to demonstrate the effectiveness of the separation of P‐ and S‐wave stress and advantage of the proposed imaging condition from the second‐order stress tensor wavefields. [ABSTRACT FROM AUTHOR]

This research aims to examine the influence of brand image, brand trust and word of mouth on product purchasing decisions. The population in this study is users of Garnier facial foam products in South Tangerang City and aged 17 years and over. The technique used for sample collection is the Side Purposive sampling technique, thus the sample used isin this study is 140. This research uses SmartPLS. The results of this study showed that brand image, brand trust and word of mouth has a significant positive effect on the purchase decision of Garnier facial foam products in South Tangerang City. The results of this research provide benefits for Garnier to improve its existing brand image to increase product sales and maintain environmental damage with a green beauty strategy. Furthermore companies can increase brand trust (trust) for the purpose of increasing consumer trust in the company, by building communication. other benefits are companies can increase word of mouth by providing something interesting. Companies need to find ways to make customers talk about the products they sell. [ABSTRACT FROM AUTHOR]

Obtaining a large number of unqualified product samples in industrial production is an arduous task. It is challenging to learn the features of few-shot object images. Despite the limited number of original images, we developed a transfer learning method called LDFISB (Large-scale Dataset to Few -Shot Image with Similar Background) that provides a feasible solution. LDFISB is trained on a large-scale dataset such as CIFAR100, and then the model is fine-tuned based on the original model and parameters to achieve classification tasks on a new APSD (auto part surface dataset) Batch normalization, padding, and Weighted Cross Entropy Loss are employed in the training processes. Hyper-parameters are configured according to Hyper-table to enhance the accuracy of the prediction. The CIFAR10, CIFARl 00, and ImageNet were considered as pre-training datasets, and the LDFISB method is capable of accurately predicting the flaw area of the product image. The LDFISB method achieves the highest accuracy on the CIFAR 100 pre-training dataset. [ABSTRACT FROM AUTHOR]

Purpose: The halal cosmetics industry continues to grow significantly. Furthermore, using halal cosmetics is a must for Muslims. This study aims to analyze the factors influencing the switching intention to halal cosmetics in Indonesia. Design/methodology/approach: This quantitative study uses a Partial Least Square-Structural Equation Modelling (PLS-SEM) on 214 respondents. The variables include halal certification, halal awareness, product image, perceived behavioral control, subjective norm, attitude, advertisement and switching intention. Findings: The product image plays the most influential role in deriving the attitude toward switching intention to halal cosmetics, following perceived behavioral control, halal awareness and subjective norm, but not halal certification and advertisement. The result indicates that the image of halal cosmetics influences customers' attitudes toward switching to using halal cosmetics. Indonesian customers know the obligation to use halal products because they are Muslim. However, the existence of halal certification does not derive the switching intention to halal cosmetics. Research limitations/implications: This study conducts research only in Indonesia. As a recommendation, further studies might conduct a comparative test using multicultural respondents in several countries. Other studies also suggested examining factors of switching intention through different generational, especially in countries with high individualism traits. Practical implications: This study will encourage the halal industry, especially the halal cosmetics industry, to pay more attention to the product image. Meanwhile, the government can provide incentives or rewards to promote industry participation in halal cosmetics. The findings provide a more detailed understanding of how product image can influence someone to switch to halal cosmetics. Originality/value: Research on switching intention to halal cosmetics is still limited. This study uses halal variables, while previous studies only used religiosity. This study also introduced the product images motivating customers' switching intention to use halal cosmetics. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image. Design/methodology/approach: The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block. Findings: Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs. Originality/value: The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier. [ABSTRACT FROM AUTHOR]

To improve the innovation of agricultural machinery product styling, this paper proposes a shape structure behavior function (SSBF) model suitable for the industrial design field. The feature line evolution method combining shape grammar and genetic algorithm was used for modelling the of the grader, which not only maintains the product style characteristics but also reflects the typical identification characteristics of the bionic prototype and produces a new product modelling scheme. By conducting cognitive and recognition experiments on product styling features, the ranking of product styling features and the contribution of each component to product styling were determined. The method of combining shape grammar and quadratic Bézier curve was used to express and encode feature lines, and genetic algorithm was used to evolve biomimetic forms to form product feature lines with typical biological morphological features; The extracted form bionic elements were integrated into the grader modelling design, and the interaction evaluation was carried out through the genetic algorithm evolution scheme. The basic form elements were extracted and analyzed, and the deduction rules were formulated and reorganized. The derived feature line geometric data considered the product's image features and the bio-inspired prototype, which can be used for the follow-up guidance of industrial design schemes. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of the direct product of sets that involve Fermatean neutrosophic (FN) elements in structures called INK-algebras. It defines terms like the direct product of Fermatean neutrosophic INK-ideals (FNINK-Is) in INK-algebras and Fermatean neutrosophic sets (FNSs), FNINK-Is, and Fermatean neutrosophic closed INK-ideals (FNCINK-Is). The proof of theorems illustrating the relationships between these ideas is included in the paper. It also defines the INK-subalgebra embedded in an INK-algebra and gives a theorem elucidating the connection between the direct product of FNINK-Is and the images of these subalgebras. In essence, the paper investigates and establishes connections between different mathematical ideas concerning INK-algebras and FNSs. [ABSTRACT FROM AUTHOR]

Copyright of Estudios Gerenciales is the property of Universidad ICESI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, people are interested in cafes, so it's providing opportunities for business people to open and manage cafe businesses. Developments in the cafe business have made people choose modern cafes that suit their interests. This research was carried out to know the influence of brand image and product quality on purchasing decisions. Tanduk Cafe and Eatery is a cafe that has been around for quite a long time in Malang City. Online questionnaires were distributed to consumers of Tanduk Cafe and Eatery in Malang City using a non-probability sampling method with an accidental sampling technique. The sample in this study amounted to 78 respondents. This research shows the results of data processing using PLS. From the research results it can be concluded that brand image and product quality have a positive and significant influence on purchasing decisions. [ABSTRACT FROM AUTHOR]