Search

Although food waste is an urgent issue with widespread economic, societal, and environmental impacts, it remains understudied in the marketing discipline. This is surprising, since most food waste occurs at the retail and consumption stages of the food life cycle. This research fills this gap by examining how resource mindset and self-construal jointly shape consumer food waste. Specifically, inducing a scarcity mindset signals that there is no resource to waste, mitigating consumer food waste regardless of self-construal. In contrast, under an abundance mindset, where there is resource to waste, activating an interdependent (vs. independent) self-construal can effectively reduce consumer food waste. The authors identify sharing obligation, the tendency to share valuable resources with in-groups, as a key mechanism behind the effect. In support of this mechanism, enhancing sharing obligation (e.g., highlighting the sharing concept, highlighting others' food needs) or diminishing it (e.g., highlighting family resource abundance) attenuates the effect of self-construal on consumer food waste under an abundance mindset. The results from one large-scale field study, four controlled experiments, and a country-level secondary data analysis provide convergent support for the proposed framework. This research not only contributes to the related literature but also provides actionable strategies for mitigating consumer food waste. [ABSTRACT FROM AUTHOR]

Although the growing transformation from offline to online shopping urges marketers to understand the impacts of this shift on brand loyalty, conclusions from the existing marketing literature have been inconsistent. Drawing on the distinctions between online and offline shopping, the authors develop a theoretical framework that focuses on uncertainty avoidance (UA) to reconcile the inconsistencies in the literature. Specifically, the authors argue that high-UA (vs. low-UA) individuals are more (vs. less) brand loyal when shopping online than when shopping offline because the product experience is less predictable in the former (vs. latter), a distinction between online and offline shopping that increases (reduces) high-UA (low-UA) individuals' tendency to stay with the brands. In findings consistent with this logic, the authors uncover that intangible value salience, a theoretically and managerially relevant boundary condition, attenuates the interplay between online (vs. offline) shopping and UA. Across eight studies that consist of secondary data, field studies, and online and laboratory experiments, the authors find converging evidence to support this theoretical stance. This research contributes insights to the loyalty literature and cross-cultural research. The managerial implications are also discussed. [ABSTRACT FROM AUTHOR]

According to the existing Retinex algorithm can not automatically adjust the parameters, an adaptive bilateral filter Retinex algorithm based on parameter estimation was proposed. Firstly, the principal component analysis and the Canny edge detection algorithm are used to estimate the noise and edge intensity. Then the spatial geometric standard deviation parameter and the luminance standard deviation parameter of the bilateral filter are calculated by linear correlation calculation. And the bilateral filter of the parameter estimation is used to decompose the image and the reflected image. Finally, the illumination image and the reflected image are compressed and enhanced in different ways and a new image is synthesized. The experimental results show that the algorithm can not only automatically set the parameters, but also effectively inhibit the halo phenomenon.

Three‐dimensional (3D) reconstruction is a pivotal research area within computer vision and photogrammetry, offering a valuable foundation of data for the development of smart cities. However, existing methods for constructing 3D models from unmanned aerial vehicle (UAV) images often suffer from slow processing speeds and low central processing unit (CPU)/graphics processing unit (GPU) utilization rates. Furthermore, the utilization of cluster‐based distributed computing for 3D modelling frequently results in inefficient resource allocation across nodes. To address these challenges, this paper presents a novel approach to 3D modelling in clusters, incorporating a dynamic load‐balancing strategy. The method divides the 3D reconstruction process into multiple stages to lay the groundwork for distributing tasks across multiple nodes efficiently. Instead of traditional traversal‐based communication, this approach employs gossip communication techniques to reduce the network overhead. To boost the modelling efficiency, a dynamic load‐balancing strategy is introduced that prevents nodes from becoming overloaded, thus optimizing resource usage during the modelling process and alleviating resource waste issues in multidevice clusters. The experimental results indicate that in small‐scale data environments, this approach improves CPU/GPU utilization by 35.8%/23.4% compared with single‐machine utilization. In large‐scale data environments for cluster‐based 3D modelling tests, this method enhances the average efficiency by 61.4% compared with traditional 3D modelling software while maintaining a comparable model accuracy. [ABSTRACT FROM AUTHOR]

The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network's training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings. [ABSTRACT FROM AUTHOR]