Communicating Product Sustainability: Consumer Responses to Sustainability Labeling in a Retail Laboratory Environment

In effort to enhance sustainable development, manufacturers and retailers have collaborated to develop a standardized sustainability index based on supply chain life cycle information. However, it is unclear whether this index will help consumers make more sustainable purchases. Research conducted in a retail laboratory addresses consumer attitudes, purchase intentions, and product choices with and without a credible standardized sustainability index, and with or without provision of background sustainability information. Results from a pilot study and two mixed design experiments indicate that, on average, consumers focus more on brand equity than on sustainability levels when they make brand choices. While the disclosure of credible information for brands within a product category affects brand-level sustainability perceptions, there are limited effects on brand purchase intentions and choices. Results also reveal a consumer misconception that nationally recognized brands are more sustainable. Implications of results are offered for producers, retailers, and public policy makers

Sum-frequency generation from etchless lithium niobate empowered by dual quasi-bound states in the continuum

The miniaturization of nonlinear light sources is central to the integrated photonic platform, driving a quest for high-efficiency frequency generation and mixing at the nanoscale. In this quest, the high-quality ($Q$) resonant dielectric nanostructures hold great promise, as they enhance nonlinear effects through the resonantly local electromagnetic fields overlapping the chosen nonlinear materials. Here, we propose a method for the enhanced sum-frequency generation (SFG) from etcheless lithium niobate (LiNbO$_{3}$) by utilizing the dual quasi-bound states in the continuum (quasi-BICs) in a one-dimensional resonant grating waveguide structure. Two high-$Q$ guided mode resonances corresponding to the dual quasi-BICs are respectively excited by two near-infrared input beams, generating a strong visible SFG signal with a remarkably high conversion efficiency of $3.66\times10^{-2}$ (which is five orders of magnitude higher than that of LiNbO$_{3}$ films of the same thickness) and a small full-width at half-maximum less than 0.2 nm. The SFG efficiency can be tuned via adjusting the grating geometry parameter or choosing the input beam polarization combination. Furthermore, the generated SFG signal can be maintained at a fixed wavelength without the appreciable loss of efficiency by selectively exciting the angular-dependent quasi-BICs, even if the wavelengths of input beams are tuned within a broad spectral range. Our results provide a simple but robust paradigm of high-efficiency frequency conversion on an easy-fabricated platform, which may find applications in nonlinear light sources and quantum photonics

EDIS: Entity-Driven Image Search over Multimodal Web Content

Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion. In this work, we introduce \textbf{E}ntity-\textbf{D}riven \textbf{I}mage \textbf{S}earch (EDIS), a challenging dataset for cross-modal image search in the news domain. EDIS consists of 1 million web images from actual search engine results and curated datasets, with each image paired with a textual description. Unlike datasets that assume a small set of single-modality candidates, EDIS reflects real-world web image search scenarios by including a million multimodal image-text pairs as candidates. EDIS encourages the development of retrieval models that simultaneously address cross-modal information fusion and matching. To achieve accurate ranking results, a model must: 1) understand named entities and events from text queries, 2) ground entities onto images or text descriptions, and 3) effectively fuse textual and visual representations. Our experimental results show that EDIS challenges state-of-the-art methods with dense entities and a large-scale candidate set. The ablation study also proves that fusing textual features with visual features is critical in improving retrieval results

Efficient photon-pair generation empowered by dual quasi-bound states in the continuum

Here we demonstrate the efficient photon-pair generation via spontaneous parametric down conversion from a semiconductor metasurface supporting dual quasi-bound states in the continuum (quasi-BICs). In a simple metasurface design composed of AlGaAs ellipse nano-cyclinders, the two high-$Q$ quasi-BIC resonances that coincide with the generated signal and idler frequencies significantly boost the local electric field. This leads to a substantial enhancement in the reverse classical nonlinear process of sum frequency generation and subsequently the remarkable high generation rate of photon pairs under the quantum-classical correspondence principle. Within a narrowband wavelength regime around the quasi-BIC resonances, the rate of pair production is enhanced up to $\sim10^{4}$ Hz, two orders of magnitude larger than that in the Mie resonant AlGaAs nanoantennas. Moreover, the photon pair emission is mainly concentrated in the normal direction with respect to the metasurface, and shows tunable rate with the $Q$ factor by engineering the rotation angle of nano-cylinders. The presented work enables nanoscale sources of high-quality entangled photons which will find applications in advanced quantum imaging and communications

Subject-centered multi-view feature fusion for neuroimaging retrieval and classification

Multi-View neuroimaging retrieval and classification play an important role in computer-aided-diagnosis of brain disorders, as multi-view features could provide more insights of the disease pathology and potentially lead to more accurate diagnosis than single-view features. The large inter-feature and inter-subject variations make the multi-view neuroimaging analysis a challenging task. Many multi-view or multi-modal feature fusion methods have been proposed to reduce the impact of inter-feature variations in neuroimaging data. However, there is not much in-depth work focusing on the inter-subject variations. In this study, we propose a subject-centered multi-view feature fusion method for neuroimaging retrieval and classification based on the propagation graph fusion (PGF) algorithm. Two main advantages of the proposed method are: 1) it evaluates the query online and adaptively reshapes the connections between subjects according to the query; 2) it measures the affinity of the query to the subjects using the subject-centered affinity matrices, which can be easily combined and efficiently solved. Evaluated using a public accessible neuroimaging database, our algorithm outperforms the state-of-the-art methods in retrieval and achieves comparable performance in classification

Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking

The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative backtracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises. By evaluating the proposed pipeline with the data provided by the Diadem challenge and the recent BigNeuron project, Rivulet is shown to be robust to challenging microscopic imagestacks. We discussed the algorithm design in technical details regarding the relationships between the proposed algorithm and the other state-of-the-art neuron tracing algorithms

Sulphur isotopes toward Sagittarius B2 extended envelope in the Galactic Center

The isotopic ratios are good tools for probing the stellar nucleosynthesis and chemical evolution. We performed high-sensitivity mapping observations of the J=7-6 rotational transitions of OCS, OC34S, O13CS, and OC33S toward the Galactic Center giant molecular cloud, Sagittarius B2 (Sgr B2) with IRAM 30m telescope. Positions with optically thin and uncontaminated lines are chosen to determine the sulfur isotope ratios. A 32S/34S ratio of 17.1\pm0.9 was derived with OCS and OC34S lines, while 34S/33S ratio of 6.8\pm1.9 was derived directly from integrated intensity ratio of OC34S and OC33S. With independent and accurate measurements of 32S/34S ratio, our results confirm the termination of the decreasing trend of 32S/34S ratios toward the Galactic Center, suggesting a drop in the production of massive stars at the Galactic centre.Comment: 20 pages, 7 figures, accepted by PAS

Reconstruction of 3D neuron morphology using Rivulet back-tracking

The 3D reconstruction of neuronal morphology is a powerful technique for investigating nervous systems. Due to the noises in optical microscopic images, the automated reconstruction of neuronal morphology has been a challenging problem. We propose a novel automatic neuron reconstruction algorithm, Rivulet, to target the challenges raised by the poor quality of the optical microscopic images. After the neuron images being de-noised with an anisotropic filter, the Rivulet algorithm combines multi-stencils fast-marching and iterative back-tracking from the geodesic farthest point on the segmented foreground. The neuron segments are dumped or merged according to a set of criteria at the end of each iteration. The proposed Rivulet tracing algorithm is evaluated with data provided from the BigNeuron Project. The experimental results demonstrate that Rivulet outperforms the compared state-of-the-art tracing methods when the images are of poor quality

The Frontier of Tungsten Oxide Nanostructures in Electronic Applications

Electrochromic (EC) glazing has garnered significant attention recently as a crucial solution for enhancing energy efficiency in future construction and automotive sectors. EC glazing could significantly reduce the energy usage of buildings compared to traditional blinds and glazing. Despite their commercial availability, several challenges remain, including issues with switching time, leakage of electrolytes, production costs, etc. Consequently, these areas demand more attention and further studies. Among inorganic-based EC materials, tungsten oxide nanostructures are essential due to its outstanding advantages such as low voltage demand, high coloration coefficient, large optical modulation range, and stability. This review will summarize the principal design and mechanism of EC device fabrication. It will highlight the current gaps in understanding the mechanism of EC theory, discuss the progress in material development for EC glazing, including various solutions for improving EC materials, and finally, introduce the latest advancements in photo-EC devices that integrate photovoltaic and EC technologies