Channel Information Research Articles

Deep blind super-resolution for hyperspectral images

Current deep learning methods for single hyperspectral image super-resolution are non-blind ones, which adopt the simplistic bicubic degradation model. These models have poor generalization performance and cannot handle unknown degradation. Additionally, blind super-resolution methods for RGB images neglect rich spectral information in hyperspectral images, which causes spectral distortion in results. To address these issues, we consider the degeneration estimation and propose a single hyperspectral image blind super-resolution algorithm. Specifically, we first use a blur kernel estimation network and a deblurring network to obtain the image without blur. We change the receptive field by exchanging spatial channel information when estimating the kernel, thereby obtaining blurry information at different scales. In addition, to reduce the errors introduced by kernel mismatch and the relevant information among bands ignored by the group convolution, we fuse the spatial information of the blurred image to compensate for the errors and then utilize the spatial and spectral information of other bands to guide the feature extraction of the current band. Finally, we integrate the feature information from different branches in a global fusion network to improve spatial and spectral information fidelity further. Extensive experiments on synthetic and real-world hyperspectral datasets unequivocally demonstrate that our approach outperforms other state-of-the-art approaches in terms of both quantitative and qualitative evaluations. Our code is publicly available at https://github.com/YoungP2001/DBSR.

Information ecosystems in early academic career building: how do researchers in the social sciences and humanities learn the tricks of the trade?

Early career investigators (ECIs) in the Social Sciences and Humanities need to receive adequate information so that they will be empowered to progress in their academic career and deal with the various evaluation processes that constitute an essential part of their professional development. This article relies on an informational-ecosystemic approach originally developed in the context of resilience studies and crisis communication and uses it as a theoretical framework to analyse and understand the “early academic career building information ecosystems” (EACBIEs), into which ECIs professionally develop. The characteristics of these ecosystems are then refined through the analysis of interviews conducted with ECIs from all around Europe in the framework of the European Network for Research Evaluation in Social Sciences and Humanities (ENRESSH), European Cooperation in Science and Technology (COST) Action. The analysis reveals the remarkable heterogeneity of the information ecosystems into which early career researchers have to build their career in Europe, articulating a diversity of formal, non-formal and informal learning environments, and several related information channels, as well as showing a geographical spread that covers institutional, national and international levels. Furthermore, although the diverse information channels at hand and geographical levels at which they operate appear in some cases to be complementary, and even mutually reinforcing, they can also, in other cases, be dysfunctional, fragmented and unfair to some extent.

Quantum inception score

Motivated by the great success of classical generative models in machine learning, enthusiastic exploration of their quantum version has recently started. To depart on this journey, it is important to develop a relevant metric to evaluate the quality of quantum generative models; in the classical case, one such example is the (classical) inception score (cIS). In this paper, as a natural extension of cIS, we propose the quantum inception score (qIS) for quantum generators. Importantly, qIS relates the quality to the Holevo information of the quantum channel that classifies a given dataset. In this context, we show several properties of qIS. First, qIS is greater than or equal to the corresponding cIS, which is defined through projection measurements on the system output. Second, the difference between qIS and cIS arises from the presence of quantum coherence, as characterized by the resource theory of asymmetry. Third, when a set of entangled generators is prepared, there exists a classifying process leading to the further enhancement of qIS. Fourth, we harness the quantum fluctuation theorem to characterize the physical limitation of qIS. Finally, we apply qIS to assess the quality of the one-dimensional spin chain model as a quantum generative model, with the quantum convolutional neural network as a quantum classifier, for the phase classification problem in the quantum many-body physics. Published by the American Physical Society 2024

Data-driven Bayesian Gaussian mixture optimized anchor box model for accurate and efficient detection of green citrus

The demand for harvesting green citrus has increased due to the rapid growth of the green citrus industry. However, real-time detection of green citrus is challenging because of its high visual similarity to the background. To address this issue, a new model called EDGC-YOLO (Efficient Detection of Green Citrus based on the YOLO) has been developed, focusing on detecting green citrus effectively and accurately. Specifically, the proposed model uses a data-driven approach that employs the highest confidence level from the aspect ratio of unobstructed citrus fruits to infer anchor boxes of obscured citrus. This method ensures a high degree of coincidence with bounding boxes based on empirical annotation. Furthermore, analyzing the distribution range of various citrus annotation categories within the dataset and the incorporating the highest confidence level of the normal distribution regarding aspect ratios, the initial cluster centers of the BGMM (Bayesian Gaussian Mixture Model) are dynamically adjusted. This strategy enables the anchor boxes to be inferred from the best suited for detecting green citrus. These improved anchor boxes provide a more accurate and effective solution for centroid positioning of obscured green citrus, significantly enhancing the entire network model’s detection accuracy and lightweight efficiency. To further improve detection accuracy and reduce model size, this study integrated the Refined-EfficientNetV2 network as the backbone, enhanced with a Convolutional Block Attention Module (CBAM) for better feature extraction. This integration allows the model to effectively capture relevant channel information and spatial features, increasing the feature extraction capability for green citrus images. Experimental results demonstrate the model’s superior performance: parameters reduced to 4.52 million, computational demand to 7.8 GFLOPs (64.4 % and 49.4 % of the original model, respectively), and model size decreased to 9.4 MB (65.3 % of the original). Additionally, the optimized model improved Precision (P), Recall (R), and mean Average Precision (mAP) by 0.5 %, 1.6 %, and 3.0 %, respectively, compared to the original model. The proposed model achieves higher detection accuracy with a smaller model size, providing theoretical support for the harvesting decision-making of green citrus harvesting robots.

Study on the Estimation of Leaf Area Index in Rice Based on UAV RGB and Multispectral Data

Leaf area index (LAI) is a key variable for monitoring crop growth. Compared with traditional measurement methods, unmanned aerial vehicle (UAV) remote sensing offers a cost-effective and efficient approach for rapidly obtaining crop LAI. Although there is extensive research on rice LAI estimation, many studies suffer from the limitations of models that are only applicable to specific scenarios with unclear applicability conditions. In this study, we selected commonly used RGB and multispectral (Ms) data sources, which contain three channels of color information and five multi-band information, respectively, combined with five different spatial resolutions of data at intervals of 20–100 m. We evaluated the effectiveness of models using single- and multi-feature variables for LAI estimation in rice. In addition, texture and coverage features other than spectra were introduced to further analyze their effects on the inversion accuracy of the LAI. The results show that the accuracy of the model established with multi-variables under single features is significantly higher than that of the model established with single variables under single features. The best results were obtained using the RFR (random forest regression) model, in which the model’s R2 is 0.675 and RMSE is 0.886 for multi-feature VIs at 40 m. Compared with the analysis results of Ms and RGB data at different heights, the accuracy of Ms data estimation results fluctuates slightly and is less sensitive to spatial resolution, while the accuracy of the results based on RGB data gradually decreases with the increase in height. The estimation accuracies of both Ms and RGB data were improved by adding texture features and coverage features, and their R2 improved by 9.1% and 7.3% on average. The best estimation heights (spatial resolution) of the two data sources were 40 m (2.2 cm) and 20 m (0.4 cm), with R2 of 0.724 and 0.673, and RMSE of 0.810 and 0.881. This study provides an important reference for the estimation of rice LAI based on RGB and Ms data acquired using the UAV platform.

Merging hierarchical triple black hole systems with intermediate-mass black holes in population III star clusters

ABSTRACT Theoretical predictions suggest that very massive stars have the potential to form through multiple collisions and eventually evolve into intermediate-mass black holes (IMBHs) within Population III star clusters embedded in mini dark matter haloes. In this study, we investigate the long-term evolution of Population III star clusters, including models with a primordial binary fraction of $f_{\rm b}=0$ and 1, using the N-body simulation code petar. We comprehensively examine the phenomenon of hierarchical triple black holes in the clusters, specifically focusing on their merging inner binary black holes (BBHs), with post-Newtonian correction, by using the tsunami code. Our findings suggest a high likelihood of the inner BBHs containing IMBHs with masses on the order of $\mathcal {O}(100)\,{\rm M}_{\odot }$, and as a result, their merger rate could be up to $0.1{\rm Gpc}^{-3}{\rm yr}^{-3}$. The orbital eccentricities of some merging inner BBHs oscillate over time periodically, known as the Kozai–Lidov oscillation, due to dynamical perturbations. Detectable merging inner BBHs for mHz GW detectors LISA/TianQin/Taiji concentrate within $z\lt 3$. More distant sources would be detectable for CE/ET/LIGO/KAGRA/DECIGO, which are sensitive from $\mathcal {O}(0.1)$Hz to $\mathcal {O}(100)$ Hz. Furthermore, compared with merging isolated BBHs, merging inner BBHs affected by dynamical perturbations from tertiary BHs tend to have higher eccentricities, with a significant fraction of sources with eccentricities closing to 1 at mHz bands. GW observations would help constrain the formation channels of merging BBHs, whether through isolated evolution or dynamical interaction, by examining eccentricities.

Insulator detection based on FA‐YOLO network with improved feature extraction ability

AbstractUnmanned aerial vehicle insulator detection that aims to recognize defective insulators from transmission lines has made significant progress in recent years. However, it still faces challenges, such as the complex background of aerial images and the small memory of unmanned aerial vehicles. This paper proposes a refined insulator detection algorithm that integrates the attention mechanism in YOLOv8 to improve the feature extraction ability. Specifically, this paper introduces a fast vision transformers structure in the you only look once (YOLO) v8 backbone section to enhance feature extraction by capturing local and global features. Additionally, the global attention mechanism is incorporated in the neck for additional feature extraction by merging comprehensive spatial and channel information into the output. Furthermore, we amalgamate depth‐wise convolution, graph convolution, and residual operation in the global attention mechanism module. This design can mitigate the issues of gradient vanishing or exploding and meanwhile enhance the distinction between spatial attention and channel attention. The proposed model is then applied to a public dataset and a set of real images from a specific power station, and the detection results show that it outperforms many competitors in terms of accuracy, efficiency, and memory size.

Triggering dual-mode dynamic optical responses by anchoring carbon dots on MnO2 hollow spheres

Multi-mode responsive optical materials with versatile application prospects for optical sensing, optoelectrical devices, and information encryption are highly desired. Here we integrate fluorescent carbon dots onto the hollow MnO2 spheres, and modulate the interaction between them to achieve a water-stimulated responsive system with dual-mode dynamic signals in both fluorescence and structural color. The composite film shows a structural color shifting from blue to green as well as displays a fluorescence “on” in the presence of trace water, with both changes capable of being reversibly switched up to 10 times. The amide bond between the two components enables the controllable dispersion and aggregation of the carbon dots which is essential to the reversibility. Fabrication of several security labels with separate information in structural color and fluorescence channels demonstrates the application potential of the composite. This work provides a concise route to develop new multi-mode responsive optical materials for applications in anti-counterfeiting, sensing, and coding.

Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm.

In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.

Unveiling the Reaction Mechanism of the N(2D) + Pyridine Reaction: Ring-Contraction versus 7-Membered-Ring Formation Channels.

Despite the relevance of the reactions of the prototypical nitrogen-containing six-membered aromatic molecule (N-heterocyclic) of pyridine (C6H5N) in environmental science, astrochemistry, planetary science, prebiotic chemistry, and materials science, few experimental/theoretical studies exist on the bimolecular reactions involving pyridine and neutral atomic/molecular radicals. We report a combined experimental and theoretical study on the elementary reaction of pyridine with excited nitrogen atoms, N(2D), aimed at providing information about the primary reaction products and their branching fractions (BFs). From previous crossed molecular beam (CMB) experiments with mass-spectrometric detection and present synergistic calculations of the reactive potential energy surface (PES) and product BFs we have unveiled the reaction mechanism. It is found that the reaction proceeds via N(2D) barrierless addition to pyridine that, via bridged intermediates followed by N atom "sliding" into the ring, leads to 7-membered-ring structures. They further evolve, mainly via ring-contraction mechanisms toward 5-membered-ring radical products and, to a smaller extent, via H-displacement mechanisms toward 7-membered-ring isomeric products and their isomers. Using the theoretical statistical estimates, an improved fit of the experimental data previously reported has been obtained, leading to the following results for the dominant product channels: C4H4N (pyrrolyl) + HCN (BF = 0.61 ± 0.20), C3H3N2 (1H-imidazolyl/1H-pyrazolyl) + C2H2 (BF = 0.11 ± 0.06), and C5H4N2 (7-membered-ring molecules or pyrrole carbonitriles) + H (BF = 0.28 ± 0.10). The ring-contraction product channels C4H4N (pyrrolyl) + HCN, C3H3N2 (1H-imidazolyl) + C2H2, C3H3N2 (1H-pyrazolyl) + C2H2, and C5H5 (cyclopentadienyl) + N2 have statistical BFs of 0.54, 0.09, 0.11, and 0.07, respectively. Among the H-displacement channels, the cyclic-CHCHCHCHNCN + H channel and cyclic-CHCHCHCHCN2 + H are theoretically predicted to have a comparable BF (0.07 and 0.06, respectively), while the other isomeric 7-membered-ring molecule + H channel has a BF of 0.03. Pyrrole-carbonitriles and 1H-ethynyl-1H-imidazole (+ H) isomeric channels have an overall BF of 0.03. Implications for the chemistry of Saturn's moon Titan and prebiotic chemistry, as well as for understanding the N-doping of graphene or carbon nanotubes, are noted.

Research on Museum Brand Communication Strategy Based on AISAS Model

With the continuous development of the economy and the improvement of cultural confidence, the cultural demand and consumption scale represented by museum visits are growing rapidly. This study takes Dentsu's AISAS (Attention-Interest-Search-Action-Share) model as the theoretical analysis framework to analyze museum users' consumption behaviors in the Internet era, to explore museum brand communication strategies adapted to the new era. A total of 209 valid questionnaires were received in this study. The research finds that in the new media era with the popularization of the Internet, the behavior of museum users has changed from one-way passive to active searching and sharing. Based on the brand communication of museums, the author puts forward countermeasures and suggestions: multi-channel communication to enhance audience contact and attention; Enrich the content and methods to enhance the interest of the audience; Enrich the information channels to facilitate the audience to take the initiative to obtain information; The combination of physical and virtual museums enhances the convenience of participation; Enhance brand influence, cultivate audience loyalty and enhance sharing initiative.

Evaluating factors affecting sexual abuse prevention education for primary school students according to the life skills approach

This comprehensive study delves into the intricate factors influencing sexual abuse prevention education for elementary school students, capturing insights from diverse stakeholders, including 423 students, 115 teachers, and 423 parents. Employing a Likert scale, the research evaluates various dimensions, shedding light on the nuanced dynamics that shape prevention programs. The results reveal that student-related factors, encompassing positive behavior, cognitive capacity, personality traits, and knowledge levels, received moderately positive mean scores. This underscores the significant contribution of inherent qualities and cognitive abilities of primary school students to the effectiveness of sexual abuse prevention education. Moving to family-related factors, parents’ extensive knowledge and understanding garnered a positive mean score of 2.91, signifying a favorable impact, while financial considerations received a slightly lower mean score of 2.92. Notably, parental educational methods and the inclination to delegate educational responsibilities to schools scored higher at 2.99 each, underscoring the influential role parents play in shaping their children's perspectives on sexual abuse prevention. Within the school environment, positive interactions between teachers and students stood out with a mean score of 2.97, emphasizing the pivotal role of supportive teacher-student relationships in facilitating effective sexual abuse prevention education. Societal factors exhibited varying influences, with local customs, culture, and social psychology receiving a mean score of 2.91, indicating a moderately positive impact. However, the development of the internet and information channels scored lower at 2.60, revealing potential challenges in adapting prevention education to the evolving landscape of online information dissemination. These nuanced findings contribute to a deeper understanding of the multifaceted factors shaping sexual abuse prevention education for elementary school students, providing a foundation for evidence-based interventions and policy decisions.

Are remittances “resilient?” Evidence from Sri Lanka between COVID-19 and the crisis

Remittances have been characterized as resilient flows of capital that provide financial relief to migrant households and emerging economies during downturns, crises and other periods of hardship. This article examines patterns of remittance transfer to Sri Lanka between January 2020 and December 2022, drawing on Central Bank statistics and online remittance surveys with migrant workers to test prominent assumptions about the drivers of remittance behavior. Findings indicate little evidence to support explanatory factors linked to the resilience hypothesis, instead pointing to the likelihood that fluctuations in official remittance figures reflect shifts between formal and informal remittance channels.

Defog YOLO for road object detection in foggy weather

Abstract Object detection research predominantly focuses on clear weather conditions, often overlooking the challenges posed by foggy weather. Fog impairs the vision of onboard cameras, creating significant obstacles for autonomous vehicles. To tackle these issues, we present the Defog YOLO algorithm, specifically designed for road object detection in foggy conditions. Our approach integrates an enhanced U-Net framework for visual defogging, where the encoder leverages super-resolution back projection to combine multi-layer features. The decoder employs a back projection feedback mechanism to improve image restoration. Additionally, we augment the Feature Pyramid Network with a noise-aware attention mechanism, allowing the network to emphasize critical channel and spatial information while mitigating noise. Given the scarcity of labeled foggy images, we introduce a fog addition module to generate a more diverse training dataset. We validate our method using a synthesized FOG-TRAINVAL dataset, derived from the VOC dataset, demonstrating its robustness in foggy scenarios. Experimental results show that our proposed method achieves an mAP score of 60% on the Real-world Task-driven Testing Set foggy weather test set, with a precision of 86.7% and a recall of 54.2%. These findings underscore the effectiveness and improved generalizability of our approach for object detection in adverse weather conditions.

Detailed detection and extraction of estuarine tidal channels with multispectral and full‐polarised SAR remote sensing

AbstractEstuarine tidal channels are active geomorphic units in tidal flats. However, accurate information on the spatiotemporal changes in tidal channel systems remains scarce. The width of the tidal channels may vary from several kilometres to tens of centimetres. Monitoring tidal channel evolution is complicated because of periodic tidal scouring, anthropogenic activities and sea level rise. In this study, we propose a synergetic classification method to detect and extract morphological information of estuarine tidal channels with a spatial resolution of up to 3 m by fusing PlanetScope multispectral data with C‐band GaoFen‐3 fully polarised Synthetic Aperture Radar (SAR) data and machine learning algorithms. Considering the Yellow River Estuary as an example, the spectral features, vegetation and water index, polarisation and texture features derived from the multispectral and SAR images were selected as input data for classifiers according to feature importance ranking. Comparison to the maximum likelihood, and support vector machine classifiers, the synergetic classification with random forest showed the best performance, with an overall accuracy of 99.6%. Based on these results, the total number of tidal channels in the Yellow River Estuary reached 872, with a total length of 348.8 km. The spatiotemporal changes in the central axis over the last 4 years (2019–2022) suggest that the evolution of tidal channels was mainly controlled by ocean dynamics and anthropogenic activities. This method provides a cost‐effective alternative to accurately map tidal channel systems in global estuarine and coastal zones and helps to quantitatively describe their morphological evolution, stability and drivers.

A deep learning-based cascade algorithm for pancreatic tumor segmentation.

Pancreatic tumors are small in size, diverse in shape, and have low contrast and high texture similarity with surrounding tissue. As a result, the segmentation model is easily confused by complex and changeable background information, leading to inaccurate positioning of small targets and false positives and false negatives. Therefore, we design a cascaded pancreatic tumor segmentation algorithm. In the first stage, we use a general multi-scale U-Net to segment the pancreas, and we exploit a multi-scale segmentation network based on non-local localization and focusing modules to segment pancreatic tumors in the second stage. The non-local localization module learns channel and spatial position information, searches for the approximate area where the pancreatic tumor is located from a global perspective, and obtains the initial segmentation results. The focusing module conducts context exploration based on foreground features (or background features), detects and removes false positive (or false negative) interference, and obtains more accurate segmentation results based on the initial segmentation. In addition, we design a new loss function to alleviate the insensitivity to small targets. Experimental results show that the proposed algorithm can more accurately locate pancreatic tumors of different sizes, and the Dice coefficient outperforms the existing state-of-the-art segmentation model. The code will be available at https://github.com/HeyJGJu/Pancreatic-Tumor-SEG.

A lightweight dual-attention network for tomato leaf disease identification.

Tomato disease image recognition plays a crucial role in agricultural production. Today, while machine vision methods based on deep learning have achieved some success in disease recognition, they still face several challenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanism called DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust to noisy labels, aimed at reducing the impact of noisy labels on the LDAMNet model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure andcomputational efficiency, and validate its application effects in more practical scenarios.

Energy-efficient maximization algorithm for energy harvesting under imperfect channel information

In this paper, we propose a novel energy-efficient resource optimization scheme in non-orthogonal multiple access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT). In this system, we consider practical imperfect channel information that accounts for random channel delays and channel estimation errors. Additionally, the small cell users (SCUs) in a heterogeneous network (HetNet) can harvest energy from the small base station (SBS) signal by energy harvesting. Based on the non-linear energy harvesting (NEH) model, the problem of maximizing energy efficiency with imperfect channel state information (CSI) is formulated. Since the formulated problem is a probabilistic mixed non-convex optimization problem, a two-stage algorithm is developed to jointly optimize sub-channel matching and power allocation. In particular, the problem is first transformed into a non-probabilistic problem through the relaxation method. For the sub-channels matching problem, a low-complexity many-to-many matching algorithm is designed to achieve dynamic matching based on the preference lists. For the power allocation problem, the closed-form transmission power of SCUs can be derived by the Dinkelbach method and Lagrangian dual approach. Simulation results show that the proposed scheme can achieve higher energy efficiency than existing linear energy harvesting (LEH)-NOMA and NEH-OFDMA schemes, with improvements of 37.99% and 84.69%, respectively.

Multi‐Concentric Generalized Perfect Vortex Beams by Hybrid Phase Metasurfaces

AbstractMetasurface‐based vortex beams are expected to overcome the current communication challenges of massive data and limited bandwidth by their theoretical infinite and mutually orthogonal states. However, limited by the spatial extension of array vortices, only finite orbital angular momentum (OAM) eigenstates are available, of which the expansion will boost the bandwidth toward an inexhaustible stage. In this manuscript, multi‐concentric generalized perfect vortex beams (G‐PVBs) are experimentally generated by the fabricated hybrid phase metasurfaces integrating generalized focusing phases and circular domain function into vortex phases. Diverse vortex shapes and helical lobes within beam patterns and spiral interferograms are both arranged in a non‐overlapping concentric manner instead of spatial arrays, respectively, breaking the limitation of symmetric shape while avoiding inter‐modal cross‐talk. The vortex shape, topological charge and vortex number are decoupled mutually in orthogonal polarization states, realizing both space and polarization multiplexing. Furthermore, shaped into anomalous profiles with asymmetric states of polarization, multi‐concentric vector G‐PVBs are experimentally generated to transmit parallelly stable order information with high robustness in turbulence environments. The proposed multi‐concentric G‐PVBs simultaneously expanded the novel vortex dimension by anomalous shapes and information channels of multi‐concentric OAM modes, providing potential application in high‐capacity vortex optical communication and information encryption.

Vicarious vs experiential information in the consumer’s construal level on imported food safety risk perception

Consumers acquire information about the imported food safety risks through distal channels such as the Internet versus proximal channels such as direct experience, and these channels influence the consumer perception of safety. However, the empirical analysis of the sources of information (distal–proximal) channels is second to none. This article explores whether direct experience with imported food induces different levels of safety risks compared to distal information through social media. Based on the Construal Level Theory (CLT), the study examines consumer responses in a survey involving 500 respondents out of 1000 distributed in South Korea, using high-level construal (vicarious information) versus a low-level construal (direct experience) framework to assess food safety risk perception. We narrowed the imported food from China to South Korea to obtain a robust analysis. Our analysis shows that vicarious (distal) sources of information increase perceived food safety risks in the minds of the consumer, while direct experience (proximity) decreases food safety risks. In a supplementary analysis, those respondents who had visited China exhibited lower levels of food safety risk of the imported food. We infer that the lower-level construal (experience) mediates the effects of higher-level construal (indirect, social media) on the imported food safety risk. However, the correlation is nonlinear, explaining why some studies find proximity while others find distance in correlation with the food safety risk. In a contribution to the literature, this study answers a longstanding question regarding international business in food supplies, supports the CLT framework, and offers a policy recommendation for both Korea and China to enhance their information channels to counteract misinformation.