A study on the adoption intention of cold chain prepared Dishes based on consumer orientation mentality

Background:  Cold chain prepared dishes, as a new type of food with low temperature and dual attributes, have attracted more and more attention from consumers. From the perspective of consumers' brand positioning mentality, this study integrated the brand, quality and other attributes of cold chain prepared vegetables, and studied the positioning mentality and adoption behavior of Chinese consumers   In this study, three cold-chain prefabricated dishes, "pickled cabbage fish", "tomato Braised beef brisket" and "Huangpi fishball", were selected as investigation objects. Contribution:  The contribution of this study is that different food value attributes explain the law of consumers' willingness to adopt cold chain prepared dishes through brand positioning mentality, and find out how different degrees of vulnerability and quality warranty Method:  Based on ZMET survey method, product effect data were used to analyze and verify structural equation model. Results:  Different value evaluation directly affects consumers' brand positioning mentality, brand positioning mind as an intermediate variable has a significant correlation with adoption intention. The degree of vulnerability moderates the relationship between value evaluation and brand positioning mentality, and the degree of warranty period regulates the relationship between quality value, brand positioning mentality and adoption intention respectively. Conclusion:  It also provides new marketing tools and theories for entrepreneurs and marketers in the cold chain food industry, which contributes to the promotion and diffusion of cold chain prepared food for consumer

A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness

Prevalence survey on pterygium among people aged 40 and above in Hengli Town of Dongguan

AIM:To investigate the prevalence of pterygium of the household population aged 40 and above in Hengli Town of Dongguan.<p>METHODS: Using the method of cluster random sampling, select 3 628 people aged 40 and above in four villages and one community for visual examination, intraocular pressure check, slit lamp examination and questionnaire.<p>RESULTS: The actual number of subjects was 3 393 people, and examination rate was 93.52%. We detected 843 patients with pterygium. The prevalence of pterygium was 24.85%.<p>CONCLUSION: There is high prevalence of pterygium in Dongguan area. The prevalence of pterygium is related with age and working environment, but has no relation with gender

Novel phenanthrene-degrading bacteria identified by DNA-stable isotope probing

Microorganisms responsible for the degradation of phenanthrene in a clean forest soil sample were identified by DNA-based stable isotope probing (SIP). The soil was artificially amended with either 12C- or 13C-labeled phenanthrene, and soil DNA was extracted on days 3, 6 and 9. Terminal restriction fragment length polymorphism (TRFLP) results revealed that the fragments of 219- And 241-bp in HaeIII digests were distributed throughout the gradient profile at three different sampling time points, and both fragments were more dominant in the heavy fractions of the samples exposed to the 13C-labeled contaminant. 16S rRNA sequencing of the 13C-enriched fraction suggested that Acidobacterium spp. within the class Acidobacteria, and Collimonas spp. within the class Betaproteobacteria, were directly involved in the uptake and degradation of phenanthrene at different times. To our knowledge, this is the first report that the genus Collimonas has the ability to degrade PAHs. Two PAH-RHDα genes were identified in 13C-labeled DNA. However, isolation of pure cultures indicated that strains of Staphylococcus sp. PHE-3, Pseudomonas sp. PHE- 1, and Pseudomonas sp. PHE-2 in the soil had high phenanthrene-degrading ability. This emphasizes the role of a culture-independent method in the functional understanding of microbial communities in situ

EC^2: Emergent Communication for Embodied Control

Embodied control requires agents to leverage multi-modal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC^2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC^2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.Comment: Published in CVPR202

Multiple-relaxation-time discrete Boltzmann modeling of multicomponent mixture with nonequilibrium effects

A multiple-relaxation-time discrete Boltzmann model (DBM) is proposed for multicomponent mixtures, where compressible, hydrodynamic, and thermodynamic nonequilibrium effects are taken into account. It allows the specific heat ratio and the Prandtl number to be adjustable, and is suitable for both low and high speed fluid flows. From the physical side, besides being consistent with the multicomponent Navier-Stokes equations, Fick's law, and Stefan-Maxwell diffusion equation in the hydrodynamic limit, the DBM provides more kinetic information about the nonequilibrium effects. The physical capability of DBM to describe the nonequilibrium flows, beyond the Navier-Stokes representation, enables the study of the entropy production mechanism in complex flows, especially in multicomponent mixtures. Moreover, the current kinetic model is employed to investigate nonequilibrium behaviors of the compressible Kelvin-Helmholtz instability (KHI). The entropy of mixing, the mixing area, the mixing width, the kinetic and internal energies, and the maximum and minimum temperatures are investigated during the dynamic KHI process. It is found that the mixing degree and fluid flow are similar in the KHI process for cases with various thermal conductivity and initial temperature configurations, while the maximum and minimum temperatures show different trends in cases with or without initial temperature gradients. Physically, both heat conduction and temperature exert slight influences on the formation and evolution of the KHI morphological structure

Numerical investigation into the thermal interference of slinky ground heat exchangers

The thermal performance of a slinky ground heat exchanger has been investigated using a validated transient 3D model for different trench separations, installation depths, soil properties, and daily operation hours. The effect of trench separation on thermal interference was analysed. The centre-to-centre distances between parallel trenches range from 1.5 m to 11 m. The initial soil temperature was found to have a significant effect on the predicted thermal performance. The predicted heat extraction using a varying initial soil temperature for a heating season would decrease with the increasing depth of installation, whereas using a uniform initial temperature would lead to increasing heat extraction with installation depth. It has also been found that soil with a high thermal conductivity would exacerbate the thermal interference between trenches with a small separation and that the thermal interference in continuous operation would be more than that in intermittent operation as a result of heat depletion in the ground. The effect of installation depth depended on the initial soil temperature for the first three months of continuous operation, but the effect decreased with operating time

Deep Generative Fixed-filter Active Noise Control

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.Comment: Accepted by ICASSP 2023. Code will be available after publicatio

Identification of Benzo[a]pyrene-metabolizing bacteria in forest soils by using DNA-based stable-isotope probing

DNA-based stable-isotope probing (DNA-SIP) was used in this study to investigate the uncultivated bacteria with benzo[a]pyrene (BaP) metabolism capacities in two Chinese forest soils (Mt. Maoer in Heilongjiang Province and Mt. Baicaowa in Hubei Province). We characterized three different phylotypes with responsibility for BaP degradation, none of which were previously reported as BaP-degrading microorganisms by SIP. In Mt. Maoer soil microcosms, the putative BaP degraders were classified as belonging to the genus Terrimonas (family Chitinophagaceae, order Sphingobacteriales), whereas Burkholderia spp. were the key BaP degraders in Mt. Baicaowa soils. The addition of metabolic salicylate significantly increased BaP degradation efficiency in Mt. Maoer soils, and the BaP-metabolizing bacteria shifted to the microorganisms in the family Oxalobacteraceae (genus unclassified). Meanwhile, salicylate addition did not change either BaP degradation or putative BaP degraders in Mt. Baicaowa. Polycyclic aromatic hydrocarbon ring-hydroxylating dioxygenase (PAH-RHD) genes were amplified, sequenced, and quantified in the DNA-SIP (13)C heavy fraction to further confirm the BaP metabolism. By illuminating the microbial diversity and salicylate additive effects on BaP degradation across different soils, the results increased our understanding of BaP natural attenuation and provided a possible approach to enhance the bioremediation of BaP-contaminated soils