Nondestructive testing of runny salted egg yolk based on improved ConvNeXt-T.

Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt-T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location-aware circular convolution (ParC) was introduced, utilizing a ParC-block to replace a portion of the original ConvNeXt-T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC-ConvNeXt-SMU-T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks. PRACTICAL APPLICATION: This study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities.

Non-destructive detection of egg white and yolk morphology transformation and salt content of salted duck eggs in salting by hyperspectral imaging.

Salt content is a crucial indicator of the maturity and internal quality of salted duck eggs (SDEs) during the pickling process. However, there is currently no valid and rapid method available for accurately detecting salt content. In the present study, we utilized hyperspectral imaging to no-destructively determine the salt content in egg yolks, egg whites, and whole eggs during the curing period. Firstly, principal component analysis was applied to explain the characteristics of egg yolk and white morphology transformation of SDEs with different maturities during curing. Secondly, sensitive spectral factors representative of changes in the salt content of SDEs were extracted by three spectral transformations (Savitzky-Golay SG, continuum removal CR, and first-order derivation FD) and three approaches of selecting characteristic wavelengths (successive projection algorithm SPA, uninformative variables elimination UVE and competitive adaptive reweighting sampling algorithm CARS). The results of the PLSR model suggested that the optimal models for predicting salt content in egg yolks, whites, and whole eggs were SG-UVE-PLSR (predicted coefficient of determination Rp2=0.912, predicted standard deviation SEp=0.151, residual prediction deviation RPD = 3.371), CR-CARS-PLSR (Rp2=0.873, SEp=0.862, RPD = 2.806), and CR-UVE-PLSR (Rp2=0.877, SEp=0.680, RPD = 2.851), respectively. Eventually, the optimal prediction model for the salt content of the whole egg was employed to a pixel spectral matrix to calculate the salt content values of pixel points on the hyperspectral image of SDEs. Additionally, pseudo-color techniques were employed to visualize the spatial distribution of predicted salt content. This work will provide a theoretical foundation for rapidly detecting maturity and enabling high-throughput quality sorting of SDEs.

Effect of a moderate-intensity comprehensive exercise program on body composition, muscle strength, and physical performance in elderly females with sarcopenia.

Objective: This study aimed at examining an eight-week moderate-intensity comprehensive exercise training program on the parameters of sarcopenia in elderly females. Methods: A total of 49 community-dwelling elderly females with sarcopenia (65.5 ± 2.5) were assigned randomly to an experiment group (EG, n = 25) and a control group (CG, n = 24). In the EG, an eight-week comprehensive exercise training program was implemented, in 1 h, 3 times per week, a total of 24 sessions. The CG only received health public education per two weeks, a total of 4 times. Subsequently, the differences between the two groups were tested through two-way repeated ANOVA. Results: ASM, SMM, and SMI in the EG were significantly improved by 0.26 kg, 0.18 kg, and 0.10 kg/m2, respectively. Group-by-time interactions were significantly different on the ASM [F (1,47) = 6.25, η2 = 0.12] and SMI [F (1,47) = 6.77, η2 = 0.13]. Muscle strength was improved 0.8 kg in the EG. Significant group-by-time interaction differences were reported in the handgrip strength [F (1,47) = 6.8, η2 = 0.13] after the eight-week intervention. Compared with the baseline, gait speed was improved a 0.05 m/s and 5-time chair stand was decreased a 0.27 s in the EG. Group-by-time interactions were significantly different in 5-time chair stand [F (1, 47) = 6.35, η2 = 0.12]. Conclusions: The moderate-intensity comprehensive exercise was confirmed as a safe and convenient exercise program. Although a load of training intensity is not sufficient to improve the gait speed, this exercise protocol is promising in delaying overall results in community-dwelling sarcopenia elderly females and contributes to the improvement of muscle mass, handgrip strength, and 5TCS.

Selective serotonin reuptake inhibitors combined with traditional Chinese medicine for premature ejaculation: A systematic review and meta-analysis.

BACKGROUND: Premature ejaculation (PE) is still a tough problem in drug treatment. Many clinical trials have proven that traditional Chinese medicine (TCM) has a significant effect in the treatment of PE. This article aims to provide the latest evidence for the efficacy and safety of TCM combined with selective serotonin reuptake inhibitors (SSRIs) in the treatment of PE. METHODS: We looked for randomized controlled trials (RCTs) from China National Knowledge Infrastructure, Wanfang, VIP Database, MEDLINE, PubMed, Web of Science, EMBASE, and Cochrane Library until June 30, 2022. STATA 15.1 software was used to analyze all data for this article. The quality of the included articles was evaluated using the Cochrane Reviewer's Handbook 5.3. RESULTS: Finally, we selected 16 high-quality RCTs in our meta-analysis, which containing 889 patients. Meta-analysis suggested that, compared with SSRIs alone, combination of TCM with SSRIs increased significantly intravaginal ejaculation latencv time and the scores of ejaculation control ability, sexual life satisfaction, PE-related distress, and communication difficulties between partners related to PE. Also, there was no significant difference in adverse effects between the two groups. In addition, the results of publication bias test showed that no significant bias occurred. CONCLUSION: The combined use of TCM and SSRIs has significant effect in the treatment of PE compared with SSRIs monotherapy and was generally well tolerated. Due to the small sample size, multicenter and large sample RCT is still needed in the future to further confirm the effectiveness and safety of TCM combined with SSRIs in the treatment of PE.

PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories.

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis.

ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses.

The research on massive open online courses (MOOCs) data analytics has mushroomed recently because of the rapid development of MOOCs. The MOOC data not only contains learner profiles and learning outcomes, but also sequential information about when and which type of learning activities each learner performs, such as reviewing a lecture video before undertaking an assignment. Learning sequence analytics could help understand the correlations between learning sequences and performances, which further characterize different learner groups. However, few works have explored the sequence of learning activities, which have mostly been considered aggregated events. A visual analytics system called ViSeq is introduced to resolve the loss of sequential information, to visualize the learning sequence of different learner groups, and to help better understand the reasons behind the learning behaviors. The system facilitates users in exploring learning sequences from multiple levels of granularity. ViSeq incorporates four linked views: the projection view to identify learner groups, the pattern view to exhibit overall sequential patterns within a selected group, the sequence view to illustrate the transitions between consecutive events, and the individual view with an augmented sequence chain to compare selected personal learning sequences. Case studies and expert interviews were conducted to evaluate the system.

Salt-tolerant Staphylococcus bacteria induce structural and nutritional alterations of salted duck egg white.

Salted duck egg white, a major by-product of salted egg yolk production, is rich in nutrients. However, its high salinity limits its application in the food industry. In the present study, three haloduric bacterium strains (C1, C2, and C3) were isolated from Jinhua ham, and strain C1 exhibited higher ratio of the transparent circle diameter to the colony diameter (HC) and gelatin liquefaction. Strain C1 was further identified as a member of the genus Staphylococcus through gene sequencing and EzTaxon-e analyses. Salted duck egg white was fermented by strain C1, and the thermal stability, microstructure, amino acid composition, and γ-aminobutyric acid of the egg white were compared with egg white without fermentation. The fermented salted duck egg white had a significantly low salinity. Meanwhile, it increased its thermal stability compared with the control through losing an endotherm at around 85°C and forming a new endotherm peak starting at 91.8°C. Additionally, free amino acids and γ-aminobutyric acid were found only in the fermented salted duck egg white. These indicated that fermentation with salt-resistant strains could alter the structure of salted duck egg white and improve its nutritional quality.

Sequence Synopsis: Optimize Visual Summary of Temporal Event Data.

Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.

PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses.

Massive open online courses (MOOCs) aim to facilitate open-access and massive-participation education. These courses have attracted millions of learners recently. At present, most MOOC platforms record the web log data of learner interactions with course videos. Such large amounts of multivariate data pose a new challenge in terms of analyzing online learning behaviors. Previous studies have mainly focused on the aggregate behaviors of learners from a summative view; however, few attempts have been made to conduct a detailed analysis of such behaviors. To determine complex learning patterns in MOOC video interactions, this paper introduces a comprehensive visualization system called PeakVizor. This system enables course instructors and education experts to analyze the "peaks" or the video segments that generate numerous clickstreams. The system features three views at different levels: the overview with glyphs to display valuable statistics regarding the peaks detected; the flow view to present spatio-temporal information regarding the peaks; and the correlation view to show the correlation between different learner groups and the peaks. Case studies and interviews conducted with domain experts have demonstrated the usefulness and effectiveness of PeakVizor, and new findings about learning behaviors in MOOC platforms have been reported.