Search Results (580)

Water quality early warning is a key aspect in industrial recirculating aquaculture systems for high-density shrimp farming. The concentrations of ammonia nitrogen and nitrite in the water significantly impact the cultured animals and are challenging to measure in real-time, posing a substantial challenge to water quality early warning technology. This study aims to collect data samples using low-cost water quality sensors during the industrial recirculating aquaculture process and to construct predictive values for ammonia nitrogen and nitrite, which are difficult to obtain through sensors in the aquaculture environment, using data prediction techniques. This study employs various machine learning algorithms, including General Regression Neural Network (GRNN), Deep Belief Network (DBN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM), to build predictive models for ammonia nitrogen and nitrite. The accuracy of the models is determined by comparing the predicted values with the actual values, and the performance of the models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. Ultimately, the optimized GRNN-based predictive model for ammonia nitrogen concentration (MAE = 0.5915, MAPE = 28.95%, RMSE = 0.7765) and the nitrite concentration predictive model (MAE = 0.1191, MAPE = 29.65%, RMSE = 0.1904) were selected. The models can be integrated into an Internet of Things system to analyze the changes in ammonia nitrogen and nitrite concentrations over time through aquaculture management and routine water quality conditions, thereby achieving the application of recirculating aquaculture system water environment early warning technology. Full article

The occurrence and spread of Verticillium dahliae (V. dahliae) in cotton depends on the combined effects of pathogens, host plants, and the environment, among which temperature is one of the most important environmental factors. Studying how temperature impacts the occurrence of V. dahliae in cotton and the mechanisms governing host defense responses is crucial for disease prevention and control. Understanding the dual effects of temperature on both pathogens and hosts can provide valuable insights for developing effective strategies to manage this destructive fungal infection in cotton. This study was based on the deciduous V. dahliae Vd-3. Through cultivation at different temperatures, Vd-3 formed the most microsclerotia and had the largest colony diameter at 25 °C. Endospore toxins were extracted, and 48 h was determined to be the best pathogenic time point for endotoxins to infect cotton leaves through a chlorophyll fluorescence imaging system and phenotypic evaluation. Transcriptome sequencing was performed on cotton leaves infected with Vd-3 endotoxins for 48 h at different culture temperatures. A total of 34,955 differentially expressed genes (DEGs) were identified between each temperature and CK (no pathogen inoculation), including 17,422 common DEGs. The results of the enrichment analysis revealed that all the DEGs were involved mainly in photosynthesis and sugar metabolism. Among the 34,955 DEGs, genes in the biosynthesis and signal transduction pathways of jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) were identified, and their expression patterns were determined. A total of 5652 unique DEGs were clustered into six clusters using the k-means clustering algorithm, and the functions and main transcription factors (TFs) of each cluster were subsequently annotated. In addition, we constructed a gene regulatory network via weighted correlation network analysis (WGCNA) and identified twelve key genes related to cotton defense against V. dahliae at different temperatures, including four genes encoding transcription factors. These findings provide a theoretical foundation for investigating temperature regulation in V. dahliae infecting cotton and introduce novel genetic resources for enhancing resistance to this disease in cotton plants. Full article

The increasing volume of motorized traffic not only negatively impacts the structural preservation and overall planning of individual buildings within the block but also disrupts the originally harmonious and pleasant spatial environment of the area. Walking, as a primary mode of urban transportation, plays a crucial role in preserving the unique characteristics of historical blocks, enhancing the quality of the urban environment, and achieving long-term sustainable urban development. This study takes the Five Avenues historical block as a case, assessing the current walking environment from the perspective of Post-Occupancy Evaluation (POE). Machine learning techniques (including web scraping, the TF-IDF algorithm, and the LDA model) were employed to collect and analyze user feedback data, assisting in constructing walking environment satisfaction indicators. A total of 19 key factors affecting walking satisfaction were identified. Paired sample t-tests, ANOVA, and reliability and validity analyses were applied to examine the feasibility and practicality of the questionnaire content. Finally, using Importance–Performance Analysis (IPA), the improvement priorities for walking environment indicators were clearly defined. Although the overall satisfaction index of the Five Avenues is comparatively high, unobstructed pathways have the greatest impact on walking environment satisfaction, followed by the rationality of guiding signage facilities, and then by public security management and facility maintenance. Furthermore, visitors prioritize factors such as the cultural recognizability of the area, travel convenience, green space accessibility, and the sidewalk width proportion; they are less focused on the functional aspects of the walkways. Based on the analysis results from POE and machine learning, targeted strategies for improving the walking environment in historical blocks were proposed, aiming to provide a more comprehensive basis for improving the walking environments of similar blocks. Full article

The red revolutionary heritage is a valuable part of China’s historical and cultural legacy, with the potential to generate economic benefits through its thoughtful development. However, challenges such as insufficient understanding, lack of comprehensive planning and layout, and limited protection and utilization methods hinder the full realization of the political, cultural, and economic value of red heritage. To address these problems, this paper thoroughly examines the current state of red revolutionary heritage protection and identifies the problems within the preservation process. Moreover, it proposes leveraging advanced artificial intelligence (AI) technology to repair some damaged image data. Specifically, this paper introduces a red revolutionary cultural relic image-restoration model based on a generative adversarial network (GAN). This model was trained using samples of damaged image and utilizes high-quality models to restore these images effectively. The study also integrates real-world revolutionary heritage images for practical application and assesses its effectiveness through questionnaire surveys. The survey results show that AI algorithms and image-processing technologies hold significant potential in the protection of revolutionary heritage. Full article

This purpose of this research is to understand the role of networked narratives in social media in modulating viewer prejudice toward ethnic neighborhoods. We designed experimental videos on YouTube based on intergroup contact theory and narrative frameworks aimed at (1) gaining knowledge, (2) reducing anxiety, and (3) fostering empathy. Despite consistent storytelling across the videos, we observed significant variations in viewer emotions, especially in replies to comments. We hypothesized that these discrepancies could be explained by the influence of the surrounding digital network on the narrative’s reception. Two-stage research was conducted to understand this phenomenon. First, automated emotion analysis on user comments was conducted to identify the varying emotions. Then, we explored contextual factors surrounding each video on YouTube, focusing on algorithmic curation inferred from traffic sources, region, and search keywords. Findings revealed that negative algorithmic curation and user interactivity result in overall negative viewer emotion, largely driven by video placement and recommendations. However, videos with higher traffic originating from viewers who had watched the storyteller’s other videos result in more positive sentiments and longer visits. This suggests that consistent exposure within the channel can foster more positive acceptance of cultural outgroups by building trust and reducing anxiety. There is the need, then, for storytellers to curate discussions to mitigate prejudice in digital contexts. Full article

The effectiveness of archives, particularly those related to cultural heritage, depends on their accessibility and navigability. An intuitive interface is essential for improving accessibility and inclusivity, enabling users with diverse backgrounds and expertise to interact with archival content effortlessly. This paper introduces a new method for visualizing and navigating dataset information through the creation of semantic graphs. By leveraging pre-trained large language models, this approach groups data and generates semantic graphs. The development of multi-layer maps facilitates deep exploration of datasets, and the capability to handle multilingual datasets makes it ideal for archives containing documents in various languages. These features combine to create a user-friendly tool adaptable to various contexts, offering even non-expert users a new way to interact with and navigate the data. This enhances their overall experience, promoting a greater understanding and appreciation of the content. The paper presents experiments conducted on diverse datasets across different languages and topics employing various algorithms and methods. It provides a thorough discussion of the results obtained from these experiments. Full article

Although it is well established that thermal environments significantly influence travel behavior, the synergistic effects of points of interest (POI) and thermal environments on behavior remain unclear. This study developed a vision-based outdoor evaluation model aimed at uncovering the driving factors behind human behavior in outdoor spaces. First, Yolo v5 and questionnaires were employed to obtain crowd activity intensity and preference levels. Subsequently, target detection and clustering algorithms were used to derive variables such as POI attractiveness and POI distance, while a validated environmental simulator was utilized to simulate outdoor thermal comfort distributions across different times. Finally, multiple classification models were compared to establish the mapping relationships between POI, thermal environment variables, and crowd preferences, with SHAP analysis used to examine the contribution of each variable. The results indicate that XGBoost achieved the best predictive performance (accuracy = 0.95), with shadow proportion (|SHAP| = 0.24) and POI distance (|SHAP| = 0.12) identified as the most significant factors influencing crowd preferences. By extrapolation, this classification model can provide valuable insights for optimizing community environments and enhancing vitality in areas with similar climatic and cultural contexts. Full article

Fibroblasts are cells that reside within the fibrous or loose connective tissues of most mammalian organs. For research purposes, fibroblasts are often subjected to long-term culture under defined conditions, during which their properties can significantly change. It is essential to understand and document these changes to obtain reliable outcomes. For the quantification of specific gene expressions, the most reliable and widely used technique is quantitative real-time polymerase chain reaction (qRT-PCR). Here, we assessed the impact of a reference gene’s stability on a qRT-PCR analysis of long-term cultured canine skin fibroblasts. After successfully isolating the fibroblasts from canine skin tissues, they were cultured and evaluated for proliferation and β-galactosidase activity at different passage numbers. With extended culture, the fibroblasts showed a long doubling time and elevated β-galactosidase activity. Using three widely used algorithms, geNorm, Normfinder, and Bestkeeper, we identified HPRT1, YWHAZ, and GUSB as the most stable reference genes for both early- and late-passage fibroblasts. Conventional reference genes such as GAPDH were found to be less stable than those genes. The normalization of Vimentin by the stable genes showed statistical differences, whereas normalization by an unstable gene did not. Collectively, this study indicates that using stable reference genes is essential for accurately and reliably measuring gene expression in both early- and late-passage fibroblasts. These findings provide valuable insights into internal controls for gene expression studies and are expected to be utilized for analyzing gene expression patterns in molecular biology research. Full article

Background/Objective: Nutritionists play a crucial role in guiding individuals toward healthier lifestyles through personalized meal planning; however, this task involves navigating a complex web of factors, including health conditions, dietary restrictions, cultural preferences, and socioeconomic constraints. The Analytic Hierarchy Process (AHP) offers a valuable framework for structuring these multi-faceted decisions but inconsistencies can hinder its effectiveness in pairwise comparisons. Methods: This paper proposes a novel hybrid Particle Swarm Optimization–Simulated Annealing (PSO-SA) algorithm to refine inconsistent AHP weight matrices, ensuring a consistent and accurate representation of the nutritionist’s expertise and client preferences. Our approach merges PSO’s global search capabilities with SA’s local search precision, striking an optimal balance between exploration and exploitation. Results: We demonstrate the practical utility of our algorithm through real-world use cases involving personalized meal planning for individuals with specific dietary needs and preferences. Results showcase the algorithm’s efficiency in achieving consistency and surpassing standard PSO accuracy. Conclusion: By integrating the PSO-SA algorithm into a mobile app, we empower nutritionists with an advanced decision-making tool for creating tailored meal plans that promote healthier dietary choices and improved client outcomes. This research represents a significant advancement in multi-criteria decision-making for nutrition, offering a robust solution to the inconsistency challenge in AHP and paving the way for more effective and personalized dietary interventions. Full article

Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs (${\tau }_2$-Precipitation and ${\tau }_2$-Discharge: R² = 0.675, p-values < 0.001; ${\tau }_3$-Precipitation and ${\tau }_3$-Discharge: R² = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming $\alpha$ = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future. Full article

This study presents an innovative approach to sustainable timber construction by integrating traditional woodworking techniques with advanced robotic technology. The research focuses on three key objectives: preserving traditional craftsmanship, enhancing material conservation, and improving production efficiency. A meta-model-based framework is developed to capture the woodcrafts of mortise and tenon joints, which are prevalent in traditional Chinese wooden architecture. The study employs parametric design and robotic arm technology to digitize and automate the production process, resulting in significant improvements in material utilization and processing efficiency. Specifically, this study utilizes genetic algorithm strategies to resolve the problem of complex mortise and tenon craftsmanship optimization for robotic arms. Compared to conventional CNC machining, the proposed method demonstrates superior performance in path optimization, reduced material waste, and faster production times. The research contributes to the field of sustainable architecture by offering a novel solution that balances the preservation of cultural heritage with modern construction demands. This approach not only ensures the continuity of traditional woodworking skills but also addresses contemporary challenges in sustainable building practices, paving the way for more environmentally friendly and efficient timber construction methods. Full article

Post-translational modification of proteins plays an important role in cancer cell biology. Proteins encoded by oncogenes may be activated by phosphorylation, products of tumour suppressors might be inactivated by phosphorylation or ubiquitinylation, which marks them for degradation; chromatin-binding proteins are often methylated and/or acetylated. These are just a few of the many hundreds of post-translational modifications discovered by years of painstaking experimentation and the chemical analysis of purified proteins. In recent years, mass spectrometry-based proteomics emerged as the principal technique for identifying such modifications in samples from cultured cells and tumour tissue. Here, we used a recently developed combinatorial search algorithm implemented in the MGVB toolset to identify novel modifications in samples from breast cancer cell lines. Our results provide a rich resource of coupled protein abundance and post-translational modification data seen in the context of an important biological function—the response of cells to interferon gamma treatment—which can serve as a starting point for future investigations to validate promising modifications and explore the utility of the underlying molecular mechanisms as potential diagnostic or therapeutic targets. Full article

Numerous nutrient profiling models (NPMs) exist worldwide, but Japan lacks an official NPM. Using the Australian and New Zealand Health Star Rating (HSR) as a reference, “Processed Foods in Japan version 1.0” (NPM-PFJ (1.0)) was developed to fit Japanese food culture and policies. In total, 668 processed foods from the Standard Tables of Food Composition in Japan were analyzed, excluding seasonings/spices, fats/oils, alcoholic beverages, and infant food. The NPM-PFJ (1.0) scoring algorithm was adapted from HSR, with revised reference values for energy, saturated fat, total sugars, sodium, protein, and dietary fiber in alignment with Japanese standards. Reference values for fruits, vegetables, nuts, and legumes (fvnl) remained unchanged. Median scores were 4.5 for HSR and 5.0 for NPM-PFJ (1.0), showing high correlation (r = 0.939, p < 0.01). Thereafter, food categories familiar and meaningful in Japan were defined based on a hierarchical cluster analysis of scoring patterns, creating six categories with distinct characteristics. Finally, the rating algorithm for NPM-PFJ (1.0) was created using each group’s score distribution (10th percentile). The NPM-PFJ (1.0) was developed through a fully transparent and evidence-based process and is expected to facilitate the reformulation of food products by food industries and help consumers easily access healthier processed foods. This model marks a significant step forward in developing an NPM tailored to Japanese food culture and health policies, with the potential to enhance public health. Full article

To address the rising incidence of non-communicable diseases (NCDs) and promote healthier eating habits, Japan requires a culturally tailored Nutrient Profile Model. This study aimed to develop a Nutrient Profile Model for Dishes in Japan version 1.0 (NPM-DJ (1.0)) that corresponds to the nutritional issues and food culture in Japan. The aim of the NPM-DJ (1.0) was to promote the health of the general population, and to prevent the increase in NCDs in Japan. The NPM-DJ (1.0) categorizes dishes into staples, sides, mains, mixed dishes, and mixed dishes with staples. The model evaluates dishes based on energy, saturated fats, sugars, and sodium as restricted nutrients, while considering protein, dietary fiber, and the weight of certain food groups as recommended nutrients. The distribution of the overall score for each dish category was analyzed and a rating algorithm was created. The baseline, modification points, and final scores were significantly lower for side dishes than for staple dishes. In contrast, the baseline points and final scores were significantly higher for mixed dishes with staple. The model effectively differentiated nutritional profiles across five dishes categories, which may promote healthier dish reformulation by food businesses operators and encourage consumers to select healthier dishes. Full article

To achieve the color matching rules for the textiles discovered during Silk Road excavations between the 4th and 8th centuries, this research proposed an image-based matching network modeling method. The Silk Road facilitated trade and cultural exchange between the East and West, and the textiles found along the way depict the development of fabrics in a color scheme with great cultural significance. A total of 165 images with brocade patterns were collected from a book with a detailed description of the Western influences on textiles along the Silk Road. Two different clustering methods, including the K-means clustering method and octree quantization approach, were used to extract the primary and secondary colors. By combining the HSV color space with the PCCS color system, the color distribution was analyzed to discover the features of representative color patterns. The co-occurrence relationship of the auxiliary colors was explored using the Apriori algorithm, and a total of eight association rules were established. The results showed that the K-means clustering algorithm can show a better effect of color classification to obtain three primary colors and nine secondary colors. The matching mechanism with a visualized network model was also proposed, which showed that reddish-yellow tones are the main colors in the brocade patterns, and the light and soft tones separately account for 27% and 20%. Beige and brown are the most common colorways, with a confidence level of 47%. One style of brocade pattern was used to demonstrate different appearances within various color networks, which could be applied to 3D virtual fitting. This image-based matching network modeling approach makes the color matching schemes visible, and can assist fashion design with fabric features influenced by historical and cultural development. Full article