Search Results (8,185)

With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. However, existing models often overlook the spatial deflection correlations among monitoring points. Therefore, this study proposes a novel deep learning framework, CGCA (Convolutional Gated Recurrent Unit with Cross-Attention), which integrates ConvGRU and cross-attention mechanisms. The model achieves spatio-temporal feature extraction and deformation prediction via an encoder–decoder architecture. Specifically, the convolutional structure captures spatial dependencies between monitoring points, while the recurrent unit extracts time-series characteristics of deformation. A cross-attention mechanism is introduced to dynamically weight the interactions between spatial and temporal data. Additionally, the model incorporates multi-dimensional inputs, including full-depth inclinometer measurements, construction parameters, and tube burial depths. The optimization strategy combines AdamW and Lookahead to enhance training stability and generalization capability in geotechnical engineering scenarios. Case studies of foundation pit engineering demonstrate that the CGCA model exhibits superior performance and robust generalization capabilities. When validated against standard section (CX1) and complex working condition (CX2) datasets involving adjacent bridge structures, the model achieves determination coefficients (R²) of 0.996 and 0.994, respectively. The root mean square error (RMSE) remains below 0.44 mm, while the mean absolute error (MAE) is less than 0.36 mm. Comparative experiments confirm the effectiveness of the proposed model architecture and the optimization strategy. This framework offers an efficient and reliable technical solution for deformation early warning and dynamic decision-making in foundation pit engineering. Full article

Cordycepin, a bioactive adenosine analog, holds promise in pharmaceutical and health product development. However, large-scale production remains constrained by the limitations of natural producers, Cordyceps spp. Herein, we report the reconstruction of the first genome-scale metabolic model (GSMM) for a cordycepin-producing strain of recombinant Aspergillus oryzae. The model, iNR1684, incorporated 1684 genes and 1947 reactions with 93% gene-protein-reaction coverage, which was validated by the experimental biomass composition and growth rate. In silico analyses identified key gene amplification targets in the pentose phosphate and one-carbon metabolism pathways, indicating that folate metabolism is crucial for enhancing cordycepin production. Nutrient optimization simulations revealed that chitosan, D-glucosamine, and L-aspartate preferentially supported cordycepin biosynthesis. Additionally, a carbon-to-nitrogen ratio of 11.6:1 was identified and experimentally validated to maximize production, higher than that reported for Cordyceps militaris. These findings correspond to a faster growth rate, enhanced carbon assimilation, and broader substrate utilization by A. oryzae. This study demonstrates the significant role of GSMM in uncovering rational engineering strategies and provides a quantitative framework for precision fermentation, offering scalable and sustainable solutions for industrial cordycepin production. Full article

The gradual recovery of the economy has positioned the digital economy as a vital force driving global economic growth. However, the sustainability of this emerging economic sector is being tested by unexpected systemic shocks. There is a scarcity of research on the factors influencing the sustainable development of the digital economy. Therefore, developing a framework to assess the sustainability of the digital economy is significant. Building on previous research, this study established an evaluation system that extracts key indicators across four dimensions: society, the economy, the environment, and technology. Data were then collected through questionnaires and in-depth interviews with experts. Subsequently, this study employed the fuzzy Decision-Making Trial and Evaluation Laboratory–Analytical Network Process (fuzzy DANP) method to determine the weight of each indicator and used the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to evaluate the sustainability of the digital economy in three cities. Sensitivity analysis was conducted to validate this comprehensive evaluation method. The results indicate that society and the economy are the two most crucial dimensions, while the regional economic development level, enterprise innovation culture, and digital divide are the top three indicators affecting the sustainable development of the digital economy industry. This work suggests that the digital economy industry should enhance regional economic levels, strengthen technological and innovative corporate cultures, and narrow the digital divide to achieve the goal of sustainable development in the digital economy sector. Full article

Tetragonia tetragonioides, or New Zealand spinach, is a widespread halophyte native to eastern Asia, Australia, and New Zealand, and naturalized in some Mediterranean regions. This underutilized vegetable is consumed for its leaves, raw or cooked. For the first time, we investigated the feasibility of using whole baby plants (including stems and leaves) as raw material for ready-to-eat (RTE) vegetable production. Our study assessed Tetragonia’s suitability for hydroponic cultivation over two cycles (autumn–winter and spring). We investigated the impact of increasing nutrient rates (only water, half-strength, and full-strength nutrient solutions) and plant densities (365, 497, and 615 plants m⁻² in the first trial and 615 and 947 plants m⁻² in the second) on baby plant production. We also analyzed the plants’ morphological and biochemical characteristics, and their viability for cold storage (21 days at 4 °C) as a minimally processed product. Tetragonia adapted well to hydroponic cultivation across both growing periods. Nevertheless, climatic conditions, plant density, and nutrient supply significantly influenced plant growth, yield, nutritional quality, and post-harvest storage. The highest plant density combined with the full-strength nutrient solution resulted in the highest yield, especially during spring (1.8 kg m⁻²), and favorable nutritional characteristics (β-carotene, Vitamin C, Fe, Cu, Mn, and Zn). Furthermore, Tetragonia baby plants proved suitable for minimal processing, maintaining good quality retention for a minimum of 14 days, thus resulting in a viable option for the RTE vegetable market. Full article

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article

Fish production faces various challenges throughout its cycle, from rearing to consumption. Organic acids have emerged as an effective fish feed and meat treatment solution. They promote health and well-being, control pathogens, improve digestion, and contribute to food preservation. This study was therefore carried out to evaluate the evolution of publications on the use of organic acids in aquaculture over time, identifying the leading journals, authors, countries, and relevant organizations associated with the publications and determining the keywords most used in publications and research trends on this type of accommodation using bibliometric analysis. For this analysis, the Web of Science (WoS) and Scopus databases were used, with the keywords and Boolean operators “organic acid*” AND (“pathogens” OR “microorganism*” OR “bacteria” OR “fungi”) AND (“fish” OR “fry” OR “pisciculture”). Ninety-six articles were found in 44 journals, with the participation of 426 authors and 188 institutions, from 1995 to 2024. The most crucial publication source with the highest impact factor was the journal Aquaculture, with 14 articles, 2 of which were written by the most relevant author, Koh C., who received the highest number of citations and had the highest impact factor among the 426 authors. China had the most scientific production, with 26 publications on organic acids in aquaculture. However, Malaysia was the country that published the most cited documents, a total of 386. The most relevant affiliation was the University of Sains Malaysia, which participated in the publication of eight articles. The 10 most frequent keywords were fish, organic acids, citric acid, article, bacteria, growth, microorganisms, Oncorhynchus mykiss, animals, and digestibility. The results indicate increased publications on the benefits of using organic acids in aquaculture, highlighting their effectiveness as antibacterial agents and promoters of zootechnical development. However, gaps still require more in-depth research into the ideal dosages, mechanisms of action, and long-term impacts of these compounds. Full article

Crystalline products with a narrow and uniform distribution of crystals by size (CSD), characterized by a desired average size, are necessary in many practices. Therefore, extensive, but mostly experimental, research is devoted to the problem of obtaining such CSDs. Alternatively, this manuscript presents a theoretical approach for calculating CSD resulting from crystallization in unstirred solutions. First, classical equations for the rates of diffusion-controlled and kinetically controlled growth of crystals are used to discuss the size-dependent growth of the nucleated crystals and the initial CSD (which arises from the non-simultaneous nucleation of crystals). Then, applying the law of conservation of matter, it is proved that the CSD continues to expand during the growth stage. Furthermore, it is substantiated that, due to their uneven spatial distribution, crystals of the same size can grow at different rates. This depends on whether the crystals are outside the diffusion fields of other crystals or are clustered together in “nests”. Moreover, by calculating the growth rates of crystals in “nests”, an explanation is given for the observation that closely spaced crystals are smaller in size than the separately growing crystals. Finally, the CSD established during the Ostwald ripening is discussed quantitatively, step-by-step. Full article

Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

Background/Objectives: The formation of oral biofilms in periodontal pockets and around dental implants with induction of periodontitis or peri-implantitis is an increasing problem in dental health. The intelligent design of a biofilm makes the bacteria embedded in the biofilm matrix highly tolerant to antiseptic therapy, often resulting in tooth or implant loss. The question therefore arises as to which mouthwashes have eradication potential against oral biofilm. Methods: A human oral biofilm model was developed based on donated blood plasma combined with buffy coats, inoculated with oral pathogenic bacterial species found in periodontal disease (Actinomyces naeslundii, Fusobacterium nucleatum, Streptococcus mitis, and Porphyromonas gingivalis). Over a span of 7 days, we tested different mouth rinsing and antiseptic solutions (Chlorhexidine, Listerine^®, NaOCl, Octenisept^®, and Octenident^®) covering the matured biofilm with 24 h renewal. Phosphate-buffered saline (PBS) was used as a control. Bacterial growth patterns were detected via quantitative polymerase chain reaction (qPCR) after 2, 4, and 7 days of treatment. Results: While all groups showed initial bacterial reduction, the control group demonstrated strong regrowth from day 2 to 4. Listerine showed a near-significant trend toward bacterial suppression. Additionally, strain-specific efficacy was observed, with Octenisept^® being most effective against Streptococcus mitis, Octenident^® and NaOCl showing superior suppression of Actinomyces naeslundii, and Listerine^® outperforming other solutions in reducing Fusobacterium nucleatum. Donor-specific, individual variability further influenced treatment outcomes, with distinct trends in bacterial suppression and regrowth observed across donors. Conclusions: These findings underscore the complexity of biofilm-associated infections and highlight the importance of targeted therapeutic approaches for managing bacterial biofilms. In this experiment, the donor-specific outcomes of the antimicrobial effects of the solutions may indicate that genetic predisposition/tolerance to oral infections appears to play a critical role in the control of oral biofilms. Full article

As a commercially valuable aquaculture species, rainbow trout (Oncorhynchus mykiss) urgently require solutions to growth inhibition associated with reproductive development. To elucidate the function of the cell cycle regulator Cyclin E genes (CCNE1 and CCNE2) in this process, we cloned the genes and analyzed their relative expression across various tissues and gonadal developmental stages. Using RNA interference (RNAi) and overexpression in RTG2 cells, we examined the effects of CCNE on cell viability, proliferation, and meiotic gene expression. Results showed that the open reading frame lengths of CCNE1 and CCNE2 were 1230 bp and 1188 bp, encoding 408 and 395 amino acids, respectively. Both proteins contain two conserved cyclin boxes, exhibit high structural similarity, and are phylogenetically most closely related to Oncorhynchus tshawytscha and Oncorhynchus kisutch. Expression and localization analyses revealed that CCNE1 was highly expressed in the ovary, while CCNE2 was highly expressed in the testis. Both proteins were expressed during fertilized egg development and key gonadal stages (at 13, 21, and 35 months post-fertilization). CCNE expression positively correlated with RTG2 cell viability and proliferation, with immunofluorescence confirming that CCNE is localized in the nucleus. Knockdown or overexpression of CCNE induced the differential expression of reproductive-related genes and key meiotic regulators. These findings suggest that CCNE1 and CCNE2 balance meiosis and gamete development through specific regulatory mechanisms, and their dysregulation may be a key factor underlying meiosis inhibition and reproductive development abnormalities. Full article

Salbutamol sulfate is a selective β2-receptor agonist used to treat asthma and chronic obstructive pulmonary disease. The crystals of salbutamol sulfate usually appear as needles with a relatively large aspect ratio, showing poor powder properties. In this study, spherical particles of salbutamol sulfate were obtained via antisolvent crystallization. Four different antisolvents, including ethanol, n-propanol, n-butanol, and sec-butanol, were selected, and their effects on crystal form and morphology were compared. Notably, a new solvate of salbutamol sulfate with sec-butanol has been obtained. The novel crystal form was characterized by single-crystal X-ray diffraction, revealing a 1:1 stoichiometric ratio between solvent and salbutamol sulfate in the crystal lattice. In addition, the effects of crystallization temperature, solute concentration, ratio of antisolvent to solvent, feeding rate, and stirring rate on the morphology of spherical particles were investigated in different antisolvents. We have found that crystals grown from the n-butanol–water system at optimal conditions (25 °C, antisolvent/solvent ratio of 9:1, and drug concentration of 0.2 g·mL⁻¹) could be developed into compact and uniform spherulites. The morphological evolution process was also monitored, and the results indicated a spherulitic growth pattern, in which sheaves of plate-like crystals gradually branched into a fully developed spherulite. This work paves a feasible way to develop new crystal forms and prepare spherical particles of pharmaceuticals. Full article

Heavy metal contamination in paddy fields poses serious risks to food safety and crop productivity. This study evaluated the potential of native soil fungi as bioinoculants to reduce metal uptake in rice cultivated under contaminated conditions. Eight fungal strains—four indigenous and four allochthonous—were selected based on their plant growth-promoting traits, including siderophore production and phosphate solubilization. Additional metabolic analysis confirmed the production of bioactive secondary metabolites. In a greenhouse experiment, three rice cultivars were grown under permanent flooding (PF) and alternate wetting and drying (AWD) in soil enriched with arsenic, cadmium, chromium, and copper. Inoculation with indigenous fungi under AWD significantly reduced the arsenic accumulation in rice shoots by up to 75%. While AWD increased cadmium uptake across all cultivars, fungal inoculation led to a moderate reduction in cadmium accumulation—ranging from 15% to 25%—in some varieties. These effects were not observed under PF conditions. The results demonstrate the potential of native fungi as a nature-based solution to mitigate heavy metal stress in rice cultivation, supporting both environmental remediation and sustainable agriculture. Full article

Background/Objectives: The global food system faces mounting pressures from population growth, dietary transitions, and resource and environmental constraints. Hybrid foods, which combine nutritional, environmental, and economic advantages, are increasingly regarded as a promising solution. This study examined consumer acceptance and willingness to pay (WTP) for a novel hybrid food product—beef rice. Methods: Based on online survey data collected from 1536 Chinese consumers, this study measured food neophobia and investigated its influence on WTP for beef rice. In addition, it explored the moderating effects of four distinct types of information interventions. Results: More than 80% of respondents expressed a willingness to purchase beef rice. Food neophobia exerted a significant negative effect on WTP (β = –1.538, p < 0.001). Among the information treatments, environmental information significantly mitigated the negative impact of food neophobia on WTP (β = 0.573, p < 0.01), while health-related and combined framings did not show significant effects. Conclusions: Chinese consumers generally hold a positive attitude toward hybrid foods such as beef rice. However, food neophobia significantly reduces their WTP. Environmental information shows a significant moderating effect and may serve as an effective strategy to enhance consumer acceptance of novel hybrid food products. Full article

The rapid growth of construction and demolition waste (CDW) presents significant challenges to sustainable urban development, particularly in densely populated regions, such as the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Despite substantial disparities in CDW management (CDWM) performance across cities, the key influencing factors and effective strategies remain underexplored, limiting the development of localized and evidence-based CDWM solutions. Therefore, this study formulated three hypotheses concerning the relationships among CDWM performance, city attributes, and governance capacity to identify the key determinants of CDWM outcomes. These hypotheses were tested using clustering and correlation analysis based on data from 11 GBA cities. The study identified three distinct city clusters based on CDW recycling, reuse, and landfill rates. Institutional support and recycling capacity were key determinants shaping CDWM performance. CDW governance capacity acted as a mediator between city attributes and performance outcomes. In addition, the study examined effective strategies and institutional measures adopted by successful GBA cities. By highlighting the importance of institutional and capacity-related factors, this research offers novel empirical insights into CDW governance in rapidly urbanizing contexts. Full article