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Livestock and poultry biodiversity constitutes an essential element of global biological diversity, playing a pivotal role in sustaining human livelihood and socioeconomic development. Domestic animal genetic resources in China are abundant and various. Especially, local breeds have strong adaptability to the environment and exhibit excellent traits. They are the material foundation for both the original innovation in agricultural technology and the development of modern animal husbandry. Conservation of animal genetic resources is the primary action for sustainable use and development of domestic animals. Globally, many national and international institutions have initiated a variety of conservation measures, legislation, and technical strategies. China has likewise undertaken relevant initiatives. In this paper, we summarize the current situation of domestic animal resources in China, including the current status of domestic animals, the conservation measurements, the sustainable utilization, the management policies, challenges, and suggestions for the conservation of domestic animal resources. The sustainable use and protection work on domestic animals can be incorporated with the issues of food security and sustainability, the protection of the environment and climatic change, concepts in which societal interest is continuously increasing. Full article

Milk and dairy products play a key role in the human diet but may also be vehicles for toxic contaminants, particularly heavy metals and metalloids (HMs), such as lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As). This integrative review examines peer-reviewed studies published between 2015 and 2025 to examine sources, occurrence, and health risks associated with HM contamination in milk and dairy products. Key sources include industrial emissions, agricultural runoff, contaminated feed and water, and inadequate packaging. This review highlights regulatory inconsistencies, limited surveillance, and underuse of metal speciation analysis, which hinder accurate toxicity assessment. Advances in trace-level HM detection systems are discussed in terms of sensitivity, accessibility, and feasibility. Studies from diverse geographic regions frequently report high levels of Pb and Cd in samples originating from industrialized areas in low- and middle-income countries. Health risk indicators, such as target hazard quotients (THQs) and margins of exposure (MOEs), often exceed safety thresholds, particularly in children, indicating significant public health risks, especially with prolonged exposure. These findings underscore the urgent need for systematic contaminant monitoring, harmonized regulations, source-focused mitigation policies, and investment in rapid, cost-effective testing technologies to safeguard milk and dairy product safety worldwide. Full article

This study proposes a lightweight real-time fishing vessel tracking and size measurement model based on a UAV. In view of the problems faced by the current fishing port management department, such as low efficiency of fishing vessel size measurement methods and difficulty in updating the size information of large quantities of fishing vessels in time, this paper proposes a lightweight real-time fishing vessel tracking and size measurement model based on a UAV. (YOLO-LFVM). The model incorporates lightweight modules, such as MobileNetV3, AKConv, and C2f, and utilizes Python scripts in conjunction with OpenCV to measure vessel size in pixels. The findings indicate that, compared to the original model, the YOLO-LFVM model’s accuracy rate, recall rate, and mAP@0.5 decrease by only 0.7%, 0.2%, and 0.3%, respectively, while mAP@0.95 increases by 1.7%. Additionally, the model’s parameters decrease by 65%, and GFLOPs decrease by 69%. When comparing the model’s output with actual vessel data, the average relative error for total length is 2.67%, and for width, it is 3.28%. The research shows that the YOLO-LFVM model is effective in ship identification, ship tracking statistics, and measurement. Through the integration with UAV remote sensing technology, it is conducive to the timely updating of large-scale fishing vessel size information. Finally, the model can assist the daily management and law enforcement of the fishing port management department and can be applied to other equipment with limited computing power to perform target detection and object size measurement tasks. Full article

This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm³ and a coefficient of determination (R²) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology has emerged as a research hotspot in the remote sensing field in recent years due to its advantages of low cost and high precision for soil moisture monitoring. Addressing the issue that fixed elevation angle intervals struggle to adapt to the varying signal characteristics of different satellites, this paper proposes an adaptive elevation angle interval selection method based on wavelet transform. This method utilizes wavelet transform to analyze the time-frequency characteristics of the residual Signal-to-Noise Ratio (SNR) signal, calculates the ratio sequence of the main frequency component strength to the noise component strength, and sets a threshold to automatically determine the retrieval elevation angle interval for each satellite, thereby improving the accuracy of feature parameter extraction. The results show the following: ① Compared to traditional fixed elevation angle intervals (5–20° and 5–30°), the proposed method significantly enhances soil moisture retrieval accuracy. ② For the averaged phase feature parameters calculated within the algorithm-selected intervals for all satellites, the R² and RMSE are 0.925 and 0.55%, respectively, representing improvements of 3.1% and 14.2% compared to the original results. ③ For signals from low-quality reflection zones, R² increased from 0.728 to 0.839 (a 13.2% improvement), while RMSE decreased from 1.045 to 0.806 (a 22.9% reduction). This method effectively adapts to the quality attenuation characteristics of satellite signals across different reflection zones, providing an optimized elevation angle interval selection strategy for GNSS-IR soil moisture retrieval. Full article

Automation has been widely applied and has greatly affected quality management in the catering industry. Intelligent restaurants refer to those in which smart devices and artificial intelligence (AI) technologies (such as robots and self-service technologies) are embedded in the restaurant environment. However, the existing research on intelligent restaurants has mostly focused on the technological development of equipment. Hence, this interdisciplinary study, integrating insights from hospitality management and human–computer interaction, examines how human-provided and automated-provided services interactively influence customers’ dining experience quality in intelligent restaurants, and how they affect customers’ perceived value and their social media sharing generation. This study develops a measurement scale of dining experience quality in intelligent restaurants that contains human-provided experience and automated-provided experience through in-depth interviews with 15 customers (Study1), and a model was proposed and verified using partial least-squares structural equation modelling (PLS-SEM) analysis on a sample of 493 customers dining in intelligent restaurants (Study 2), which shows that the quality of dining experience has a positive effect on customer perceived value, overall satisfaction in intelligent restaurants, and social media sharing generation. Specifically, automated-provided services offer functional value, while human employees mainly provide perceived emotional value. Perceived functional value has a greater impact on overall satisfaction with intelligent restaurants. The originality of this research is that it integrates services provided by humans and services provided by automated devices and clarifies the different roles of functional and emotional value in shaping customers’ perceived value. These findings provide a new research perspective for intelligent restaurants and insight into the optimization of service quality and automation systems in intelligent restaurants, thereby promoting sustainable business practices in the industry. Full article

Topology optimization serves as a critical method for promoting lightweight structural design. Traditional methods predominantly focus on mechanical performance evaluation, often neglecting the critical correlation between modal characteristics and structural stiffness. The Evolutionary Structural Optimization (ESO) method is extensively employed in topology optimization; however, iterative oscillations lead to issues such as grid divergence and diminished solution quality. To address issues such as iterative oscillations and mesh divergence in the traditional Evolutionary Structural Optimization (ESO) method, this study applies a Simp Evolutionary Structural Optimization (SI-ESO) methodology. This method integrates intermediate density parameters and penalty factors into the progressive structural optimization process, thereby significantly enhancing iterative convergence and model quality. This work applied the optimized SI-ESO method to the lightweight redesign of an aviation generator’s rear cover, with validation conducted through additive manufacturing. Subsequently, the back cover of an aviation generator was redesigned and fabricated utilizing additive manufacturing technology. Empirical results indicate that under maximum stress conditions and employing the same additive process, the maximum deformation of the SI-ESO-optimized model is reduced compared to that of the ESO-designed model. Compared with the original design, the SI-ESO-optimized model achieved a 31% weight reduction, while relative to the ESO-optimized model, it exhibited a 27% lower maximum stress and a 10.53% higher first-order frequency, demonstrating both lightweighting and enhanced structural stiffness. Full article

Automated landmark recognition represents a cornerstone technology for advancing smart tourism systems, cultural heritage documentation, and enhanced visitor experiences. Contemporary deep learning methodologies have substantially transformed the accuracy and computational efficiency of destination classification tasks. Addressing critical gaps in existing approaches, we introduce an enhanced Samarkand_v2 dataset encompassing twelve distinct historical landmark categories with comprehensive environmental variability. Our methodology incorporates a systematic multi-threshold pixel intensification strategy, applying graduated enhancement transformations at intensity levels of 100, 150, and 225 to accentuate diverse architectural characteristics spanning from fine-grained textural elements to prominent reflective components. Four independent YOLO11 architectures were trained using original imagery alongside systematically enhanced variants, with optimal epoch preservation based on validation performance criteria. A key innovation lies in our intelligent selective ensemble mechanism that conducts exhaustive evaluation of model combinations, identifying optimal configurations through data-driven selection rather than conventional uniform weighting schemes. Experimental validation demonstrates substantial performance gains over established baseline architectures and traditional ensemble approaches, achieving exceptional metrics: 99.24% accuracy, 99.36% precision, 99.40% recall, and 99.36% F1-score. Rigorous statistical analysis via paired t-tests validates the significance of enhancement strategies, particularly demonstrating effectiveness of lower-threshold transformations in capturing architectural nuances. The framework exhibits remarkable resilience across challenging conditions including illumination variations, structural occlusions, and inter-class architectural similarities. These achievements establish the methodology’s substantial potential for practical smart tourism deployment, automated heritage preservation initiatives, and real-time mobile landmark recognition systems, contributing significantly to the advancement of intelligent tourism technologies. Full article

This article presents an analysis of the architectural integration of two new buildings implemented on the Gdańsk University of Technology campus (Poland) as a case study of combining contemporary technologies with cultural continuity. The buildings, designed by the author of the article, who is the main designer, are a conscious response to the historical urban and architectural context of the campus, the development of which started at the beginning of the 20th century in the style of Dutch Neo-Renaissance. The new buildings refer to the architectural heritage of the university through their scale and colors, but their form, details and applied technological solutions clearly reflect modernity. A particularly important element of their modern character is the implementation of advanced pro-ecological systems for obtaining energy from renewable sources (RES), which fits into the current climate challenges and the role of the technical university as a promoter of sustainable development. The article discusses how architecture, materials and modern building systems were used to create a dialogue between tradition and innovation. The analysis is based on design documentation and planning conditions, and its background is a broader discourse on culturally sustainable architecture. Conscious of other, more conservative views, the author puts forward the thesis that cultural continuity does not require stylistic imitation, but conscious, contextual reinterpretation. The results of the article enrich the debate on the development of academic campuses, heritage-responsible design and the role of the architect in shaping a space that connects the future with the past. The main research contribution of the article is the presentation of an original method of designing architectural objects that integrates advanced pro-ecological technologies with a contextual reinterpretation of architectural heritage, which constitutes a new perspective in the discussion on culturally sustainable architecture. Full article

Nurseries represent a fundamental component of modern agricultural systems, specializing in the cultivation and management of diverse seedlings. Scientific cultivation methods significantly enhance seedling survival rates, while intelligent agricultural robots improve operational efficiency through autonomous plant protection. Central to these robotic systems, the perception system utilizes advanced neural networks to process environmental data from both images and point clouds, enabling precise feature extraction. This review systematically explores prevalent image-based models for classification, segmentation, and object detection tasks, alongside point cloud processing techniques employing multi-view, voxel-based, and original data approaches. The discussion extends to practical applications across six critical plant protection areas. Image-based neural network models can fully utilize the color information of objects, making them more suitable for tasks such as leaf disease detection and pest detection. In contrast, point cloud-based neural network models can take full advantage of the spatial information of objects, thus being more applicable to tasks like target information detection. By identifying current challenges and future research priorities, the analysis provides valuable insights for advancing agricultural robotics and precision plant protection technologies. Full article

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability. Full article

This study focuses on optimizing the flow field uniformity within a vertical honeycomb wet electrostatic precipitator (WESP), which is a critical prerequisite for achieving high particulate removal efficiency. For a vertical honeycomb WESP with an air capacity of 25,000 m³/h, the internal flow field is optimized by adjusting the opening ratio and aperture ratio of the airflow equalizing plate, installing additional deflector plates, and adding additional airflow equalizing plates at strategic locations. The optimization reduces the velocity relative standard deviation at the anode inlet section to 0.14. Through 1:1-scale equipment construction and testing, the particle concentration at the outlet is stabilized below 10 mg/Nm³, with an average removal efficiency of 95.88%—a 5.7% improvement over the original model. This study solves the design dependency on empirical guidance for vertical honeycomb WESP in the food industry, providing a green technology paradigm for low-carbon industrial emissions. Full article

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

This systematic review based on PRISMA presents an analysis of 74 studies, conducted between 1889 and 2024, on the issue of digital technologies for the development of entrepreneurial skills of young people, with a focus on Latin America. The original review combines 44 regional and 30 international studies, examining mobile platforms, e-commerce, artificial intelligence, and immersive technologies. The results present a paradigm of innovation through constraint, with all successful adaptations driven by infrastructural limitations. As case studies, Latin American contexts have demonstrated the effectiveness of mobile technology and microlearning comparable to costly immersive technologies, completely contradicting deficit narratives. Seventy-eight percent of regional studies adhere systematically to the Sustainable Development Goals (SDGs 4, 8, 10), illustrating an inclusive approach to technological development that values social impact over technical sophistication. Triangulation bibliometrics confirm the institutionalization of three research traditions—techno-deterministic, constructivist, and critical—with a focus on innovation–digital transformation–technological entrepreneurship. Studies show that contexts traditionally considered “limited” generate innovations with potential for reverse transfer to developed economies. The implementation gap between research and practice reflects systemic tensions between academic frameworks and contextual complexities. This will motivate fundamental justifications for implementing educational policies in ways that support contextual diversity as a strategic strength, fostering the sustainable development of youth entrepreneurial skills in the digital age. Full article

Grape pomace (i.e., the residual skins, seeds, and pulp left after vinification) retains up to 70% of the fruit’s original phenolic compounds and is also rich in dietary fiber. As such, because this by-product is generated in large quantities worldwide and its disposal is both technologically problematic and costly, reusing it as a food ingredient could simultaneously mitigate environmental burdens, lower winery waste-management expenses, and enhance the nutritional profile of fortified foods. In this context, this study investigated the nutritional enrichment of dark chocolate by incorporating flour produced from red (cv. Syrah) and white (cv. Arinto) grape pomace at three levels (5, 10, and 15% w/w). Formulated chocolates and controls were manufactured under industrial tempering conditions and subsequently analyzed for protein, lipids, sugars, dietary fiber, total phenolic content, antioxidant capacity (DPPH and ORAC), color, texture, and consumer perception (hedonic test). All fortified samples showed higher fiber and antioxidant activity than the control, with “White_15” showing higher fiber content (43.1%) and “Red_5” for ORAC (69,483 µmol TE/100 g) and DPPH (6587 µmol TE/100 g). Dietary fiber showed an increase in content with the increase in grape pomace incorporation, regardless of the type (red or white). Texture softening was observed in all fortified chocolates independently of the incorporation level or type (red or white). Principal Component Analysis (PCA) and hierarchical clustering confirmed clear separation between control and fortified chocolates based on the parameters analyzed. Sensory evaluation with untrained panelists revealed good overall acceptability across all formulations. These findings demonstrate that grape pomace flour can be effectively valorized as a functional ingredient in dark chocolates, supporting circular economy practices in the wine and confectionery sectors while delivering products with enhanced health-promoting attributes (nutritional and antioxidant). Full article