Search Results (3,734)

The aim of this study was to develop a mathematical model to predict the body weight of tilapia fingerlings using variables obtained through computer vision. A total of 2092 tilapia fingerlings and juveniles, weighing between 10 and 100 g, were fasted for 12 h, anesthetized, weighed, and photographed using an iPhone 12 Pro Max at 33 cm height in a closed container with different bottom colors. Images were segmented using Roboflow’s instance segmentation model, achieving 99.5% mean average precision, 99.9% precision, and 100% recall. From the segmented images, area, perimeter, major axis (MA), minor axis (SA), X and Y centroids, compactness, eccentricity, and the MA/SA ratio were extracted. Seventy percent of the data was used to build the model, and 30% for validation. Stepwise multiple regression (backward selection) was performed, using body weight as the dependent variable. The prediction model was: −17.7677 + 0.0007539(area) – 0.0848303 (MA) – 0.108338(SA) + 0.0034496(CX). The validation model showed similar coefficients and R² = 0.99. The second validation, using observed versus predicted values, also yielded an R² of 0.99 and a mean absolute error of 1.57 g. Correlation and principal component analyses revealed strong positive associations among body weight, area, axes, and predicted values. Computer vision proved effective for predicting tilapia fingerlings’ weight. Full article

In this study, we focus on solving the nonlinear time-fractional Hirota–Satsuma coupled Korteweg–de Vries (KdV) and modified Korteweg–de Vries (MKdV) equations, using the Yang transform iterative method (YTIM). This method combines the Yang transform with a new iterative scheme to construct reliable and efficient solutions. Readers can understand the procedures clearly, since the implementation of Yang transform directly transforms fractional derivative sections into algebraic terms in the given problems. The new iterative scheme is applied to generate series solutions for the provided problems. The fractional derivatives are considered in the Caputo sense. To validate the proposed approach, two numerical examples are analysed and compared with exact solutions, as well as with the results obtained from the fractional reduced differential transform method (FRDTM) and the q-homotopy analysis transform method (q-HATM). The comparisons, presented through both tables and graphical illustrations, confirm the enhanced accuracy and reliability of the proposed method. Moreover, the effect of varying the fractional order is explored, demonstrating convergence of the solution as the order approaches an integer value. Importantly, the time-fractional Hirota–Satsuma coupled KdV and modified Korteweg–de Vries (MKdV) equations investigated in this work are not only of theoretical and computational interest but also possess significant implications for achieving global sustainability goals. Specifically, these equations contribute to the Sustainable Development Goal (SDG) “Life Below Water” by offering advanced modelling capabilities for understanding wave propagation and ocean dynamics, thus supporting marine ecosystem research and management. It is also relevant to SDG “Climate Action” as it aids in the simulation of environmental phenomena crucial to climate change analysis and mitigation. Additionally, the development and application of innovative mathematical modelling techniques align with “Industry, Innovation, and Infrastructure” promoting advanced computational tools for use in ocean engineering, environmental monitoring, and other infrastructure-related domains. Therefore, the proposed method not only advances mathematical and numerical analysis but also fosters interdisciplinary contributions toward sustainable development. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

In response to the low efficiency of collaborative processing of sewing machine cases at the part level in network collaborative manufacturing, this paper proposes a sewing machine cases manufacturing service composition optimization method based on an improved multi-objective artificial hummingbird algorithm. The structure and production process of sewing machine cases are analyzed; a framework for service composition optimization in the sewing machine cases manufacturing service platform is established; the required manufacturing resource service composition is determined; and a dual-objective service composition optimization mathematical model that considers Quality of Service (QoS) indicators and flexibility indicators is constructed. Opposition-based learning strategies, roulette wheel selection strategies, and improved differential evolution strategies are embedded in the multi-objective artificial hummingbird algorithm, and the improved artificial hummingbird algorithm (ORAHA_DE) is used to solve the sewing machine cases manufacturing service composition optimization model. The experimental results show the effectiveness and superiority of this composition optimization method in solving the sewing machine cases manufacturing composition optimization problem while avoiding entrapment in a local optimum during the solution process, thereby achieving the composition optimization of sewing machine cases collaborative manufacturing services. Full article

Edge detection, a cornerstone of computer vision, identifies intensity discontinuities in images, enabling applications from object recognition to autonomous navigation. This survey presents a mathematically grounded analysis of edge detection’s evolution, spanning traditional gradient-based methods, convolutional neural networks (CNNs), attention-driven architectures, transformer-backbone models, and generative paradigms. Beginning with Sobel and Canny’s kernel-based approaches, we trace the shift to data-driven CNNs like Holistically Nested Edge Detection (HED) and Bidirectional Cascade Network (BDCN), which leverage multi-scale supervision and achieve ODS (Optimal Dataset Scale) scores 0.788 and 0.806, respectively. Attention mechanisms, as in EdgeNAT (ODS 0.860) and RankED (ODS 0.824), enhance global context, while generative models like GED (ODS 0.870) achieve state-of-the-art precision via diffusion and GAN frameworks. Evaluated on BSDS500 and NYUDv2, these methods highlight a trajectory toward accuracy and robustness, yet challenges in efficiency, generalization, and multi-modal integration persist. By synthesizing mathematical formulations, performance metrics, and future directions, this survey equips researchers with a comprehensive understanding of edge detection’s past, present, and potential, bridging theoretical insights with practical advancements. Full article

In arid and semi-arid climates, where water scarcity is a persistent challenge, accurately estimating reference evapotranspiration (ET) becomes essential for sustainable water management and agricultural planning. The objectives of this study are to compare hourly ET among P–M ASCE, P–M FAO, and P–M KSA mathematical models. In addition to the accuracy assessment of daily ET derived from hourly timestep calculations for the P–M ASCE, P–M FAO, and P–M KSA. To achieve these goals, a total of 525,600-min data points from the Riyadh region, KSA, were used to compute the reference ET at multiple temporal resolutions: hourly, daily, hourly averaged over 24 h, and daily as the sum of 24 h values, across all selected Penman–Monteith (P–M) models. For hourly investigation, the comparison between reference ET computed as average hourly values and as daily/24 h values revealed statistically and practically significant differences. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001) with R² of 94.75% for ASCE, 94.87% for KSA at h_plt = 50 cm, 92.41% for FAO, and 92.44% for KSA at h_plt = 12 cm. For daily investigation, comparing the sum of 24 h ET computations to daily ET measurements revealed an underestimation of daily ET values. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001), with R² exceeding 90% for all studied reference ET models. This comprehensive approach enabled a rigorous evaluation of reference ET dynamics under hyper-arid climatic conditions, which are characteristic of central Saudi Arabia. The findings contribute to the growing body of literature emphasizing the importance of high-frequency meteorological data for improving ET estimation accuracy in arid and semi-arid regions. Full article

The role of emotional intelligence (EI) in the academic context has been steadily established, together with its impact on students’ academic achievement, well-being, and professional success. Therefore, this study examined the development of a key EI ability—emotional understanding—throughout secondary school and explored its impact on students’ academic achievement (maternal language and mathematics) at the end of this cycle, using the Vocabulary of Emotions Test. A total of 222 students were followed over the entire 3-year secondary cycle, using a three-wave longitudinal design spanning from 10th to 12th grade. At the first wave, participants were aged between 14 and 18 years (M = 15.4; SD = 0.63), with 58.6% being female. Overall, the results of Latent Growth Curve modeling indicated that students’ emotional understanding increased over the secondary school cycle. While student’s gender predicted the emotional understanding change patterns throughout secondary school, student’s GPA in 10th grade did not. Moreover, the initial levels of ability-based emotional understanding predicted students’ achievement in maternal language at the end of the cycle. Our findings offer valuable insights into how EI skills can contribute to academic endeavors in late adolescence and will explore their impact on educational settings. Full article

Quantum secret sharing (QSS) faces inherent limitations in scaling to multi-user networks due to excess noise introduced by highly asymmetric beam splitters (HABSs) in chain-structured topologies. To overcome this challenge, we propose an optical frequency comb-based continuous-variable QSS (OFC CV-QSS) scheme that establishes parallel frequency channels between users and the dealer via OFC-generated multi-wavelength carriers. By replacing the chain-structured links with dedicated frequency channels and integrating the Chinese remainder theorem (CRT) with a decentralized architecture, our design eliminates excess noise from all users using HABS while providing mathematical- and physical-layer security. Simulation results demonstrate that the scheme achieves a more than 50% improvement in maximum transmission distance compared to chain-based QSS, with significantly slower performance degradation as users scale to 20. Numerical simulations confirm the feasibility of this theoretical framework for multi-user quantum networks, offering dual-layer confidentiality without compromising key rates. Full article

The aim of this article is to identify key factors shaping organizational agility as a determinant of the sustainable development of an organization in the conditions of Economy 5.0. The research used the survey method conducted in 2024 on a sample of 312 respondents. It analyzed the impact of decision-making processes, identification with the goals of the organization, tolerance of rapid changes, internal communication, internal motivation and implementation of the idea of work–life balance. Based on the results, an original mathematical model was constructed presenting the relationships between the analyzed variables. The research results confirmed a significant relationship between the level of organizational agility and the ability of the organization to implement the sustainable development strategy. It was identified that factors such as quick and accurate decision-making, strong identification of employees with the goals of the organization and efficient communication have the greatest impact on strengthening this ability. The limitation of the research was the homogeneity of the sample and the inability to fully take into account variables related to the industry and cultural context. The research highlights that enhancing organizational agility is crucial for achieving sustainable development and building lasting competitive advantage in the dynamic context of the Economy 5.0. Full article

To address the problems of the time consumption, labor intensiveness, easy contamination, uneven drying, and impact on the medicinal efficacy of Fritillaria ussuriensis in the traditional drying method, the hot-air-drying characteristics of Fritillaria ussuriensis were studied. The changes in the moisture ratio and drying rate of Fritillaria ussuriensis under different hot-air-drying conditions (45 °C, 55 °C, 65 °C) were compared and analyzed. Six common mathematical models were used to fit the moisture change law, and it was found that the cubic model was the most suitable for describing the drying characteristics of Fritillaria ussuriensis. The R² values after fitting under the three temperature conditions were all greater than 0.99, and the maximum was achieved at 45 °C. Based on the principle of hot-air drying, a drying device for Fritillaria ussuriensis with a processing capacity of 15 kg/h was designed. It adopted a thermal circulation structure of inner and outer drying ovens, with the heating chamber separated from the drying chamber. The structural parameters were optimized based on Fluent simulation analysis. After optimization, the temperature of each layer was stable at 338 K ± 2 K, and the pressure field and velocity field were evenly distributed. The drying process parameters of Fritillaria ussuriensis were optimized based on response surface analysis, and the optimal process parameters were obtained as follows: inlet temperature: 338 K (65 °C), inlet air velocity: 3 m/s, and drying time: 10 h. The simulation results showed that the predicted moisture content of Fritillaria ussuriensis under the optimal working conditions was 12.58%, the temperature difference of Fritillaria ussuriensis at different positions was within 0.8 °C, and the humidity deviation was about 1%. A prototype of the drying device was built, and the drying test of Fritillaria ussuriensis was carried out. It was found that the temperature and moisture content of Fritillaria ussuriensis were consistent with the simulation results and met the design requirements, verifying the rationality of the device structure and the reliability of the simulation model. This design can significantly improve the distribution of the internal flow field and temperature field of the drying device, improve the drying quality and production efficiency of Fritillaria ussuriensis, and provide a technical reference for the Chinese herbal medicine-drying industry. Full article

Dual three-phase PMSMs (DTP-PMSMs) have attracted increasing attention in the field of robotics industry for their higher power density and enhanced fault-tolerant ability. The non-phase-shifted DTP-PMSM (NPSDTP-PMSM), which shows naturally prevailed performance on zero-sequence current (ZSC) suppression, necessitates the investigation on the control method with improved fault-tolerant performance. In this paper, a novel fault-tolerant control (FTC) method for NPSDTP-PMSM is proposed, which concurrently simultaneously reduces copper loss and suppresses torque ripple under single and dual open phase fault. Firstly, the mathematical model of NPSDTP-PMSM is established, where the ZSC self-suppressing mechanism is revealed. Based on which, investigations on open phase fault and the copper loss characteristics for NPSDTP-PMSM are conducted. Subsequently, a novel fault-tolerant control method is proposed for NPSDTP-PMSM, where the torque ripple is reduced by mutual cancellation of harmonic torques from two winding sets and minimized copper loss is achieved based on the convex characteristic of copper loss. Experimental validation on an integrated robotic joint motor platform confirms the effectiveness of the proposed method. Full article

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

Based on the theoretical framework of the phase comparison method and the computational analysis of the interference model calculation analysis, this paper designs, implements, establishes, calibrates, and verifies an interference experimental platform. The proposed methodology validates the effectiveness and practical feasibility of multiantenna synthetic interference technology in real-world applications. Experimental results demonstrate that the developed system can achieve flexible and arbitrary interference angles with desired distortion characteristics through precise amplitude–phase modulation, enabling dynamic manipulation of phase plane angles. Furthermore, the system successfully synthesizes false target positions at distances exceeding five times the baseline length from the jamming platform center. Both mathematical computations and experimental validations confirm that this multiantenna synthetic interference technology represents an advanced electromagnetic countermeasure characterized by two-dimensional planar interference coverage and robust phase parameter tolerance, while also enabling artificial angular glint generation. This technology exhibits significant potential for practical engineering applications. Full article

Developing green mining technology for medium-to low-grade mines requires achieving minimal or no damage to the mining area’s ecological environment. A medium-to low-grade phosphate mine in Hubei Province was taken as the research object in this study. The tailings were selected as the main filling aggregate. Indoor tests and theoretical analysis were conducted to analyze the influence of curing age, the water–cement ratio, the cement–sand ratio, and slurry concentration on the strength of the cemented backfill. Furthermore, a multi-factor non-linear mathematical model of the strength of the cementitious filler was established. The study results indicated that the strength of backfill increased linearly with the increase in the curing age, decreased negatively with the increase in the water–cement ratio, and increased exponentially with the increase in the cement–sand ratio and the slurry concentration. The multivariate non-linear prediction model of the strength of the filling body at different ages was also established based on the test results. This predictive model could effectively predict the strength of the cemented backfill, and the error value was not larger than 4%. Our research results can lay a theoretical foundation for developing medium-to low-grade phosphate mine filling with tailings as the main filling aggregate. Full article