Search Results (73)

Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article

Background: Complete environmental adaptation requires both survival and reproductive success. The hypoxic Qinghai-Tibet Plateau (>3000 m) challenges reproduction in indigenous species. Tibetan sheep, a key plateau-adapted breed, possess remarkable hypoxic tolerance, yet the genetic basis of their reproductive success remains poorly understood. Methods: We integrated transcriptomic and genomic data from Tibetan sheep and two lowland breeds (Small-tailed Han sheep and Hu sheep) to identify Tibetan sheep reproduction-associated genes (TSRGs). Results: We identified 165 TSRGs: four genes were differentially expressed (DEGs) versus Small-tailed Han sheep, 77 DEGs versus Hu sheep were found, and 73 genes were annotated in reproductive pathways. Functional analyses revealed enrichment for spermatogenesis, embryonic development, and transcriptional regulation. Notably, three top-ranked selection signals (VEPH1, HBB, and MEIKIN) showed differential expression. Murine Gene Informatics (MGI) confirmed that knockout orthologs exhibit significant phenotypes including male infertility, abnormal meiosis (male/female), oligozoospermia, and reduced neonatal weight. Conclusions: Tibetan sheep utilize an evolved suite of genes underpinning gametogenesis and embryogenesis under chronic hypoxia, ensuring high reproductive fitness—a vital component of their adaptation to plateaus. These genes provide valuable genetic markers for the selection, breeding, and conservation of Tibetan sheep as a critical genetic resource. Full article

In contrast to maintaining asphalt pavements, maintaining healthy and functional concrete pavements is a much greater challenge due to the especially brittle nature of concrete, which may require a more complex rehabilitation plan. Thanks to nondestructive testing, noninvasive on-site inspections can be carried out to assess a pavement’s condition, with the falling weight deflectometer (FWD) being the most representative example. In this study, five toll stations with concrete pavements in operation, for which no long-term monitoring protocols existed yet, were evaluated mainly with deflectometric tests using the FWD. The objective of the study was to propose a methodological framework to support responsible decision-makers in the strategic management of concrete pavements at toll stations. To meet this aim, a test campaign was organized to evaluate the pavement condition of individual slabs or lanes, assess the durability of the slabs, and determine the efficiency of load transfer across joints and cracks. As a key finding, pavement slab deflections were found to exhibit a considerable range; in particular, a range of 50–1450 μm for the maximum deflection of the FWD was observed. This finding stimulated a distribution fitting analysis to estimate characteristic values and thresholds for common deflection indicators that were validated on the basis of pavement design input data. Finally, the study proceeded with the development of a conceptual approach proposing evaluation criteria for individual slab assessment and the condition mapping of in-service concrete pavements. Full article

The Global Navigation Satellite System (GNSS) is extensively utilized in various applications that require triangulation solutions for positioning, navigation, and timing (PNT). These solutions are obtained by solving state estimates, traditionally using methods like weighted least squares (WLS) and Kalman Filters (KF). While these conventional approaches are foundational, they frequently encounter challenges related to robustness, particularly the necessity for precise noise statistics and the reliance on potentially accurate prior assumptions. This paper introduces a hybrid approach to GNSS state estimation, which integrates deep neural networks (DNNs) with the KF framework, employing the maximum likelihood principle for unsupervised training. Our methodology combines the strengths of DNNs with conventional KF techniques, leveraging established model-based priors while enabling flexible, data-driven modifications. We parameterize components of the Extended Kalman Filter (EKF) using neural networks, training them with a probabilistically informed maximum likelihood loss function and backpropagation. We demonstrate that this hybrid method outperforms classical algorithms both in terms of accuracy and flexibility for easier implementation, showing better than 30% improvement in the variance of the horizontal position for the simulated as well as the real-world dynamic receiver dataset. For the real-world dynamic dataset, our method also provides a better fit to the measurements than the classical algorithms. Full article

This study addresses the dual challenges of complex road environments and diverse task-safety requirements in mobile-robot path planning by proposing an innovative method that integrates a dual-layer fuzzy control system with an improved genetic algorithm. Initially, an expert system-based dual-layer fuzzy control system is developed. The first layer translates complex road conditions and obstacles into road-safety levels, while the second layer combines these with task-safety levels to generate fitness weights for the genetic algorithm. Furthermore, road-safety factors are incorporated into the genetic algorithm’s fitness function to enhance safety considerations in path planning. The algorithm implementation incorporates Bernoulli chaotic mapping, Gaussian operators, and Symmetrical Sigmoid operators to optimize the selection, crossover, and mutation processes, significantly boosting the algorithm’s global search capability and efficiency. Experimental results indicate that the proposed method reduces path distance by up to 5.9% and decreases the number of turns by up to 85.7%, demonstrating superior universality and robustness across various comparative experiments. This research contributes to resolving the issues posed by complex road environments and varying task-safety requirements in mobile-robot path planning. Full article

Ship trajectory tracking controllers based on deep reinforcement learning (DRL) are widely applied in various fields such as autonomous driving and robotics due to their strong adaptive learning capabilities and optimization decision-making ability. However, ship trajectory control faces challenges such as long training cycles and poor convergence performance. These issues are primarily caused by the unreasonable design of algorithm models and reward functions, which limit the performance optimization and energy efficiency improvements in real-world navigation. In this paper, we propose a ship trajectory tracking control algorithm based on deep reinforcement learning. The proposed algorithm introduces maximum entropy theory and experience replay techniques. Additionally, it enhances the reward function module by adding reward terms and fitting weight designs. A three-dimensional simulation environment is constructed to validate the proposed method. The results demonstrate that the controller designed in this study outperforms traditional DRL controllers in terms of fast convergence, convergence stability, and final reward values. The controller meets the requirements for tracking conventional trajectories and shows stable and efficient performance in both wide-area water search experiments and river channel traversal experiments. These experimental results provide valuable insights for future research directions. Full article

This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device. Full article

Phosphate sorption data are often analyzed by least-squares fitting to the two- or three-parameter Freundlich model. The standard methods are flawed by (1) treating the measured pseudo-equilibrium concentration C as the independent (hence error-free) variable and (2) neglecting the weighting that should accommodate the varying precision of the data. Here, we address both of these shortfalls and use a global fit model to achieve optimal precision in fitting data for five acidic Australian soil types. Each individual dataset consists of measured C values for up to nine phosphate spiking levels C₀. For each soil type, there are three–five such datasets from varying levels of phosphate fertilizer pre-exposure (P_f) two years earlier. These datasets are fitted simultaneously by expressing the Freundlich capacity factor a and exponent b as theoretically predicted functions of the assay amounts of Fe, Al, and P measured for each P_f. The analysis allows for uncertainty in both C and C₀, with inverse-variance weighting from variance functions estimated by residuals analysis. The estimated presorbed P amounts Q depend linearly on P_f, with positive intercepts at P_f = 0, indicating residual phosphate in the soils prior to the laboratory phosphate treatments. The key takeaway points are as follows: (1) global analysis yields optimal estimates and improved precision for the fit parameters; (2) allowing for uncertainty in C is essential when the data include C values near 0; (3) varying data precision requires weighting to yield optimal parameter estimates and reliable uncertainties. Full article

Physical activity induces modifications in the concentrations of trace mineral elements. However, studies exploring sex-related differences in manganese (Mn) and molybdenum (Mo) levels among athletes are scarce. Mn and Mo are essentials metals required for a variety of metabolic functions, including those involved in normal human development, the activation of certain metalloenzymes, energy metabolism, and immune system function. They are important cofactors for a variety of enzymes, including those involved in neurotransmitter synthesis and metabolism. The presence of molybdenum (Mo) is essential for several enzymes, including xanthine oxidase (XO), aldehyde oxidase, sulfite oxidase (SO), and the mitochondrial amidoxime reductase component (mARC). This study aimed to: (a) analyse changes in plasma, urine, erythrocyte, and platelet Mn and Mo concentrations throughout a competitive season in men’s and women’s football players, and (b) investigate sex-based discrepancies. A total of 46 football players (22 men: age; 20.62 ± 2.66 years; height; 1.76 ± 0.061 m; weight; 71.50 ± 5.93 kg, and 24 women: age; 23.21 ± 4.11 years; height; 1.65 ± 0.06 m; weight; 59.58 ± 7.17 kg) participated in this study. Three assessments were conducted throughout the competitive season. Data were collected on anthropometry, body composition, nutritional intake, physical fitness, female hormones, haematology, and the determination of Mn and Mo in different biological compartments. Regarding Mn, significant sex differences were observed in plasma, urine, and erythrocyte concentrations (p < 0.05). Moreover, significant variations were observed throughout the season in all analysed biological compartments (p < 0.05). Regarding Mo, significant sex differences were reported in plasma concentrations (p < 0.05). Similarly, there were variations throughout the season in all analysed biological compartments (p < 0.05). Plasma, urine, erythrocyte, and platelet Mn and Mo concentrations could change during a competitive season in football players. On the other hand, sex differences could exist in plasma, urine, and erythrocyte Mn concentrations in football players. Full article

Hāpuku (Polyprion oxygeneios) is a promising candidate for aquaculture production in New Zealand. Methods for spawning, juvenile production, and growout to harvest entirely on land, where water quality, pathogens, environmental impacts, and genetic “pollution” can be tightly controlled, have been developed, and genetic improvement to optimise land-based production is the obvious next step. However, estimates of genetic parameters are required to design a rigorous, disciplined, and effective selective breeding program. By using existing data consisting of irregularly spaced repeated measurements of fork length and live body weight collected on wild-collected founders and two generations of captively reared progeny, we evaluated the species’ genetic potential for improvement in growth. We first tested a range of univariate random regression models to identify the best-fitting models for these data. Subsequently, using a bivariate model, we estimated variance components for growth trajectories of fork length and whole body weight. With one to six records available per fish, the best-fitting univariate models included only a fixed effect for contemporary groups and fixed and random genetic third-order Legendre polynomials. More complex models that included full-sib family and/or permanent environmental effects produced unacceptable constrained and/or non-positive-definite solutions. Both traits are moderately heritable at all stages of the growout phase (~0.4–0.5), and the genetic correlation patterns between daily breeding values estimated via the covariance function are different for length and weight. Genetic correlations for length between all pairs of age-specific breeding values are positive and strong (>0.7) and change gradually and smoothly with increasing temporal separation. For weight, these correlations deteriorate more rapidly with increasing time lags between measurements and become negative for some age pairings. We conclude that random regression analyses are a valuable tool for extracting genetic information from irregularly spaced repeated measurements of fish size, speculate that emerging technologies for high-throughput genotyping and phenotyping will add to the value of this approach in the near future, and reason that a breeding strategy that rigorously takes into account the potentially unfavourable genetic correlations between breeding values for weight at some ages will further adapt hāpuku to land-based systems and enhance the profitability commercial-scale production. Full article

Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&D). The design of new product forms must ensure the continuity of PI and concurrently address the emotional needs of users. It demands a high level of experience from designers and significant investment in R&D. To solve this problem, a generative and quantitative design method powered by AI, based on Shape Grammar (SG) and Kansei Engineering (KE), is proposed. The specific method is as follows: Firstly, representative products for Morphological Analysis (MA) are selected, SG is applied to establish initial shapes and transformation rules, and prompts are input into Midjourney. This process generates conceptual sketches and iteratively refines them, resulting in a set of conceptual sketches that preserve the PI. Secondly, a web crawler mines online reviews to extract Kansei words. Factor Analysis (FA) clusters them into Kansei factors, and the Grey Analytic Hierarchy Process (G-AHP) calculates their grey weights. Thirdly, after analyzing the PI conceptual sketches for feature extraction, the features are integrated with CRs into the Quality Function Deployment (QFD) matrix. Experts evaluate the relationships using interval grey numbers, calculating the optimal ranking of PI Engineering Characteristics (PIECs). Finally, professional designers refine the selected sketches into 3D models and detailed designs. Using a Chinese brand as a case study, we have designed a female electric moped (E-moped) to fit the PI and users’ emotional needs. Through a questionnaire survey on the design scheme, we argue that the proposed innovative method is efficient, applicable, and effective in balancing the product form design of PI and user emotions. Full article

Background: There is a consensus among combat sports researchers regarding the important role of evaluating the comprehensive special fitness and performance of athletes. This is due to the specific structure of tasks performed in these sports as they require a comprehensive and integrated function of broadly understood physical fitness. The present study aimed to develop and check the Special Brazilian Jiu-Jitsu Fitness Test: Takedown Zone (SBJJFT-TZ), Gi formula’s reliability. The major task of this sport-specific tool is to illustrate the comprehensive special fitness and performance of professional Brazilian jiu-jitsu athletes. Methods: The study covered 27 BJJ athletes (age in years: 25.36 ± 2.99; height: 175.04 ± 5.70 cm; weight: 76.56 ± 8.59 kg; BMI: 24.96 ± 2.30; 6.33 ± 2.51 years of training) with a high sports skill level. A coaches’ ranking of the athletes studied according to sports achievement was developed. An experimental approach to the problem was employed, with the SBJJFT-TZ assessment, including throws and specialized locomotion, performed on two dates (seven days apart). The test parameters were recorded and, using a specialized formula, an index showing comprehensive special fitness was calculated. To verify the test’s validity and reliability, using statistical procedures, the results were comparatively analyzed (Student’s t-test for the dependent variables), and the relationships between rankings were examined (Pearson’s r linear correlation). The level of statistical significance was set at p < 0.05. Results: The analyses showed statistically significant relationships with very high strength between coach ranking and SBJJFT-TZ parameters for both dates (R = 0.73–0.88; p < 0.001). A similar trend of significant correlations was noted between the first and second SBJJFT-TZ dates (R = 0.96–0.98; p < 0.001), and the strength of the coexistence was almost complete. The test-retest procedure for SBJJFT-TZ showed similar levels for the parameters studied, without significant variation (p > 0.05) but with a weak effect profile (d = 0.03–0.06). Internal variation in the variables was very low (HR CV < 10%) and moderate (Throws and Index CV = 20.82–32.25%). Significantly different throwing performance between sets was shown, with an advantage in the first round of work compared to the second, and a moderate effect (p < 0.05; d = 0.38–0.39). Conclusions: The identification of relationships, the comparison, and the test-retest procedure showed the high application value of SBJJFT-TZ. The proposed tool provides a reliable cross-sectional evaluation of BJJ athletes’ special fitness and performance in the stand-up fighting plane, as well as at the moment of achieving an advantage over the opponent, and favors implementation in field conditions. Full article

One requirement posed by customers is to achieve adequate durability levels as described in technical requirement documents. Modal analysis is one of the design assessments aimed at identifying the risks of high cycle fatigue (HCF). This article presents a novel application of an artificial immune system (AIS) in the optimization of a nozzle guide vane’s modal characteristics. The aim is to optimize the system’s natural frequencies in the vibration vane and adjacent hardware (turbine casing). The geometrical characteristics accounted for in the optimization process include the shell thicknesses on the turbine casing side and the nozzle outer band features (hook thickness, leaning and position). The optimization process is based on a representative model established from FEM analysis results. The framework is robust because of the applied metamodel and does not require time-consuming FEM analysis in order to evaluate the fitness function. The aim is to minimize the model area (a derivative of the system weight) with constraints imposed on the frequency (a penalty function). The optimum design is given as the solution with an increased shell thickness in the turbine casing and leaning nozzle outer band hooks to obtain the maximum stiffness of the system. The results obtained by means of the artificial immune system (AIS) and a novel variant based on an additional costimulation procedure (CAIS) are compared with the solution obtained by means of a genetic algorithm implemented in the commercial CAE software (Ansys version 19.2). Full article

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train. Full article

A set of online inspection systems for surface defects based on machine vision was designed in response to the issue that extrusion molding ceramic 3D printing is prone to pits, bubbles, bulges, and other defects during the printing process that affect the mechanical properties of the printed products. The inspection system automatically identifies and locates defects in the printing process by inspecting the upper surface of the printing blank, and then feeds back to the control system to produce a layer of adjustment or stop the printing. Due to the conflict between the position of the camera and the extrusion head of the printer, the camera is placed at an angle, and the method of identifying the points and fitting the function to the data was used to correct the camera for aberrations. The region to be detected is extracted using the Otsu method (OSTU) on the acquired image, and the defects are detected using methods such as the Canny algorithm and Fast Fourier Transform, and the three defects are distinguished using the double threshold method. The experimental results show that the new aberration correction method can effectively minimize the effect of near-large selection caused by the tilted placement of the camera, and the accuracy of this system in detecting surface defects reached more than 97.2%, with a detection accuracy of 0.051 mm, which can meet the detection requirements. Using the weighting function to distinguish between its features and defects, and using the confusion matrix with the recall rate and precision as the evaluation indexes of this system, the results show that the detection system has accurate detection capability for the defects that occur during the printing process. Full article