Search Results (251)

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

The number of donkeys in Europe has significantly declined in recent decades due to mechanization; however, recently, the demand for donkey milk and other purposes has led to a slight increase in their population. However, information on how they are kept and managed, and their welfare is limited. This review aimed to explore the husbandry, management, and welfare of donkeys (Equus asinus) across European Union member states, Switzerland, and the United Kingdom. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was used. The search was conducted using Scopus and Web of Science, identifying 797 records that were screened using titles, keywords, and abstracts, resulting in 78 retained records. An additional 19 records were identified using snowballing and experts’ suggestions, bringing the total to 97. Dairy donkeys have been studied mainly in Italy, and there they are usually managed under extensive to semi-intensive husbandry systems. Donkeys involved in human intervention therapies are generally managed semi-intensively. Based on the literature, most donkeys are provided with shelter and outdoor access, and this can be with or without pasture, except the free-range donkeys that graze year-round. Health and management-related issues (e.g., obesity, dental disorders, and hoof disorders) could be overlooked, potentially compromising their welfare. The feeding management of donkeys is generally traditional and poorly studied, relying mainly on forages supplemented with concentrates. Most donkeys suffer from overweight/obesity except for lactating donkeys, which are often underweight. This may indicate unbalanced feeding practices. Improved understanding of housing and feeding management is essential for establishing evidence-based welfare guidelines tailored to the donkeys’ species-specific needs. Full article

Triticale is a cereal that currently has a cultivated global area of approximately 3.8 Mha. It is widely used as a feed and forage crop. Although winter triticale cultivars are planted in Poland, Germany and Belarus (the main producers), a significant portion of their cultivation is carried out in the Mediterranean basin using spring cultivars. Spain and Türkiye are two examples of the success of this crop in terms of promotion, breeding, and expansion. Thus, in 2022/23, 280,000 hectares of triticale were planted in Spain, while 100,000 hectares were planted in Türkiye, ranking 5th and 8th in the world, respectively. Current triticale cultivars have high grain and/or forage yield. Furthermore, dual-purpose cultivars are available and can be intercropped with legumes, which increases their possibilities in the field. Triticale competes well with weeds and is resistant to many diseases. It performs well in acidic soils, and it is tolerant to drought, conditions common in the Mediterranean basin. In the future, funding for spring triticale breeding programs (which are scarce and declining) should be maintained, and projects to improve agronomic techniques and publicize the advantages of this crop could be implemented. Furthermore, the use of triticale for human food could expand in the region, especially in MENA countries. Full article

Lavender is a plant widely used in the cosmetic, pharmaceutical, and food industries, and it is also well known for producing nectar and pollen that bees use to make honey. However, due to increasingly adverse atmospheric conditions in recent years, characterized by prolonged dry spells or intense rainfall focused in short periods, the production of monofloral honey, such as lavender honey, has become increasingly challenging. Therefore, accurate mapping of monofloral zones in order to support beekeepers in placing their beehives in the best location is required. In this context, the town of Kuyucak in Isparta Province (Turkey), renowned for its extensive lavender fields, was selected. Using true orthophoto images from 2020 with a ground sampling distance (GSD) of 30 cm, machine learning classification methods and deep learning techniques were applied to identify and map the correspondent lavender fields. Lavender plants within the region were detected using Maximum Likelihood (ML), Support Vector Machine (SVM), and Random Forest (RF) classifiers, as well as the Mask R-CNN deep learning method. The classification achieved an overall accuracy of 95% and a kappa coefficient of 0.94. Subsequently, assuming a bee foraging range of 3 km, a moving squared window (sizing 3 × 3 km) was used to estimate local areas with potential forage resources and the corresponding honey production potential. The resulting honey potential production maps then used to identify optimal location for beekeepers’ hives in order to maximize lavender honey production. Full article

Artificial protozoa optimizer (APO), as a newly proposed meta-heuristic algorithm, is inspired by the foraging, dormancy, and reproduction behaviors of protozoa in nature. Compared with traditional optimization algorithms, APO demonstrates strong competitive advantages; nevertheless, it is not without inherent limitations, such as slow convergence and a proclivity towards local optimization. In order to enhance the efficacy of the algorithm, this paper puts forth a multi-strategy fusion artificial protozoa optimizer, referred to as MSAPO. In the initialization stage, MSAPO employs the piecewise chaotic opposition-based learning strategy, which results in a uniform population distribution, circumvents initialization bias, and enhances the global exploration capability of the algorithm. Subsequently, cyclone foraging strategy is implemented during the heterotrophic foraging phase. enabling the algorithm to identify the optimal search direction with greater precision, guided by the globally optimal individuals. This reduces random wandering, significantly accelerating the optimization search and enhancing the ability to jump out of the local optimal solutions. Furthermore, the incorporation of hybrid mutation strategy in the reproduction stage enables the algorithm to adaptively transform the mutation patterns during the iteration process, facilitating a strategic balance between rapid escape from local optima in the initial stages and precise convergence in the subsequent stages. Ultimately, crisscross strategy is incorporated at the conclusion of the algorithm’s iteration. This not only enhances the algorithm’s global search capacity but also augments its capability to circumvent local optima through the integrated application of horizontal and vertical crossover techniques. This paper presents a comparative analysis of MSAPO with other prominent optimization algorithms on the three-dimensional CEC2017 and the highest-dimensional CEC2022 test sets, and the results of numerical experiments show that MSAPO outperforms the compared algorithms, and ranks first in the performance evaluation in a comprehensive way. In addition, in eight real-world engineering design problem experiments, MSAPO almost always achieves the theoretical optimal value, which fully confirms its high efficiency and applicability, thus verifying the great potential of MSAPO in solving complex optimization problems. Full article

This paper examines various aspects of maize plant height. Firstly, it emphasizes that maize is a significant food and forage crop with considerable research significance, and that its plant height is influenced by multiple factors, including biotic elements such as genes and plant hormones, as well as abiotic factors such as soil, water, and climate. Secondly, the paper explores the complex relationship between maize plant height and yield, noting that moderate plant height can improve photosynthetic efficiency, reduce lodging risk, and enhance yield, although it may also affect kernel quality. Additionally, the paper reviews the application of modern biotechnological methods in maize plant height research, such as genome-wide linkage analysis, gene editing, transgenic technology, and epigenetic studies, which aid in elucidating the genetic mechanisms underlying plant height. Finally, it outlines future research directions for improving maize plant height and yield, highlighting key challenges that require urgent attention, such as the advancement of gene editing techniques, the integration of multiple biotechnologies, and strategies to address climate change, with the ultimate goal of achieving precision breeding for high-yielding, stress-resistant, and broadly adaptable maize varieties. Full article

The Parrot Optimization Algorithm (PO) represents a contemporary nature-inspired metaheuristic technique formulated through observations of Pyrrhura Molinae parrot behavioral patterns. PO exhibits effective optimization capabilities by achieving equilibrium between exploration and exploitation phases through mimicking foraging behaviors and social interactions. Nevertheless, during iterative progression, the algorithm encounters significant obstacles in preserving population diversity and experiences declining search effectiveness, resulting in early convergence and diminished capacity to identify optimal solutions within intricate optimization landscapes. To overcome these constraints, this work presents the Adaptive Differentiated Parrot Optimization Algorithm (ADPO), which constitutes a substantial enhancement over baseline PO through the implementation of three innovative mechanisms: Mean Differential Variation (MDV), Dimension Learning-Based Hunting (DLH), and Enhanced Adaptive Mutualism (EAM). The MDV mechanism strengthens the exploration capabilities by implementing dual-phase mutation strategies that facilitate extensive search during initial iterations while promoting intensive exploitation near promising solutions during later phases. Additionally, the DLH mechanism prevents premature convergence by enabling dimension-wise adaptive learning from spatial neighbors, expanding search diversity while maintaining coordinated optimization behavior. Finally, the EAM mechanism replaces rigid cooperation with fitness-guided interactions using flexible reference solutions, ensuring optimal balance between intensification and diversification throughout the optimization process. Collectively, these mechanisms significantly improve the algorithm’s exploration, exploitation, and convergence capabilities. Furthermore, ADPO’s effectiveness was comprehensively assessed using benchmark functions from the CEC2017 and CEC2022 suites, comparing performance against 12 advanced algorithms. The results demonstrate ADPO’s exceptional convergence speed, search efficiency, and solution precision. Additionally, ADPO was applied to wind power forecasting through integration with Long Short-Term Memory (LSTM) networks, achieving remarkable improvements over conventional approaches in real-world renewable energy prediction scenarios. Specifically, ADPO outperformed competing algorithms across multiple evaluation metrics, achieving average R² values of 0.9726 in testing phases with exceptional prediction stability. Moreover, ADPO obtained superior Friedman rankings across all comparative evaluations, with values ranging from 1.42 to 2.78, demonstrating clear superiority over classical, contemporary, and recent algorithms. These outcomes validate the proposed enhancements and establish ADPO’s robustness and effectiveness in addressing complex optimization challenges. Full article

Oat is a dual-purpose crop valued for both grain and forage. The stay-green (SG) trait, which delays leaf senescence and prolongs photosynthesis, has been shown to increase yield and quality in several crop species, yet its performance across diverse environments in oats remains underexplored. In this study, multi-location field trials were conducted in Ledu, Huangzhong and Haiyan, Qinghai Province, China, to comprehensively evaluate the performance of stay-green oat lines. The traits evaluated included grain yield components, nutritional quality, and seedling establishment traits. A TOPSIS (technique for order preference by similarity to an ideal solution) model, coefficient of variation (CV) and G × E (genotype × environment) visualization were used to assess adaptability, stability, and genotype × environment interactions. On average, the stay-green lines exhibited an 16.00% increase in plot yield and a 22.93% increase in thousand-grain weight compared to controls. Notable improvements were also observed in the starch (7.58% LN_SG in HZ and HY) and protein (3.58%, QY5_SG all the sites) contents, as well as multiple seedling establishment indices, with the seedling vigor indices increasing by more than 50%. Stability analysis further showed that the stay-green lines were stable in spike length, thousand-grain weight, water-soluble carbohydrates, and seed and seedling vigor. TOPSIS analysis identified ‘LN_SG’ as the top-performing and most adaptable genotype across all environments. Overall, stay-green oat lines demonstrated superior performance in grain yield, nutritional quality, and seedling establishment. These findings highlight their potential for field application and their value as parental materials in oat breeding programs enhancing environmental adaptability and stability. Full article

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

In recent years, passive acoustic monitoring (PAM) technology has significantly contributed to advancements in aquaculture techniques, system iterations, and increased production yields within intelligent feeding systems for Penaeus vannamei. However, current PAM-based intelligent feeding systems do not incorporate environmental factors into the decision process, limiting the improvement of monitoring accuracy in complex environments such as ponds. To establish a connection between environmental factors and the feeding acoustics of P. vannamei, this study utilized PAM technology combined with video analysis to investigate the effects of three key environmental factors—temperature, ammonia nitrogen, and nitrite nitrogen—on the feeding behavioral characteristics of shrimp, with a specific focus on acoustic signals “clicks”. The results demonstrated a significant correlation between the number of clicks and feed consumption in shrimp across different treatments, establishing this stable relationship as a reliable indicator for assessing shrimp feeding status. When water temperature increased from 20 °C to 32 °C, shrimp feed consumption showed an elevation from 0.46 g to 0.95 g per 30 min, with the average number of clicks increasing from 388 to 2947.58 and sound pressure levels rising accordingly. Conversely, ammonia nitrogen at 12 mg/L reduced feed consumption by 0.15 g and decreased click counts by 911.75 pulses compared to controls, while nitrite nitrogen at 40 mg/L similarly suppressed feed consumption by 0.15 g and the average number of clicks by 304.75. A rise in water temperature stimulated shrimp behaviors such as feeding, swimming, and foraging, while elevated concentrations of ammonia nitrogen and nitrite nitrogen significantly inhibited shrimp activity. Redundancy analysis revealed that temperature was the most prominent factor among the three environmental factors influencing shrimp feeding. This study is the first to quantify the specific effects of common environmental factors on the acoustic feeding signals and feeding behavior of P. vannamei using PAM technology. It confirms the feasibility of using PAM technology to assess shrimp feeding conditions under diverse environmental conditions and the necessity of integrating environmental monitoring modules into future feeding systems. This study provides behavioral evidence for the development of precise feeding technologies and the upgrade of intelligent feeding systems for P. vannamei. Full article

Modified nano-clays, alone or combined with probiotics, may offer a novel and sustainable approach to improve ruminal fermentation and mitigate CH₄ emissions in high-fiber diets. This study evaluated the properties and effects of modified nano-bentonite (MNB), with or without yeast (Saccharomyces cerevisiae), compared to natural bentonite (NB) and monensin, using the in vitro gas production (GP) technique. The substrate used was a basal diet composed primarily of forage (Trifolium alexandrinum clover) in a 70:30 forage-to-concentrate ratio. The treatments were a control group receiving the basal diet without additives; a monensin-added diet containing 40 mg/kg of dry matter (DM); a yeast-added diet with Saccharomyces cerevisiae at 2 × 10⁸ CFU/g of DM; a NB clay-added diet at 5 g/kg of DM; and MNB diets added at two levels (0.5 g/kg of DM (MNB_Low) and 1 g/kg of DM (MNB_High)), with or without S. cerevisiae. MNB showed a smaller particle size and improved properties, such as higher conductivity, surface area, and cation exchange capacity, than NB. Sulfur and related functional groups were detected only in MNB. No differences were observed in total GP, while both the monensin diet and the MNB_High-with-yeast diet significantly reduced CH₄ emissions compared to the control (p < 0.05). The MNB_High-without-yeast combination significantly (p < 0.05) reduced hemicellulose degradation, as well as total protozoal counts, including Isotricha and Epidinium spp. (p < 0.05), compared to the control. Ammonia levels did not differ significantly among treatments, while NB and MNB_High diets tended to have (p = 0.063) the highest short-chain fatty acid (SCFA) concentrations. These findings suggest the potential modulatory effects of yeast and MNB on rumen fermentation dynamics and CH₄ mitigation. Full article

Permanent grasslands play a crucial role in ruminant nutrition, providing cost-effective and nutritionally rich forage. Their effective management is essential for improving agricultural productivity and sustainability. This review examines factors affecting forage quality, including environmental conditions, botanical composition, conservation methods, and fertilization strategies. The impact of grassland management practices, such as cutting frequency, grazing systems, and soil fertility enhancement, on forage nutritional value is discussed. Advances in breeding, including genomic selection and molecular techniques, offer opportunities to improve digestibility and resistance to environmental stress. Furthermore, conservation methods, including haymaking and silage production, significantly influence forage quality. Special attention is given to the role of legumes and multi-species swards in enhancing protein content and mineral composition. The review highlights that optimizing forage quality requires an integrated approach, combining agronomic practices, genetic improvements, and sustainable management strategies. Future research should focus on developing resilient forage systems that maintain high nutritional value while adapting to changing climatic conditions. Full article

This paper offers an exhaustive review of various pivotal aspects of forage whole-plant maize silage. It commences with an exploration of the foundational elements of planting, including the growing environment, variety selection, planting techniques, management practices, and harvesting considerations. The paper assesses the nutritional value of maize silage, its effects on animal health, and its current applications in livestock farming. Additionally, it elucidates the principles of fermentation, pathogen control, and the impact of fermentation technology on silage quality. The paper also discusses utilization strategies and technological advancements. A historical perspective is provided, alongside an analysis of current challenges, opportunities, and the global market positioning of maize silage. Furthermore, the paper delves into future prospects by addressing sustainable development strategies, adaptation to climate change, and ethical and economic controversies. The primary aim is to serve as a comprehensive reference for further research, production practices, and industrial chain development in the domain of forage whole-plant maize silage. Full article

Cultivated pasture grasses contribute forage to more than 40% of cattle produced in 11 southern states in the USA. In recent years the increasing intoxication of cattle feeding on pasture grasses raised serious concerns about their palatability. While molecular and metagenomics techniques have revealed the great diversity of microbial composition and functional richness of the grass endosphere microbiome, meta-sequencing techniques enable us to gain a bird’s-eye view of all plant-associated microbiomes as a ‘holobiont’. Plant holobionts provide a more comprehensive approach where one can define the functions of microbial communities and feedback between the core and satellite microbiomes of a targeted host. In the near future we will be able to tailor our grasses and their endosphere microbiomes through the host-directed selection of a ‘modular microbiome’, leading to ‘plant enhanced holobionts’ as a microbiome-driven solution to managing the intoxication of pasture grasses in livestock. The present review aims to understand the potential co-relation between the endosphere microbiome community composition and mycotoxin production in forage grasses in the southern United States. Full article

Wire electrical discharge machining (WEDM), as a significant branch of non-traditional machining technologies, is widely applied in fields such as mold manufacturing and aerospace due to its high-precision machining capabilities for hard and complex materials. This paper systematically reviews the research progress in WEDM process optimization from two main perspectives: traditional optimization methods and artificial intelligence (AI) techniques. Firstly, it discusses in detail the applications and limitations of traditional optimization methods—such as statistical approaches (Taguchi method and response surface methodology), Adaptive Neuro-Fuzzy Inference Systems, and regression analysis—in parameter control, surface quality improvement, and material removal-rate optimization for cutting metal materials in WEDM. Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. Finally, this investigation looks ahead to the key application scenarios and development trends of AI techniques in the WEDM field for cutting metal materials. Full article