Search Results (237)

The hot stamping process of aluminum alloys involves multiple parameters, including blank holder force, stamping speed, die temperature, and friction coefficient. Traditional methods often fail to capture the nonlinear interactions among these parameters. This study proposes an optimization framework that integrates BP neural networks with genetic algorithms (GA), while six bio-inspired algorithms—Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Crested Porcupine Optimizer (CPO), Grey lag Goose Optimization (GOOSE), Dung Beetle Optimizer (DBO), and Parrot Optimizer (PO)—were employed to optimize the network hyperparameters. Comparative results show that all optimized models outperformed the baseline BP model (R² = 0.702, RMSE = 0.106, MAPE = 20.8%). The PO-BP achieved the best performance, raising R² by 27.3% and reducing MAPE by 27.1%. Furthermore, combining GA with the PO-BP model yielded optimized process parameters, reducing the maximum thinning rate to 17.0% with only a 1.16% error compared with experiments. Overall, the proposed framework significantly improves prediction accuracy and forming quality, offering an efficient solution for rapid process optimization in intelligent manufacturing of aluminum alloy automotive parts. Full article

I recently developed the concept of eco-cognitive openness and situatedness to explain how cognitive systems, whether human or artificial, engage dynamically with their surroundings to generate information and creative outcomes through abductive cognition. Human cognition demonstrates significant eco-cognitive openness, utilizing external resources like tools and cultural contexts to produce contextually rich hypotheses, sometimes highly creative via what I called “unlocked strategies.” Conversely, generative AI, such as large language models (LLMs) and image generators, employs “locked strategies,” relying on pre-existing datasets with minimal real-time environmental interaction—this leads to limited creativity. While these systems can yield some low-level degrees of creative outputs, their lack of human-like eco-cognitive openness restricts their ability to achieve high-level creative abductive feats, which remain a human strength, especially among the most talented. However, LLMs often outperform humans in routine cognitive tasks, exposing human intellectual limitations rather than AI deficiencies. Much human cognition is repetitive and imitative, resembling “stochastic parrots,” much like LLMs. Thus, LLMs are potent cognitive tools that can enhance human performance but also endanger creativity. Future AI developments, such as human–AI partnerships, could improve eco-cognitive openness, but risks like bias and overcomputationalization necessitate human oversight to ensure meaningful results. In collaborative settings, generative AI can serve as an epistemic mediator, narrowing the gap toward unlocked creativity. To safeguard human creativity, control over AI output must be maintained, embedding them in socio-cultural contexts. I also express concern that ethical and legal frameworks to mitigate AI’s negative impacts may fail to be enforced, risking “ethics washing” and “law washing.” Full article

Atherosclerosis is highly prevalent among captive psittacine populations and is a frequent cause of veterinary consultations. Ante-mortem diagnosis remains challenging, but the serum lipoprotein analysis has been suggested as a useful tool for identifying associated risk factors and improving understanding of its pathogenesis. Unlike in humans, the relationship between lipoproteins and atherosclerosis in parrots has not been clearly established. This retrospective cohort study analyzed n = 1199 blood samples from 692 parrots across 14 genera to establish reference intervals for high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) following ASVCP guidelines. Lipoprotein levels were evaluated in relation to factors such as genus, age, sex, diet, reproductive status, body condition score, and atherosclerosis prevalence (diagnosed by endoscopy and/or necropsy). The results demonstrated genus-specific differences and significant associations between LDL-C and atherosclerosis, with non-HDL-C showing a similar, less pronounced, trend. Higher LDL-C values were measured in the presence of moderate-severe atherosclerosis. Birds on seed diets had higher lipoprotein levels and were more likely to be diagnosed with atherosclerosis in comparison to birds fed a pelleted or extruded diet. The role of HDL-C remained less conclusively defined. The results of this study provide a foundational framework for the future use of lipoprotein analysis in parrot medicine, offering novel insights into the management of cardiovascular health in pet parrots. 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

Vegetation and soil indices are able to indicate patterns of gradual plant growth. Therefore, productivity data may be used to predict performance in the development of pastures prior to grazing, since the morphology of the pasture follows repetitive cycles through the grazing of animals. Accordingly, in recent decades, much attention has been paid to the monitoring and development of vegetation by means of remote sensing using remote sensors. The current study seeks to determine the differences between three remote sensing products in the monitoring and development of white clover and perennial ryegrass ratios. Various grass and legume associations (perennial ryegrass, Lolium perenne, and white clover, Trifolium repens) were evaluated in different proportions to determine their yield and relationship through vegetation and soil indices. Four proportions (%) of perennial ryegrass and white clover were used, being 100:0; 90:10; 80:20 and 70:30. Likewise, to obtain spectral indices, a Spectral Evolution PSR-1100 spectroradiometer was used, and two UAVs with a MAPIR 3W RGNIR camera and a Parrot Sequoia multispectral camera, respectively, were employed. The data collection was performed before and after each cut or grazing period in each experimental unit, and post-processing and the generation of spectral indices were conducted. The results indicate that there were no significant differences between treatments for yield or for vegetation indices. However, there were significant differences in the index variables between sensors, with the spectroradiometer and Parrot obtaining similar values for the indices both pre- and post-grazing. The NDVI values were closely correlated with the yield of the forage proportions (R² = 0.8948), constituting an optimal index for the prediction of pasture yield. Full article

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article

In this paper we explore ChatGPT’s ability to produce a summary, a precis, and/or an essay on the basis of excerpts from a novel—The Solid Mandala—by Nobel Prize Australian writer Patrick White. We use a number of prompts to test a number of functions related to narrative analysis from the point of view of the “sujet”, the “fable”, and the style. In the paper, we illustrate extensively a number of recurrent semantic mistakes that can badly harm the understanding of the contents of the novel. We made a list of 12 different types of semantic mistakes or parrotting we found GPT made, which can be regarded as typical for stochastic-based generation. We then tested Gemini for the same 12 mistakes and found a marked improvement in all critical key issues. The conclusion for ChatGPT is mostly negative. We formulate an underlying hypothesis for its worse performance, the influence of vocabulary size, which in Gemini is seven times higher than in GPT. Full article

There has been a great need for replacing combustion-powered vehicles with electric vehicles (EV), and fully electric cars are meant to replace combustion engine cars. This has led to considerable research into improving the performance of EVs, especially via electric motor voltage control. A wide range of optimization algorithms have been used as traditional approaches, but the dynamic parameters of electric motors, impacted by temperature and different driving cycles, continue to be a problem. This study introduces an improved version of the Parrot Optimizer (IPO) aimed at enhancing voltage regulation in EVs. The algorithm can intelligently adjust certain motor parameters for adaptive management to maintain performance based on different situations. To ensure a stable and sustainable power supply for the powertrain of the EV, a photovoltaic (PV) system is used with energy storage batteries. Such an arrangement seeks to deliver permanent electric energy, a solution to traditional grid electricity reliance. This demonstrates the effectiveness of IPO, with the resultant motor performance remaining optimal despite parameter changes. It is also illustrated that energy production, by integrating PV systems, prevents excessive voltage line drops and thus voltage imbalances. The proposed intelligent controller is verified based on multiple simulations, demonstrating and ensuring significant improvements in EV efficiency and reliability. Full article

Background/Objectives: Beak and feather disease virus (BFDV) is the causative agent of psittacine beak and feather disease (PBFD), affecting psittacine birds. There is currently no commercial vaccine or treatment for this disease. This study developed a novel BFDV coat protein mRNA vaccine encapsidated by TMV coat protein to form pseudovirions (PsVs) and tested its immunogenicity alongside BFDV coat protein (CP) subunit and DNA vaccine candidates. Methods: mRNA and BFDV CP subunit vaccine candidates were produced in Nicotiana benthamiana and subsequently purified using PEG precipitation and gradient ultracentrifugation, respectively. The DNA vaccine candidate was produced in E. coli cells harbouring a plasmid with a BFDV1.1mer pseudogenome. Immunogenicity of the vaccine candidates was evaluated in African grey parrot chicks. Results: Successful purification of TMV PsVs harbouring the mRNA vaccine, and of the BFDV-CP subunit vaccine, was confirmed by SDS-PAGE and western blot analysis. TEM analyses confirmed formation of TMV PsVs, while RT-PCR and RT-qPCR cDNA amplification confirmed encapsidation of the mRNA vaccine candidate within TMV particles. Restriction digests verified presence of the BFDV1.1mer genome in the plasmid. Four groups of 5 ten-week-old African grey parrot (Psittacus erithacus) chicks were vaccinated and received two boost vaccinations 2 weeks apart. Blood samples were collected from all four groups on day 14, 28 and 42, and sera were analysed using indirect ELISA, which showed that all vaccine candidates successfully elicited specific anti-BFDV-CP immune responses. The subunit vaccine candidate showed the strongest immune response, indicated by higher binding titres (>6400), followed by the mRNA and DNA vaccine candidates. Conclusions: The candidate vaccines present an important milestone in the search for a protective vaccine against PBFD, and their inexpensive manufacture could considerably aid commercial vaccine development. Full article

The South Caspian Basin of Iran (SCBI), a vital ecosystem for unique and valuable fish species, is under severe threats due to anthropogenic activities that are rapidly deteriorating its fish biodiversity. The initial step to effectively combat or mitigate threats to biodiversity is to precisely identify these threats. While such threats are often categorized qualitatively, there is a lack of a comparative quantitative assessment of their severity. This means that although we may have a general understanding of the threats, we do not have a clear picture of how serious they are relative to one another. This study aimed to quantify and prioritize these threats using a modified quantitative “SWOT” (Strengths, Weaknesses, Opportunities, Threats) analysis. Twenty multidisciplinary experts identified and evaluated 26 threats, and we used multivariate cluster analysis to categorize them as “High”, “Medium”, and “Low” based on their quantitative contributions to overall threat. Invasive non-native species and global warming emerged as the most significant threats, followed by resource exploitation, habitat destruction, and pollution. We then used this information to develop a “Situation Model” and “Results Chains” to guide responses to the threats. According to the Situation Model, these threats are interconnected, driven by factors such as population growth, unsustainable resource use, and climate change. To address these challenges, we propose the Results Chains, including two strategies focused on scientific research, land-use planning, public awareness, and community engagement. Prioritizing these actions is crucial for conserving the Caspian Sea’s unique fish fauna and ensuring the region’s ecological and economic sustainability. Full article

This paper addresses the problem of controlling quadcopters operating in an environment characterized by unpredictable disturbances such as wind gusts. From a control point of view, this is a nonstandard, highly challenging problem. Fundamentally, these quadcopters are high-order dynamical systems characterized by an under-actuated and highly nonlinear model with coupling between several state variables. The main objective of this work is to achieve a trajectory by tracking desired altitude and attitude. The problem was tackled using a robust control approach with a multi-loop nonlinear controller combined with extended Kalman filtering (EKF). Specifically, the flight control system consists of two regulation loops. The first one is an outer loop based on the backstepping approach and allows for control of the elevation as well as the yaw of the quadcopter, while the second one is the inner loop, which allows the maintenance of the desired attitude by adjusting the roll and pitch, whose references are generated by the outer loop through a standard PID, to limit the 2D trajectory to a desired set path. The investigation integrates EKF technique for sensor signal processing to increase measurements accuracy, hence improving robustness of the flight. The proposed control system was formally developed and experimentally validated through indoor tests using the well-known Parrot Mambo unmanned aerial vehicle (UAV). The obtained results show that the proposed flight control system is efficient and robust, making it suitable for advanced UAV navigation in dynamic scenarios with disturbances. Full article

Gravitational wave (GW) signals are often affected by noise interference in the detection system; in order to attenuate the impact of detector noise and enhance the waveform characteristics of the signal, this paper proposes a space-based GW signal denoising method that combines the Parrot algorithm (PO) with the improved wavelet threshold (IWT) to optimize the variational mode decomposition (VMD). To address the challenge of selecting the number of modes K and the penalty factor $\alpha$ in VMD, PO is introduced to select the optimal parameters, achieving a good balance between global search and local optimization. The components after modal decomposition are divided into preserved modal components and noise modal components, and the IWT is introduced to further denoise the noise modal components; finally, the signal is reconstructed to achieve the purpose of denoising the GW signal. The algorithm is verified by the GW simulation signal and the measured signal. The experimental results show that the algorithm is superior to other algorithms in the noise separation of GW signals, significantly improves the SNR, improves the detection accuracy of GW, and provides a new technical means for the extraction and analysis of GW signals. Full article

A lack of awareness among exotic bird owners regarding the quality of feed may contribute to adverse health outcomes, including toxicosis, systemic mycoses, and potentially neoplastic processes. Fungi of the Aspergillus genus are the most pathogenic to avian species, particularly due to their involvement in respiratory diseases such as aspergillosis, which affects the air sacs. This study aims to assess the presence of Aspergillus spp. in commercially available parrot feed (grain mixtures) available on the Polish pet market, considering different price categories. A total of 22 dry parrot food samples were analyzed using the PN-ISO 21527-2:2009 protocol. Aspergillus spp. colonies were isolated from 16 out of 22 samples (72.7%), indicating a high incidence of contamination. Although these results are preliminary, they highlight a microbiological risk associated with grain-based parrot feeds and underscore the need for stricter quality control and greater awareness among pet owners and manufacturers. Full article

Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R² values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. Full article

This research describes a multi-objective trajectory planning method for robotic arms based on time, energy, and impact. The quintic Non-Uniform Rational B-Spline (NURBS) curve was employed to interpolate the trajectory in joint space. The quintic NURBS interpolation curve can make the trajectory become constrained within the kinematic limits of velocity, acceleration, and jerk while also satisfying the continuity of jerk. Then, based on the Parrot Optimization (PO) algorithm, through improvements to reduce algorithmic randomness and the introduction of appropriate multi-objective strategies, the algorithm was extended to the Multi-Objective Parrot Optimization (MOPO) algorithm, which better balances global search and local convergence, thereby more effectively solving multi-objective optimization problems and reducing the impact on optimization results. Subsequently, by integrating interpolation curves, the multi-objective optimization of joint trajectories could be performed under robotic kinematic constraints based on time–energy-jerk criteria. The obtained Pareto optimal front can provide decision-makers in industrial robotic arm applications with flexible options among non-dominated solutions. Full article