Search Results (149,181)

Path planning for multi-robot systems in complex dynamic environments is a key issue in autonomous robotics research. In response to the challenges posed by such environments, this paper proposes a dual-layer symmetric path planning algorithm that integrates an improved Glasius bio-inspired neural network (GBNN) and an enhanced dynamic window approach (DWA). This algorithm enables real-time obstacle avoidance for multi-robots in dynamic environments while effectively addressing robot-to-robot conflict issues. First, to address the low global optimization capability of the GBNN algorithm in the first layer, a signal waveform propagation model for single-neuron signals is established, enhancing the global optimization ability of the algorithm. Additionally, a path optimization function is developed to remove redundant points along the path, improving its efficiency. In the second layer, based on the global path, a reward function is introduced into the DWA. The Score function within the DWA algorithm is also modified to enable symmetric path adjustments, effectively reducing detour paths and minimizing the probability of deviation from the planned trajectory while ensuring real-time obstacle avoidance under the condition of maintaining the global path’s optimality. Next, to address conflicts arising from multi-robot encounters, a dynamic priority method based on distance is proposed. Finally, through multi-dimensional comparative experiments, the superiority of the proposed method is validated. Experimental results show that, compared with other algorithms, the improved neural network-DWA algorithm significantly reduces path length and the number of turns. This research contributes to enhancing the efficiency, adaptability, and safety of multi-robot systems. Full article

Monitoring stress in captive fish is crucial for their welfare, but continuous physiological measures in unrestrained animals are challenging. Rainbow trout (Oncorhynchus mykiss) exhibit divergent personalities, ranging from bold to shy, which correlate with cortisol-mediated stress responses. To determine whether personality affects the sympathetic nervous system, heart rate was measured during three potentially stressful events as a proxy for sympathetic nervous system responses. Firstly, trout were classified as bold or shy, using a novel object test. Subsequently, trout were implanted with biologgers to record heart rate in vivo at rest during and after the behavioral tests. Following recovery, the fish underwent a second novel object test, a confinement test, a pair-wise contest, and a final novel object test to explore the degree of boldness over the experimental period, which remained consistent. Heart rate was relatively higher in both bold and shy animals during the confinement test and the pair-wise contest compared with the novel object test, which indicated that heart rate monitoring was a valid gauge of the valence of the experience. Heart rate responses did not differ between bold and shy trout, indicating that behavioral phenotype did not influence the autonomic stress response. Thus, heart rate is a reliable indicator of stress without the need to account for intra-specific behavioral variations. Full article

Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information. Firstly, ordinary convolutional layers were used to extract shallow features of noise in the image. Then, a combination of two-branch networks with different and complementary structures was used to extract deep features from the noise information in the image to solve the problem of insufficient feature extraction by the single-branch network model. The upper branch network used densely connected blocks to extract local features of the noise in the image. The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. The enhanced features were then processed through the multiscale feature fusion module to obtain multiscale image feature information at different depths to obtain more robust fused features. Finally, the shallow features and deep features were summed using a long jump join and were processed through an ordinary convolutional layer and output to obtain a residual image. In this paper, Set12, BSD68, Set5, CBSD68, and SIDD are used as a test dataset to which different intensities of Gaussian white noise were added for testing and compared with several mainstream denoising methods currently available. The experimental results showed that this paper’s algorithm had better objective indexes on all test sets and outperformed the comparison algorithms. The method in this paper not only achieved a good denoising effect but also effectively retained the edge and texture information of the original image. The proposed method provided a new idea for the study of deep neural networks in the field of image denoising. Full article

This experiment aimed to investigate the effect of dietary supplementation of γ-aminobutyric acid (GABA) on the growth performance, immune response, and oxygen-transport-related factors of Gibel carp (Carassius auratus gibelio). An eight-week culturing experiment was designed with five experimental diets, with the actual GABA content being 368 mg/kg (G1, control group), 449 mg/kg (G2), 527 mg/kg (G3), 602 mg/kg (G4), and 675 mg/kg (G5). The results showed that the level of 527 mg/kg (G3) of GABA significantly increased the specific growth rate (SGR), weight gain rate (WGR), and final body weight (FBW) of Gibel carp, while the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), and glucose (GLU) were also increased significantly. In addition, 527 mg/kg (G3) and 602 mg/kg (G4) of GABA significantly increased the total antioxidant capacity (T-AOC). The mRNA expression of tnf-α, tgf-β, and il-10 was significantly increased at the level of 449 mg/kg (G2). In terms of oxygen-carrying capacity, the mRNA expression of epo, tf, tfr1, ho-1, and vegf was markedly increased at the level of 449 mg/kg (G2). In conclusion, dietary GABA supplementation can boost growth performance, enhance the immune system, and increase oxygen-carrying capacity in Gibel carp. Full article

Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a promising biomimetic approach to these challenges. However, the original MGO can experience premature convergence and limited exploration capabilities. This paper introduces an enhanced bio-inspired algorithm, termed crisscross moss growth optimization (CCMGO), which incorporates a crisscross (CC) strategy and a dynamic grouping parameter, further emulating the biological mechanisms of spore dispersal and resource allocation in moss. By mimicking the interwoven growth patterns of moss, the CC strategy facilitates improved information exchange among population members, thereby enhancing offspring diversity and accelerating convergence. The dynamic grouping parameter, analogous to the adaptive resource allocation strategies of moss in response to environmental changes, balances exploration and exploitation for a more efficient search. Key findings from rigorous experimental evaluations using the CEC2017 benchmark suite demonstrate that CCMGO consistently outperforms nine established metaheuristic algorithms across diverse benchmark functions. Furthermore, in a real-world application to a three-channel reservoir production optimization problem, CCMGO achieves a significantly higher net present value (NPV) compared to benchmark algorithms. This successful application highlights CCMGO’s potential as a robust and adaptable tool for addressing complex, real-world optimization challenges, particularly those found in resource management and other nature-inspired domains. Full article

Freshwater reservoirs serve as vital water sources for numerous residential areas. However, the excessive presence of nutrients, such as nitrogen and phosphorus, stimulates rapid algal proliferation, leading to the occurrence of algal blooms. To prevent this phenomenon, it is imperative to conduct regular ecological surveys aimed at assessing water quality and monitoring the dynamic composition of aquatic biological communities within the reservoir’s ecosystem. In this study, seasonal changes in water quality parameters and the spatial and temporal distribution of planktonic algae at 14 sampling sites in the Danjiangkou reservoir were analyzed. A total of 136 taxonomic units of planktonic algae were identified, belonging to 8 phyla, 41 families, and 88 genera, with the dominant algae belonging to the phyla Chlorophyta, Bacillariophyta, and Cyanophyta. The order of abundance of the algae was summer > autumn > spring > winter and Hanku > Intake > Danku > Outflow. WT, pH, DO, COD_Mn, and Chl a were the primary drivers influencing the changes in the planktonic algal community within the reservoir. Two dominant algae, Chlamydomonas debaryana and Scenedesmus quadricauda, were isolated and cultured indoors to simulate the growth behaviors of algae in the Danjiangkou reservoir. The results show that the growth of C. debaryana was severely limited by the temperature, light, and nutrient concentration, whereas the growth of S. quadricauda was slightly affected under different temperature and light conditions and could occur at low concentrations of nitrogen and phosphorus nutrients. With excess nutrient levels, excessive proliferation of S. quadricauda could potentially cause algal blooms. This study examined the growth characteristics of the dominant algae in the Danjiangkou reservoir under laboratory conditions and delved into their interdependencies with environmental factors, aiming to furnish a theoretical and experimental foundation for investigating algal community dynamics and preventing algal blooms within the freshwater reservoir. Full article

Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain–computer interface (MI–BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial. Objectives: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks. Methods: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification. Results: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively. Conclusions: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness. Full article

Aquaculture is the fastest-growing food industry worldwide because it allows faster intensive production in a limited space and short time. However, the trade-off of this production scheme has led to infectious disease outbreaks that harm food production with economic impacts. Immunostimulants have entered the industry to fight against diseases by enhancing the immune system and conferring better protection against pathogens. In this regard, dietary immunostimulants have been tested at the farm level, such as carbohydrates and proteins known to enhance immunity and improve survival rates under experimental conditions. Despite the success of immunostimulant inclusion in experimental aquaculture, economic evaluation is an innovative avenue to be integrated when a novel immunostimulant is developed. The bioeconomic analysis permits the accurate demonstration of advantages or disadvantages regarding survival and yield performance upon immunostimulant application through mathematical and statistical estimates. An integrative bioeconomic model for testing a novel immunostimulant should contemplate technological, biological, and economic submodels at least; thus, financial variables, such as revenue, costs, and profitability, should also be considered for proper decision-making. Therefore, this perspective briefly describes the most relevant immunostimulants used in shrimp farms and offers bioeconomic elements that should be considered for affordable immunostimulant development and inclusion in shrimp aquaculture. Full article

Underwater acoustic (UWA) communication encounters significant challenges, including impulsive noise from breaking waves and marine organisms, as well as long-delay taps caused by ocean properties and high transmission rates. To address these issues, we enhance the channel estimation process by introducing iteratively reweighted least squares (IRLS) methods and propose an impulsive noise suppression algorithm. Furthermore, we analyze the inter-frequency interference (IFI) resulting from channel variability and implement IFI cancellation (IFIC) during iterative processing. Furthermore, an IFIC-based dual decision–feedback equalization (DDFE) algorithm is proposed for fast time-varying channels, enabling a considerable reduction in channel length and subsequent equalizer complexity. The proposed IFIC-based DDFE algorithm with impulsive noise suppression has been validated through sea trial data, demonstrating robustness against impulsive noise. Experimental results indicate that the proposed algorithm reduces click signal energy and significantly improves receiver performance compared to traditional DDFE algorithms. This research highlights the effectiveness of adapted UWA communication strategies in environments characterized by impulsive noise and long delay taps, facilitating more reliable UWA communication. Full article

To investigate the differences in seismic performance of buckling-restrained braces (BRBs) with significantly different lengths and to explore the influence of length on the energy dissipation efficiency of BRBs within the same structure, this study designed and fabricated two BRBs with lengths of 8.5 m and 3.5 m based on an actual engineering project. Low-cycle reciprocating load tests were conducted to compare the performance of the two BRBs in terms of hysteretic energy dissipation capacity, tension–compression bearing capacity imbalance coefficient, cumulative plastic deformation capacity, and low-cycle fatigue life. Additionally, the energy dissipation and damping efficiency of BRBs of different lengths within the same structure was analyzed. The results indicate that under cyclic loading based on design displacement, the 8.5 m BRB exhibits a greater equivalent viscous damping ratio, cumulative hysteretic energy dissipation, and cumulative plastic deformation, leading to more efficient energy dissipation and damping effects. The length of the brace is a significant factor affecting the imbalance coefficient of tension–compression bearing capacity, with longer braces resulting in a larger imbalance coefficient. The 3.5 m BRB shows less deviation from the mean values of various fatigue parameters, indicating more stable low-cycle fatigue performance. Within the same structure, shorter BRBs with larger design displacements achieve higher energy dissipation efficiency, allowing for more effective utilization of their energy dissipation capacity. This study’s conclusions provide valuable references for designers in the rational selection of BRBs of different lengths in actual engineering projects and offer preliminary insights into the energy dissipation efficiency of BRBs of varying lengths within a structure. Full article

This study explores the tensile performance of blind rivet joints in galvanized steel sheets, focusing on their behavior under shear and normal load conditions. Blind rivets are frequently used in structural applications due to their ease of installation and ability to be applied from one side, making them highly effective in industries like aerospace and automotive. Two types of DIN 7337—4.8 × 8 blind rivets—galvanized steel St/St and stainless steel A2/A2—paired with galvanized steel sheets DX51D + Z275, were experimentally tested to assess how their material properties affect their joint strength, deformation patterns, and failure modes. Single-lap shear, double-lap shear, and pure normal load tests were conducted in multiple configurations to evaluate joint performance under varying loading conditions, simulating real-world stresses. Using custom-built equipment, controlled forces were applied perpendicular to the rivet joints to replicate practical loading conditions. The results revealed distinct differences in the load-bearing capacities of the two materials, offering valuable insights for applications where corrosion resistance and structural integrity are critical. Finite element analysis (FEA) was then used to simulate the behavior of the joints, with the results validated against experimental data. To enhance the reliability of numerical simulations in optimizing the design of rivet joints, a methodology was proposed to calibrate non-linear FEA models to experimental results, and a substantial agreement of 92.53% was achieved via optimization in ANSYS OptiSLang. This research contributes to our broader understanding of riveted connections, providing practical recommendations for assessing the performance of such joints in various engineering fields. Full article

Arbitrary translational Six Degrees of Freedom (6DoF) video represents a transitional stage towards immersive terminal videos, allowing users to freely switch viewpoints for a 3D scene experience. However, the increased freedom of movement introduces new distortions that significantly impact human visual perception quality. Therefore, it is crucial to explore quality assessment (QA) to validate its application feasibility. In this study, we conduct subjective and objective QAs of arbitrary translational 6DoF videos. Subjectively, we establish an arbitrary translational 6DoF synthesized video quality database, specifically exploring path navigation in 3D space, which has often been limited to planar navigation in previous studies. We simulate path navigation distortion, rendering distortion, and compression distortion to create a subjective QA database. Objectively, based on the spatio-temporal distribution characteristics of various distortions, we propose a no-reference video quality assessment (VQA) metric for arbitrary translational 6DoF videos. The experimental results on the established subjective dataset fully demonstrate the effectiveness and superiority of the proposed objective method. Full article

In this work, four different experimental assessment approaches, namely, the Tissue Softness Analyzer (TSA), a Subjective Evaluation (SUB), the Kawabata Evaluation System (KES), and an Optical System (OPT), were used for the evaluation of softness on a set of 29 different tissue paper products. After processing and the interpretation of the results given by each one of the used methods, a procedure was implemented in the current work to make a comparison between them. The procedure consists in tracking the position of the tissue paper products on a ranking table, regardless of what values were obtained through each one of the four used methods independently. This comparison revealed to be very useful in determining the differences verified between methods allowing to conclude which ones were the least and the most concordant, and, at the same time, enabling us to identify interesting cases of tissue paper products on the set that caught our attention for their distinctive characteristics. Full article

Oral squamous cell carcinoma (OSCC) is one the most prevalent head and neck cancers and represents a major cause of morbidity and mortality worldwide. The main established risk factors for OSCC include tobacco and alcohol consumption and betel quid chewing, which may contribute alone or in combination with other environmental factors to carcinogenesis. The oral microbiota is emerging as a key player in the establishment of the molecular and cellular mechanisms that may trigger or promote carcinogenesis, including in the oral cavity. Among the bacterial species found in the oral microbiota, Fusobacterium nucleatum, an anaerobic bacterium commonly found in oral biofilms and a periodontal pathogen, has gained attention due to solid evidence implicating F. nucleatum in colorectal cancer (CRC). F. nucleatum has been shown to induce chronic inflammation, promote cell proliferation and trigger cellular invasion while deploying immune evasion mechanisms. These experimental findings were first obtained in in vitro and in vivo models of CRC and are being confirmed in studies on OSCC. In this review, we summarize the most recent findings on the role of F. nucleatum in OSCC, discuss the clinical implications in terms of prognosis and provide an overview of the key mechanisms involved. Moreover, we identify research questions and aspects that require investigations to clarify the role of F. nucleatum in OSCC. We anticipate that studies in this emerging field may have a significant clinical impact on the diagnosis, prognosis and management of OSCC. Full article

The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2–5.25%), gelatin (2–5.25%), and TEMPO-Nano fibrillated cellulose (0.5–1%) at shear rates from 0.1 to 100 s⁻¹. Our study analyzed 169 rheological measurements using 80% training and 20% validation data. The results, based on the coefficient of determination (R2) and mean absolute error (MAE), showed that the RF algorithm-based model performed best: [(R2, MAE) RF = (0.99, 0.09), (R2, MAE) PF = (0.95, 0.28), (R2, MAE) DT = (0.98, 0.13)]. These predictive models serve as valuable tools for bioink formulation optimization, allowing researchers to determine effective viscosities without extensive experimental trials to accelerate tissue engineering. Full article