Search Results (1,616)

Discrimination between normal (fresh/non-frozen) and frozen-thawed beef is crucial for ensuring food safety. This paper proposed a novel, non-destructive and real-time you only look once for normal and frozen-thawed beef discrimination (YOLO-NF) model using deep learning techniques. The simple, parameter-free attention module (SimAM) and the squeeze and excitation (SE) attention mechanism were introduced to enhance the model’s performance. A total of 1200 beef samples were used, with their images captured by a charge-coupled device (CCD) camera. In the model development, specifically, the training set comprised 3888 images after data augmentation, while the validation set and test set each included 216 original images. Experimental results on the test set showed that the YOLO-NF model achieved precision, recall, F1-Score and mean average precision (mAP) of 95.5%, 95.2%, 95.3% and 98.6%, respectively, significantly outperforming YOLOv7, YOLOv5 and YOLOv8 models. Additionally, gradient-weighted class activation mapping (Grad-CAM) was adopted to interpret the model’s decision basis. Moreover, the model was deployed on the web interface for user convenience, and the discrimination time on the local server was 0.94 s per image, satisfying the requirements for real-time processing. This study provides a promising technique for high-performance and rapid meat quality assessment in food safety monitoring systems. Full article

The corrosion behavior of open-cell AlSn6Cu-based composites, one reinforced with SiC particles and the other with Al₂O₃ particles, was investigated. The composites were fabricated via liquid-state processing, employing both squeeze casting and the replication method, and they produced in two distinct pore size ranges (800–1000 µm and 1000–1200 µm). Corrosion performance was systematically evaluated through gravimetric (weight loss) measurements and electrochemical techniques, including open-circuit potential monitoring and potentiodynamic polarization tests. Comprehensive microstructural and phase analyses were conducted using X-ray diffraction, energy-dispersive X-ray spectroscopy, and scanning electron microscopy. The results revealed that both reinforcement type and pore architecture have a significant impact on corrosion resistance. Al₂O₃-reinforced composites consistently outperformed their SiC-containing counterparts, and pore enlargement generally improved performance for the unreinforced alloy and the Al₂O₃ composite but not for the SiC composite. Overall, the optimal corrosion resistance is achieved by pairing a coarser-pore architecture (1000–1200 µm) with Al₂O₃ reinforcement, which minimizes both instantaneous (electrochemical) and cumulative (gravimetric) corrosion metrics. This study addresses a gap in current research by providing the first detailed assessment of corrosion in open-cell AlSn6Cu-based composites with controlled pore architectures and different ceramic reinforcements, offering valuable insights for the development of advanced lightweight materials for harsh environments. Full article

Ensuring the surface quality of fresh soybeans is critical for maintaining their commercial value and consumer confidence. However, traditional manual inspection remains labor-intensive, subjective, and inadequate for real-time, high-throughput sorting. In this study, we present a multi-scale attention-augmented You Only Look Once version 8 (YOLOv8) framework tailored for real-time surface defect detection in fresh soybeans. The proposed model integrates two complementary attention mechanisms—Squeeze-and-Excitation (SE) and Multi-Scale Dilated Attention (MSDA)—to enhance the detection of small, irregular, and low-contrast defects under complex backgrounds. Rather than relying on cross-model comparisons, we perform systematic ablation studies to evaluate the individual and combined contributions of SE and MSDA across diverse defect categories. Experimental results from a custom-labeled soybean dataset demonstrate that the integrated SE+MSDA model achieves superior performance in terms of precision, recall, and Mean Average Precision (mAP), particularly for challenging categories such as wormholes and speckles. The proposed framework provides a lightweight, interpretable, and deployment-ready solution for intelligent agricultural inspection, with potential applicability to broader food quality control tasks. Full article

The Necrópolis de Wari Kayan, at the Paracas site in the coastal desert of south–central Peru, is a large archeologically excavated mortuary complex with fine textile preservation, dated approximately to 2000 BP. This study investigates loose yarns associated with textiles from Wari Kayan tomb 12 (bundle 382), collected by the late Dr. Anne Paul in 1985 at what is now the Museo Nacional de Arqueología Antropología e Historia del Perú (MNAAHP). Sequencing multiple state-of-the-art analyses, including direct analysis in real time mass spectrometry (DART-MS), high performance liquid chromatography (HPLC) with diode array detection, and accelerator mass spectrometry, on the same small sample, seeks to “squeeze out every drop” of information. Six mantles from the outer layer include different sets of color hues and values, representing either different time periods or different producer groups. Plasma oxidation at low temperature (<100 °C) prepared carbon dioxide for AMS radiocarbon analysis. Fibers remaining after oxidation were combusted for light-stable isotope analysis. The sequential analysis results in fiber and dye composition, radiocarbon age, and stable isotope fractionation values may suggest fiber origin, continuing and updating a project started over 40 years ago. Full article

In clinical medicine, rats are commonly used as experimental subjects. However, their estrous cycle significantly impacts their biological responses, leading to differences in experimental results. Therefore, accurately determining the estrous cycle is crucial for minimizing interference. Manually identifying the estrous cycle in rats presents several challenges, including high costs, long training periods, and subjectivity. To address these issues, this paper proposes a classification network, Spatial Long-distance EfficientNet (SLENet). This network is designed based on EfficientNet, specifically modifying the Mobile Inverted Bottleneck Convolution (MBConv) module by introducing a novel Spatial Efficient Channel Attention (SECA) mechanism to replace the original Squeeze Excitation (SE) module. Additionally, a non-local attention mechanism is incorporated after the last convolutional layer to enhance the network’s ability to capture long-range dependencies. On 2655 microscopy images of rat vaginal epithelial cells (with 531 test), SLENet achieves 96.31% accuracy, surpassing EfficientNet (94.20%). This finding provides practical value for optimizing experimental design in rat-based studies such as reproductive and pharmacological research, but this study is limited to microscopy image data, without considering other factors; thus, future work could incorporate temporal pattern and multi-modal inputs to further enhance robustness. Full article

Using Bayesian inference to calibrate constitutive model parameters has recently seen a rise in interest. The Markov chain Monte Carlo (MCMC) algorithm is one of the most commonly used methods to sample from the posterior. However, the choice of which MCMC algorithm to apply is typically pragmatic and based on considerations such as software availability and experience. We compare three commonly used MCMC algorithms: Metropolis-Hastings (MH), Affine Invariant Stretch Move (AISM) and No-U-Turn sampler (NUTS). For the comparison, we use the Kullback-Leibler (KL) divergence as a convergence criterion, which measures the statistical distance between the sampled and the ‘true’ posterior. We apply the Bayesian framework to a Newtonian squeeze flow problem, for which there exists an analytical model. Furthermore, we have collected experimental data using a tailored setup. The ground truth for the posterior is obtained by evaluating it on a uniform reference grid. We conclude that, for the same number of samples, the NUTS results in the lowest KL divergence, followed by the AISM sampler and last the MH sampler. Full article

This paper presents three simple and efficient advanced optimization algorithms, namely the best–worst–random (BWR), best–mean–random (BMR), and best–mean–worst–random (BMWR) algorithms designed to address unconstrained and constrained single- and multi-objective optimization tasks of the metal casting processes. The effectiveness of the algorithms is demonstrated through real case studies, including (i) optimization of a lost foam casting process for producing a fifth wheel coupling shell from EN-GJS-400-18 ductile iron, (ii) optimization of process parameters of die casting of A360 Al-alloy, (iii) optimization of wear rate in AA7178 alloy reinforced with nano-SiC particles fabricated via the stir-casting process, (iv) two-objectives optimization of a low-pressure casting process using a sand mold for producing A356 engine block, and (v) four-objectives optimization of a squeeze casting process for LM20 material. Results demonstrate that the proposed algorithms consistently achieve faster convergence, superior solution quality, and reduced function evaluations compared to simulation software (ProCAST, CAE, and FEA) and established metaheuristics (ABC, Rao-1, PSO, NSGA-II, and GA). For single-objective problems, BWR, BMR, and BMWR yield nearly identical solutions, whereas in multi-objective tasks, their behaviors diverge, offering well-distributed Pareto fronts and improved convergence. These findings establish BWR, BMR, and BMWR as efficient and robust optimizers, positioning them as promising decision support tools for industrial metal casting. Full article

Text-dependent speaker verification (TD-SV), which verifies speaker identity using predefined phrases, has gained attention as a reliable contactless biometric authentication method for smart devices, internet of things (IoT), and real-time applications. However, in real-world environments, limited training data, background noise, and microphone channel variability significantly degrade TD-SV performance, particularly on resource-constrained devices that require real-time inference. To address these challenges, we propose a lightweight end-to-end TD-SV model based on a convolution-augmented transformer (Conformer) architecture enhanced with a channel-temporal attention (CTA) module as an input enhancement that specifically targets speaker-discriminative patterns in short, fixed utterances. Unlike existing attention mechanisms (Squeeze-and-Excitation Networks (SENet), Convolutional Block Attention Module (CBAM)) designed for computer vision tasks, our CTA module employs frequency-wise statistical pooling to capture acoustic variability patterns crucial for speaker discrimination within identical phonetic content. Experiments conducted on the challenging far-field and noisy SLR 85 HI-MIA dataset demonstrate that the proposed CTA-Conformer achieves an equal error rate (EER) of 2.04% and a minimum detection cost function (minDCF) of 0.20, achieving competitive performance compared to recent TD-SV approaches. Additionally, INT8 quantization reduces the model size by 75.8%, significantly improves inference speed, and enabling real-time deployment on edge devices. Our approach thus offers a practical solution for robust and efficient TD-SV in embedded internet of things (IoT) environments. Full article

Animal temperament, defined as consistent behavioral and physiological responses to stressors, plays a crucial role in cattle welfare, productivity, and safety during handling. This motivates researchers to identify objective, non-invasive methods for temperament assessment. Infrared thermography (IRT) has emerged as a promising tool to detect superficial temperature changes associated with stress and temperament in cattle. This study aimed to evaluate how superficial temperature variations measured by IRT in fattening bulls are influenced by environmental temperature, humidity, and temperament. The study involved 223 bulls at approximately 7.5 months old, while thermal images of eye and muzzle regions were captured at baseline and during restraint in a squeeze chute. Temperament was assessed using chute score and flight time, and environmental conditions were recorded via a temperature–humidity index (THI). Results showed significant increases in eye and muzzle temperatures during handling. Notably, changes in eye temperature were independent of environmental THI but correlated with flight time, with more temperamental bulls displaying larger temperature increases. In contrast, changes in muzzle temperature were strongly influenced by ambient THI and its variation at handling, consistent with the region’s thermoregulatory function. Temperament explained a small proportion of temperature variation. A follow-up experiment on a subset of 104 bulls around 11 months old showed no significant age effects on the IRT–temperament relationship. These findings indicate that IRT, particularly of the eye region, holds promise as a non-invasive, objective method to assess stress responses related to temperament in cattle. Careful selection of thermal regions and accounting for environmental influences are critical. While IRT alone accounts for limited variability, its integration with other behavioral and physiological measures could enhance temperament evaluation. This approach offers novel opportunities for improving animal welfare and management by identifying highly temperamental individuals without invasive procedures. Future research with higher temporal resolution and varied stressors is warranted to further elucidate temperature dynamics associated with temperament. Full article

In this study, a novel casting–forging hybrid forming technique, introducing torsional shear during squeeze casting, was investigated. This approach enhances the forming efficiency and refines the grain size. Using a finite element method coupled with a viscoplastic self-consistent model, a macro-microscopic simulation model of the squeeze casting torsional forming process was established. The introduction of torsional shear in SQ results in a more uniform distribution and lower equivalent stress, thereby improving the forming efficiency. Additionally, the shear force is increased during the forming process, the shear force is greater with the distance from the torsional axis increasing, and the great shear force could be maintained for a long time. Ultimately, this leads to a thinner wall thickness, finer secondary dendrites, and eutectic Si in the workpiece. During the SQT process, for introducing $(1\overline{1}1)[10\overline{1}]$ slip during the late stage of deformation, a significant shift in grain rotation directions happens and the grain rotation angles increase, finally attributed to the development of the $(1\overline{1}\overline{1})[0\overline{1}1]$ texture. Full article

Open-mouth panting in broiler chickens is a visible and critical indicator of heat stress and compromised welfare. However, detecting this behavior in densely populated cages is challenging due to the small size of the target and frequent occlusions and cluttered backgrounds. To overcome these issues, we proposed an enhanced object detection method based on the lightweight YOLOv8n framework, incorporating four key improvements. First, we add a dedicated P2 detection head to improve the recognition of small targets. Second, a space-to-depth grouped convolution module (SGConv) is introduced to capture fine-grained texture and edge features crucial for panting identification. Third, a bidirectional feature pyramid network (BIFPN) merges multi-scale feature maps for richer representations. Finally, a squeeze-and-excitation (SE) channel attention mechanism emphasizes mouth-related cues while suppressing irrelevant background noise. We trained and evaluated the method on a comprehensive, full-cycle broiler panting dataset covering all growth stages. Experimental results show that our method significantly outperforms baseline YOLO models, achieving 0.92 mAP@50 (independent test set) and 0.927 mAP@50 (leakage-free retraining), confirming strong generalizability while maintaining real-time performance. The initial evaluation had data partitioning limitations; method generalizability is now dually validated through both independent testing and rigorous split-then-augment retraining. This approach provides a practical tool for intelligent broiler welfare monitoring and heat stress management, contributing to improved environmental control and animal well-being. Full article

Asymmetrical nanogrooves are commonly employed as blazed gratings for precision measurement, optical communication, and optical sensing applications. Diamond cutting is a promising deterministic processing technology for nanogrooves with a triangular cross-section profile. Non-free cutting of nanogrooves makes it hard to suppress the cutting-caused deformation because of the low stiffness of nanogrooves. Focusing on the influence of non-free cutting on the deformation of asymmetrical nanogrooves, this paper systematically investigates the asymmetrical nanogroove cutting in amorphous nickel phosphorous material through mechanism revelation, simulation analysis, and experimental discussion. The materials removal mechanism by two side edges with different slopes in the non-free cutting is revealed according to the shear interference. According to the relative feed direction between tool and workpiece, two types of feed cases in the asymmetrical nanogrooves, named D1 and D2, respectively, are investigated by comparison in terms of deformation mechanism, nanogrooves topography, and nodal stress of tool edges. The extrusion by tool edges and the squeeze by the chip flow mainly influence the deformation of nanogrooves. In the D1 case, the horizontal component of squeeze by the chip flow towards the rear just-fabricated nanogroove, and the severely deformed nanogrooves are stacking together. On the contrary, in the D2 case, the flowing chip squeezes the front uncut materials, relieving the cutting-caused deformation, and asymmetrical nanogrooves have clear V-shaped cross-section profiles. It is proven that the D2 strategy is more suitable for asymmetrical nanogroove machining. The work in this paper will contribute to further understanding of non-free cutting and the processing technology of asymmetrical nanogrooves. Full article

Squeezed light, a prominent non-classical state of light, exhibits reduced quantum noise in one quadrature component below the standard quantum limit (SQL). The property enables quantum-enhanced precision measurements, surpassing the SQL in quantum sensing applications. This review comprehensively introduces the fundamental concepts, classifications, and experimental generation techniques of squeezed light. It further explores its pivotal role and recent advances in diverse quantum sensing domains, including interferometry, gravitational wave detection, magnetometry, force sensing, biomedical sensing, and quantum radar. The review covers theoretical foundations of squeezed states (including quadrature operators and classification schemes, experimental generation techniques in atomic ensembles, nonlinear crystals, and fibers), fundamentals of quantum sensing with squeezed light (from quantum noise theory to quantum-enhanced metrology), and quantum-enhanced sensing applications across the aforementioned domains. Finally, future challenges and opportunities in the field are discussed. Full article

The suspension arm is a crucial connecting component in the automotive powertrain system, required to withstand various working condition loads, thus necessitating high mechanical performance. With the continuous development of forming processes, the forming method of suspension arms has gradually shifted from traditional gravity casting to squeeze casting. Along with the demand for automotive lightweighting, there is an urgent need for lightweight requirements in suspension arm components. This study employs a multi-condition topology optimization method, incorporating the forming requirements of the squeeze casting process, to conduct lightweight design of a certain mounting bracket. The filling and solidification processes were numerically simulated using Anycasting, followed by mechanical property testing and microstructure analysis of the product. The results revealed that the topology-optimized suspension arm met the strength and stiffness requirements under all working conditions, with a mass reduction of approximately 54.7% compared to the pre-optimized version. Based on the forming process analysis of the suspension arm, the design of its squeeze casting mold was completed. Using AnyCasting software (AnyCasting 6.7), numerical simulations of the filling and solidification processes of the suspension arm were conducted. Combined with theoretical calculations, the forming process parameters for the suspension arm were finally determined as follows: extrusion speed of 15 cm/s-10 cm/s-5 cm/s (multi-stage speed), pouring temperature of 690 °C, mold temperature of 250 °C, extrusion pressure of 81.4 MPa, and holding time of 45 s. Through T6 heat treatment, the tensile strength, yield strength, and elongation after fracture of the suspension arm reached 326.05 MPa, 276.87 MPa, and 9.68%, respectively. Metallographic analysis showed that the eutectic silicon in the T6 heat-treated specimens was primarily spherical in shape, uniformly distributed without significant clustering. The reason for this difference may be that heat treatment affects the boundary dissolution degree of alloying elements. For eutectic Al-Si alloys, the boundary dissolution and diffusion of alloying elements are accelerated, which is beneficial for improving the mechanical properties of the alloy. Finally, in order to quantitatively analyze the microstructural properties of the material after heat treatment, analyses of secondary dendrite arm spacing and porosity were conducted, leading to the conclusion that the microstructure after heat treatment is more uniform and dense. Full article

(1) Background: Multimodal tactile cognition is paramount for robotic dexterity, yet its advancement is constrained by the limited realism of existing texture datasets and the difficulty of effectively fusing heterogeneous signals. This study introduces a comprehensive framework to overcome these limitations by integrating a parametrically designed dataset with a novel fusion architecture. (2) Methods: To address the challenge of limited dataset realism, we developed a universal texture dataset that leverages information entropy and Perlin noise to simulate a wide spectrum of surfaces. To tackle the difficulty of signal fusion, we designed the Multimodal Fusion Attention Transformer Network (MFT-Net). This architecture strategically combines a Convolutional Neural Network (CNN) for local feature extraction with a Transformer for capturing global dependencies, and it utilizes a Squeeze-and-Excitation attention module for adaptive cross-modal weighting. (3) Results: Evaluated on our custom-designed dataset, MFT-Net achieved a classification accuracy of 86.66%, surpassing traditional baselines by a significant margin of over 21.99%. Furthermore, an information-theoretic analysis confirmed the dataset’s efficacy by revealing a strong positive correlation between the textures’ physical information content and the model’s recognition performance. (4) Conclusions: Our work establishes a novel design-verification paradigm that directly links physical information with machine perception. This approach provides a quantifiable methodology to enhance the generalization of tactile models, paving the way for improved robotic dexterity in complex, real-world environments. Full article