Search Results (646)

In order to solve the problem encountered by traditional potato–stem separation devices, that is, they cannot meet the requirements when installed in small-scale harvesters, a new type of vertical differential roller potato–stem separation device was developed. The device features a compact structure and simultaneously possesses both separating and conveying functions. Through the analysis of the separation force between potato and stem, the structure and parameters of the separation device were determined. The simulation and the field test of the potato–stem separation process were carried out with the vertical differential roller speed, the vertical differential roller gap width and the conveyor chain speed as the influencing factors. The simulation test analysed the influence law of different working parameters on the performance of potato–stem separation. The field test revealed the order of the effects of various factors on the impurity rate and skin-breaking rate, concluding that the optimal combination of operational parameters was a vertical differential roller rotational speed of 6 s⁻¹, a vertical differential roller gap width of 7 mm, and a conveyor chain speed of 1.4 m·s⁻¹. This experiment fills the research gap in the study of potato–stem separation devices suitable for small-scale potato harvesters and promotes the development of compact potato harvesters. Full article

Thermoset fibre-reinforced composites are widely used in high-end industries, but a growing demand for more sustainable and recyclable alternatives conveyed the research efforts towards thermoplastics. To expand their usage, new approaches to their manufacture and mechanical performance must be tackled and tailored to each engineering challenge. The present study designed, manufactured and tested advanced multi-layer laminated composites of thermoplastic polypropylene prepreg reinforced with continuous woven fibreglass with interlayer toughening through thermoplastic polyurethane elastomer (TPU) layers manufactured by fused filament fabrication. The manufacturing process was iteratively optimized, resulting in successful adhesion between layers. Three composite configurations were produced: baseline glass fibre polypropylene (GFPP) prepreg and two multi-layer composites, with solid and honeycomb structured TPU layers. Thermal and mechanical analyses were conducted with both the polyurethane elastomer and the manufactured laminates. Tensile testing was conducted on additively manufactured polyurethane elastomer specimens, while laminated composites were tested in three-point bending. The results demonstrated the potential of the developed laminates. TPU multi-layer laminates exhibit higher thermal stability compared to the baseline GFPP prepreg-based composites. The addition of elastomeric layers decreases the flexural modulus but increases the ability to sustain plastic deformation. Multi-layer laminate composites presenting honeycomb TPU layers exhibit improved geometric and mechanical consistency, lower delamination and fibre breakage, and a high elastic recoverability after testing. Full article

Coriander is a significant crop, playing an essential role in daily life for various purposes, including flavouring curries and medicinal uses, among others. Despite its importance, coriander is still harvested manually. To address this, developed a self-propelled battery-operated coriander harvester, designed with ergonomics, environmental sustainability and affordability for small and marginal farmers in mind. The harvester is equipped with a main frame, a lead-acid battery, a BLDC motor, a reciprocating cutter bar, a PU conveyor belt, a collection bag, a handle, and transport wheels. The harvester was tested on the coriander crop, and the results were analyzed using Design Expert software to optimize various operational parameters. The harvester’s performance was evaluated at three forward speeds: 1.5 km/h, 2 km/h, and 2.5 km/h, resulting in covered areas of 0.114 ha, 0.164 ha, and 0.22 ha, with field efficiency values of 76%, 82%, and 88%, respectively. Optimal harvesting conditions were identified by design expert software at a forward speed of 1.64 km/h, with a conveyor driving pulley at level 3 (50.8 mm) and a cutting height at level 2 (75 mm). Under these conditions, the harvester achieved a harvesting efficiency of 97.24% and a cutting efficiency of 98.2%, with minimal conveying loss of 0.96%. The theoretical field capacity was 0.16 ha/h, the actual field capacity was 0.131 ha/h, and the overall field efficiency was 81.8%. Full article

The axle box of a railway vehicle is a critical component, and its maintenance involves complex procedures that are difficult to convey with traditional, document-based manuals. To address these challenges, augmented reality (AR)-based educational content was developed to digitize maintenance training and enhance its effectiveness. The content’s implementation was guided by a systematic storyboard, which was based on interviews with skilled staff. It also utilized specialized algorithms to improve the accuracy of mechanical measurement work and the efficiency of User Interface (UI) generation. The user experience of the developed content was comprehensively evaluated using a combination of two methods: a formative evaluation through direct observation of work performance and a post-survey administered to 40 participants. As a result of the evaluation, the mean work success rate was 62.5%, demonstrating the content’s high efficiency as a training tool. The overall mean score from the post-survey was 4.11, indicating high user satisfaction and perceived usefulness. A one-way ANOVA was performed and revealed a statistically significant difference in post-survey scores among the four age groups. The developed content was found to be more effective for younger participants. The results confirm the high potential of AR as a digital educational method for complex maintenance work. Full article

Electromyography (EMG) signals reflect hand motion intention and exhibit a certain degree of amplitude symmetry. Nowadays, recognition of hand motion intention based on EMG has enriched its burgeoning promotion in various applications, such as rehabilitation, prostheses, and intelligent supply chains. For instance, the motion intentions of humans can be conveyed to logistics equipment, thereby improving the level of intelligence in a supply chain. To enhance the recognition accuracy of multiple hand motion intentions, this paper proposes a hand motion intention recognition method that decodes EMG signals based on improved long short-term memory (LSTM). Firstly, we performed preprocessing and utilized overlapping sliding windows on EMG segments. Secondly, we chose LSTM and improved it so as to capture features and enable prediction of hand motion intention. Specifically, we introduced the optimal key hyperparameter combination in the LSTM model using a genetic algorithm (GA). We found that our proposed method achieved relatively high accuracy in detecting hand motion intention, with average accuracies of 92.0% (five gestures) and 89.7% (seven gestures), while the highest accuracy reached 100.0% (seven gestures). Our paper may provide a way to predict the motion intention of the human hand for intention communication. Full article

Particle-based 3D printing shows great potential in high-performance composite fabrication due to high raw material utilization and flexible material compatibility. However, constrained by conventional extrusion system structures, critical issues (non-uniform melt conveying, insufficient mixing efficacy, poor extrusion stability, etc.) remain. To address these, this study proposes a novel separate-type pin screw integrating solid–liquid separation (from split screws) and high-efficiency mixing (from pin screws) to improve CF/PLA composite extrusion efficiency and mixing homogeneity in particle-based 3D printing. Three-dimensional modeling, static strength/stiffness analysis, and POLYFLOW-based numerical simulation of particle melt conveying/mixing in the screw channel were conducted to analyze structural parameter effects on pressure field, shear rate, and mixing. Experiments assessed printer extrusion rate (different screws) and printed specimen mechanical properties. The simulation and experiment confirmed the optimized screw has better pressure distribution and mixing at 20 rpm, with optimal pin parameters: diameter 2 mm, height 1.6 mm, radial angle 60°, and axial spacing 10 mm. This work offers theoretical/structural support for particle-based 3D printing extrusion system optimization. Full article

Motion perception is a fundamental function of the tactile system, essential for object exploration and manipulation. While human studies have largely focused on discrete or pulsed stimuli with staggered onsets, many natural tactile signals are continuous and rhythmically patterned. Here, we investigate whether phase differences between “simultaneously” presented, “continuous” amplitude-modulated vibrations can induce the perception of motion across fingertips. Participants reliably perceived motion direction at modulation frequencies up to 1 Hz, with discrimination performance systematically dependent on the phase lag between vibrations. Critically, trial-level confidence reports revealed the lowest certainty for anti-phase (180°) conditions, consistent with stimulus ambiguity as predicted by the mathematical framework. I propose two candidate computational mechanisms for tactile motion processing. The first is a conventional cross-correlation computation over the envelopes; the second is a probabilistic model based on the uncertain detection of temporal reference points (e.g., envelope peaks) within threshold-defined windows. This model, despite having only a single parameter (uncertainty width determined by an amplitude discrimination threshold), accounts for both the non-linear shape and asymmetries of observed psychometric functions. These results demonstrate that the human tactile system can extract directional information from distributed phase-coded signals in the absence of spatial displacement, revealing a motion perception mechanism that parallels arthropod systems but potentially arises from distinct perceptual constraints. The findings underscore the feasibility of sparse, phase-coded stimulation as a lightweight and reproducible method for conveying motion cues in wearable, motion-capable haptic devices. Full article

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like the anterior temporal lobe and temporoparietal junction, and GC to a fronto-striatal procedural network encompassing Broca’s area and the basal ganglia. Moreover, a central theme is the integration of Chomsky’s theories of Universal Grammar (UG), which posits an innate human linguistic endowment, with their neurobiological correlates. This integration analysis bridges foundational models that first mapped syntax (Friederici’s work) to distinct neural pathways with contemporary network-based theories that view grammar as an emergent property of dynamic, inter-regional neural oscillations. Furthermore, we examine the genetic factors influencing ID and GC, including genes implicated in neurodevelopmental and neurodegenerative disorders. A comparative anatomical perspective across human and non-human primates illuminates the evolutionary trajectory of the language-ready brain. Also, we emphasize that, clinically, ID and GC serve as sensitive neurocognitive markers whose power lies in their often-dissociable profiles. For instance, the primary decline of ID in Alzheimer’s disease contrasts with the severe grammatical impairment in nonfluent aphasia, aiding in differential diagnosis. Importantly, as non-invasive and scalable metrics, ID and GC also provide a critical complement to gold-standard but costly biomarkers like CSF and PET. Finally, the review considers the emerging role of AI and Natural Language Processing (NLP) in automating these linguistic analyses, concluding with a necessary discussion of the critical challenges in validation, ethics, and implementation that must be addressed for these technologies to be responsibly integrated into clinical practice. Full article

Semantic similarity, the task of determining whether two sentences convey the same meaning, is central to applications such as paraphrase detection, semantic search, and question answering. Despite the widespread adoption of transformer-based models for this task, their performance is influenced by both the choice of similarity measure and BERT (bert-base-nli-mean-tokens), RoBERTa (all-roberta-large-v1), and MPNet (all-mpnet-base-v2) on the Microsoft Research Paraphrase Corpus (MRPC). Sentence embeddings were compared using cosine similarity, dot product, Manhattan distance, and Euclidean distance, with thresholds optimized for accuracy, balanced accuracy, and F1-score. Results indicate a consistent advantage for MPNet, which achieved the highest accuracy (75.6%), balanced accuracy (71.0%), and F1-score (0.836) when paired with cosine similarity at an optimized threshold of 0.671. BERT and RoBERTa performed competitively but exhibited greater sensitivity to the choice of Similarity metric, with BERT notably underperforming when using cosine similarity compared to Manhattan or Euclidean distance. Optimal thresholds varied widely (0.334–0.867), underscoring the difficulty of establishing a single, generalizable cut-off for paraphrase classification. These findings highlight the value of fine-tuning of both Similarity metrics and thresholds alongside model selection, offering practical guidance for designing high-accuracy semantic similarity systems in real-world NLP applications. Full article

In order to meet the harsh working environment and complex and changeable stress conditions, the low-temperature and high-pressure liquid carbon dioxide conveying system used in oil extraction will choose metal bellows for transportation. In this paper, the bellows in an accident section are investigated and observed by the working environment and characterization methods such as macroscopic analysis, metallographic analysis, EDS component analysis, fracture scanning electron microscopy analysis, and related mechanical performance test methods. The failure mechanism of the accident is preliminarily judged, and the unidirectional fluid–structure coupling model and the standard k-ω turbulence model are used as the calculation models for subsequent simulation. Combined with Fluent finite element simulation analysis, it is verified that the failure is caused by a welding defect, the maximum stress of the metal bellows under normal conditions is less than its own yield strength, and the material can work normally. When the welding crack is greater than 2 mm, the strength of the workpiece weld will be reduced, and the stress concentration has exceeded the yield strength that the workpiece can bear, causing failure fracture at the welding defect part. Combined with ANSYS simulation of accident defects, compared with bellows without defects, the stress at the crack will increase with the increase in the inlet flow velocity and decrease with the increase in temperature, and the flow rate will have a greater influence on it. Therefore, in actual working conditions, the stiffness and fatigue life of the conveying system can be improved by appropriately reducing the liquid flow rate and increasing the temperature. It provides a reference for the future application research of bellows and the research on bellows fracture failure. Full article

Communication plays a vital role in conveying messages, expressing emotions, and sharing perceptions, becoming a fundamental aspect of human interaction with the environment. For individuals with hearing impairments, sign language serves as an essential communication tool, enabling interaction both within the deaf community and with non-deaf individuals. This study aims to bridge this misconception by developing an iconic language recognition system using the Deep Learning-based YOLO-v11 algorithm. YOLO-v11, a state-of-the-art object detection algorithm, is known for its speed, accuracy, and efficiency. The system uses image recognition to identify hand gestures in ASL and translates them into text or speech, facilitating inclusive communication. The accuracy of the training model is 94.67%, and the accuracy of the testing model is 93.02%, indicating that the model has excellent performance in recognizing sign language from the training and testing datasets. Additionally, the model is very reliable in recognizing the classes “Hello”, “I Love You”, “No”, and “Thank You” with a sensitivity close to or equal to 100%. This research contributes to advancing communication equality for individuals with hearing impairments, promoting inclusivity, and supporting their integration into society. Full article

Striatum can be described as a brain region containing a general neuronal mechanism to associate actions or events with reward. In particular, neural activity in the human striatum is modulated by social actions and, critically, by the conjunction of social actions and own reward. To perform this function, dopamine and oxytocin signaling reaching the striatum represent a key factor. These neurotransmitters, in both humans and animals, are released in response to afferent vagal and sensory stimulation, as well as sexual and social interactions, conveying information related to reward and pleasure associated with an event. Dopamine and oxytocin have several effects in common, but of particular interest is evidence indicating that they can mutually modulate their action. The present review focuses on available data delineating interactions between dopaminergic and oxytocinergic signaling in the striatum. In this context, recent data on the possible role played by striatal astrocytes and microglia as key modulators of this crosstalk will be briefly discussed. Full article

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

To address the issues of low efficiency, high cost of manual harvesting, and the lack of mechanized harvesting technology and equipment for high-stem Chrysanthemum coronarium, a self-propelled orderly harvester was designed to perform key harvesting operations such as row alignment, clamping and cutting, orderly conveying, and collection. Based on the analysis of agronomic requirements for cultivation and mechanized harvesting needs, the overall structure and working principle of the machine were described. Meanwhile, the key components such as the reciprocating cutting mechanism and orderly conveying mechanism were structurally designed and theoretically analyzed. The main structural and operating parameters of the harvester were determined based on the geometric and kinematic conditions of high-stem Chrysanthemum coronarium during its movement along the conveying path, as well as the mechanical model of the conveying process. In addition, a three-factor, three-level Box-Behnken field experiment was also conducted with the experimental factors including the machine’s forward, cutting, and conveying speed, and evaluation indicators like harvesting loss rate and orderliness. A second-order polynomial regression model was established to analyze the relationship between the evaluation indicators and the factors using the Design-Expert 13 software, which revealed the influence patterns of the machine’s forward speed, reciprocating cutter cutting speed, conveying device speed, and their interaction influence on the evaluation indicators. Moreover, the optimal parameter combination, obtained by solving the optimization model for harvesting loss rate and orderliness, was forward speed of 260 mm/s, cutting speed of 250 mm/s, and conveying speed of 300 mm/s. Field test results showed that the average harvesting loss rate of the prototype was 4.45% and the orderliness was 92.57%, with a relative error of less than 5% compared to the predicted values. The key components of the harvester operated stably, and the machine was capable of performing cutting, orderly conveying, and collection in a single pass. All performance indicators met the mechanized orderly harvesting requirements of high-stem Chrysanthemum coronarium. Full article

This study investigated the vortex-induced vibration (VIV) characteristics of a proposed water-conveying truss pipeline cable-stayed bridge through wind tunnel tests. The experimental results indicated that both vertical bending and torsional VIV responses decreased as the wind attack angle increased. The vertical bending VIV behavior of the bridge was significantly influenced by the lateral spacing and relative height of the pipelines. Adjustments to these geometric parameters markedly affected the structural VIV response. Furthermore, computational fluid dynamics (CFD) was employed to analyze the flow field around the truss pipeline bridge. The results revealed that changes in the lateral spacing and relative height of the pipelines primarily altered the VIV performance by modifying vorticity distribution, separation point position, and other critical flow field parameters around the truss section. These findings underscore the importance of considering the effects of geometric parameters on VIV during the design of the truss section in pipeline bridges. Full article