Search Results (943)

Flexible electromagnetic actuators have attracted considerable attention for applications in soft robotics, adaptive manipulation, and human–machine interaction due to their fast response, large deformation capability, and inherent compliance. However, the concurrent application of high actuation performance and long-term cyclic durability remains a major challenge, particularly for liquid metal (LM)-based soft systems, where interfacial instability between LM conductors and polymer substrates often leads to performance degradation. In this work, we report a fabrication strategy in which patterned eutectic gallium–indium (EGaIn) liquid metal circuits are directly written onto electrospun graphene oxide/thermoplastic polyurethane (GO/TPU) nanofiber membranes. The incorporation of graphene oxide significantly enhances interfacial adhesion through hydrogen bonding interactions between oxygen-containing functional groups in GO and the native Ga₂O₃ layer on the LM surface, while the electrospun fibrous architecture further improves mechanical interlocking and structural stability. As a result, the fabricated actuator exhibits robust electromechanical performance, achieving a maximum bending deformation of 90° under a driving current of 0.8 A and maintaining stable operation over 2000 actuation cycles with negligible performance degradation. To further demonstrate its practical functionality, a soft robotic gripper was constructed based on the optimized actuator configuration. The gripper enables the stable grasping and lifting of objects with a weight up to seven times its own mass, while maintaining safe and compliant interaction with fragile objects. This work provides a simple yet effective strategy to simultaneously enhance actuation efficiency, interfacial stability, and mechanical reliability in LM-based GO/TPU flexible electromagnetic actuator systems, offering promising potential for next-generation soft robotic applications. Full article

Background: Carpal tunnel release (CTR) surgery may have an impact on the speed and accuracy of handwriting and digital typing using a computer or mobile devices. Methods: In this prospective cohort study, patients undergoing CTR surgery of the dominant hand, between 18 and 70 years of age, with frequent use of a QWERTY keyboard and smartphone were included. A baseline Visual Analog Scale (VAS) for subjective alteration of writing and Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) were collected at a maximum 3-month follow-up. Digital typing speed (word per minute, wpm) and accuracy (number of mistakes) were tested. Handwriting was assessed by means of direct supervision of one investigator. Results: Of the 30 enrolled patients, 23 (76.7%) completed the 3-month follow-up and were included in the final analysis (mean age 55 ± 12.3 years). Pre- and post-surgery improvements in keyboard typing speed (14.8 ± 6.8 wpm to 17.6 ± 5 wpm) and mobile texting speed (16.7 ± 5.9 wpm to 21.7 ± 6.5 wpm) were significantly improved over time. Accuracy improved significantly only in keyboard typing, where the mean number of errors was reduced (13.1 ± 8.2 to 9.9 ± 5.6). QuickDASH scores decreased significantly (39.1 ± 9.1 to 17 ± 6). Conclusions: CTR surgery was associated with improved typing speed and reduced the number of errors (19% and −24%, respectively) as well as texting speed (30%). This improvement may be relevant in daily and occupational activities, as reported in the previous literature. Full article

The use of Artificial Intelligence (AI) in Formative and Shared Assessment (F&SA) processes refers to the application of AI-based technologies to support formative and continuous assessment in Higher Education (HE). F&SA systems involve the ongoing monitoring of students’ learning, the provision of feedback that enables them to regulate and improve their performance, and the collection of information that informs the continuous improvement of teaching practice. In this context, AI can serve a dual purpose: when orientated towards students, it enhances learning outcomes; when directed at educators, it supports the development of their pedagogical expertise through tools designed to assist in the creation of assessment instruments, the generation of automated feedback, the analysis of learning data, and the design of simulation environments that foster the development of professional competencies. The integration of AI into F&SA practices holds considerable potential to transform traditional assessment approaches by enabling more personalised, adaptive, and timely feedback for both students and educators. In this shared assessment framework, students may likewise draw on AI applications to support specific dimensions of their learning, including academic writing, knowledge organisation, and the generation of educational content, thereby becoming active participants in their own assessment processes. However, the incorporation of AI into F&SA also requires careful consideration of the pedagogical, ethical, and institutional challenges it entails, particularly those related to academic integrity, cognitive offloading, and the responsible use of AI tools. It is therefore essential to promote AI literacy in HE among both faculty members and students, fostering a critical and informed engagement with these technologies that ensures the pedagogical relationship, along with the shared, formative nature of assessment, remains at the core of meaningful learning processes. Full article

Silicon carbide (SiC) has high thermal conductivity and thermal stability; however, its high hardness and brittleness make the fabrication of three-dimensional (3D) SiC microstructures—particularly those intended for thermal management of power devices—highly challenging. Because SiC exhibits strong absorption in the ultraviolet (UV) spectral range, this study conducted UV nanosecond laser irradiation to perform dry, direct-write processing on SiC, with material removal achieved through vaporization. It established an optimization workflow covering processes from the selection of planar processing parameters to the fabrication of 3D micropillar arrays with high surface quality and geometric fidelity. The key process variables were the pulse repetition frequency, nominal laser power, number of repeated scans per layer, and number of Z-direction focal shifts between layers. The micropillar arrays fabricated using the proposed approach were characterized in terms of their total material removal depth, sidewall verticality, and top-surface roughness. The results indicated that processing with a high repetition frequency resulted in favorable sidewall verticality; however, the pillar top surfaces were susceptible to high roughness resulting from spatter and melt backfilling. To address this problem, a strategy involving the fabrication of fewer shifting layers and the use of more scan repetitions per layer was employed. This strategy mitigated cumulative defocus errors, increased the total material removal depth, and achieved a suitable balance among removal depth, sidewall verticality, and top-surface roughness. Overall, this study provides practical guidelines for the direct-write 3D microstructuring of hard materials such as SiC. These guidelines have potential applications in the rapid fabrication of chip-level heat dissipation microstructures. They can reduce process complexity and manufacturing cost while improving design flexibility for 3D thermal architectures. Full article

In micro-extrusion and direct ink writing, the nozzle outlet diameter is often used to estimate the deposited line width or free-filament diameter. However, highly loaded conductive silver pastes may exhibit pronounced extrusion swell after leaving the nozzle, resulting in a filament diameter larger than the nozzle inner diameter. To quantify this deviation, this study proposes a single-parameter semi-theoretical correction model based on radial force balance at the nozzle exit, integrating Herschel–Bulkley yield stress–shear-thinning rheology with a finite-deformation description. The exit radial stress is derived from pressure-driven circular tube flow, while the post-exit radial expansion is balanced against atmospheric constraint. A comprehensive correction force constant, C, is introduced to account for wall-induced energy dissipation, particle-structure rearrangement, residual elastic recovery, and model simplifications. After calibration using a transition-swelling nozzle, C was determined as 1.03 × 10⁻² N. The model was applied to six nozzle diameters and four nozzle length–pressure conditions. For Nozzles 1–4 with significant swelling, the mean absolute percentage error was 5.31%, while the overall error for all six nozzles was 11.84%, mainly due to overestimation for the nearly non-swelling Nozzle 6. For varying nozzle lengths, the error was 5.20%, and both experimental and predicted swell ratios decreased with increasing effective nozzle length. The model provides a semi-theoretical tool for estimating free-filament dimensions and analyzing nozzle-length effects, primarily under pronounced-swell conditions. Its predictive capability becomes limited as the swell ratio approaches unity, where additional corrections for wall slip, relaxation, and the zero-swell boundary are required. Full article

This paper introduces a novel method for producing planar electrochemical devices by combining multi-material 3D printing with metal leaves (3D-MLEs). The fabrication process is based on the use of two polymeric materials, polylactic acid (PLA) and polycaprolactone (PCL), leveraging their different melting points. The approach exploits the thermoadhesive properties of polyesters, which can act as bonding layers upon heating, enabling a direct-writing and low-step fabrication strategy. The device was fabricated using a dual-extruder 3D printer to produce a PLA support containing PCL tracks, followed by selective thermal adhesion of the metal leaf onto the PCL. This process exploits the different melting temperatures of the two polymers: PCL softens and becomes adhesive at the selected temperature, while the PLA support remains structurally unaffected. A final brushing step enables the definition of a well-controlled three-electrode geometry. Following optimization of the fabrication parameters, a platinum leaf-based device (3D-PtLE) was assembled and evaluated using potassium hexacyanoferrate(II) and hexaammineruthenium(III) chloride as redox probes. The optimized device was subsequently applied to hydrogen peroxide detection in phosphate buffer (pH 7), exhibiting a linear response in the concentration range of 0.25–5 mM, with a limit of detection of 67 μM and good repeatability (RSD = 4.2%). The analytical applicability of the device was further demonstrated through the analysis of a real sample consisting of washing water prepared from a sodium percarbonate-based cleaning tablet, with good agreement (98 ± 6%) with the standard titration method. The proposed strategy provides a simple, low-cost, and customizable approach for fabricating planar electrochemical platforms based on pure metal electrodes, combining high analytical performance with straightforward manufacturing. Full article

The firing of traditional gray pottery relies on complex physicochemical reactions governing its color, dimensional accuracy, and structural stability. Unclear kiln-transformation mechanisms restrict standardized and digital production of this Chinese intangible cultural heritage. Herein, direct ink writing (DIW) was used to fabricate Hejin gray pottery green bodies from local ternary raw materials. Thermodynamic calculations, TG–DTG/DSC, XRD, XRF, and atmosphere-controlled firing tests were combined to reveal coupled phase evolution and reduction color-forming mechanisms during sintering. Two interrelated kiln-transformation processes were identified. First, sequential mineral reconstruction occurs at four critical temperatures: free water loss at 119.8 °C, two-stage dehydroxylation of hydrous silicates at 270.5 °C and 767.9 °C, and CaCO₃ decomposition at 547.9 °C. Uneven shrinkage and gas release at these temperatures induce cracking, blistering, and deformation of printed bodies. Micron-sized CaCO₃ (equivalent radius ≈ 1.31 μm) exhibits high surface energy and significantly reduces its decomposition temperature, consistent with experimental observations. Second, reducing atmospheres trigger competitive phase formation. Distinct from the conventional Fe₂O₃ → Fe₃O₄ → FeO reduction pathway, Fe oxides preferentially react with abundant Al₂O₃ to form thermodynamically stable FeAl₂O₄ spinel, yielding uniform celadon-gray tones. The final color is nearly independent of 20–90 vol% CO, and air-isolated cooling below 600 °C is mandatory to prevent secondary oxidation and reddening. This work establishes a thermodynamic framework for DIW-printed Hejin gray pottery kiln transformation, clarifies microscale defect and color-evolution mechanisms, and offers theoretical guidance for atmosphere-controlled firing and digital mass production of heritage ceramics. Full article

Osteochondral defects, involving both articular cartilage and subchondral bone, can lead to joint degeneration and osteoarthritis. Recent advances in 3D-printed biphasic scaffolds offer promising opportunities to recreate physiological microenvironments for tissue regeneration. In tissue engineering, these scaffolds can be mechanically stimulated to promote targeted cartilage and bone formation. While computational models have been widely used to study mechanically induced cellular responses in monophasic scaffolds, time-dependent modelling of biphasic osteochondral systems remains relatively scarce. In this study, a fluid–structure interaction (FSI) framework coupled with a mechanoregulatory algorithm was developed to predict mechanically induced early-stage mesenchymal stem cell (MSC) differentiation in biphasic open-porous osteochondral scaffolds comprising chondral and bone layers designed for direct ink writing (DIW). In a second model, an interfacial barrier layer representing the native osteochondral interface was integrated. Dynamic compressive loading (1 Hz, $2.5\%$ strain) was applied. The simulations predicted region-specific differentiation patterns in both the chondral and subchondral bone regions. In the scaffold without a barrier layer, approximately $68.9\%$ of MSCs in the chondral layer and $93.4\%$ of MSCs in the bone layer underwent chondrogenic and osteogenic differentiation, respectively. Incorporation of the barrier layer caused only minor changes, reducing predicted cartilage and bone differentiation by approximately $1.5\%$ and $3.9\%$, respectively. Overall, this study highlights the capability of computational modelling to predict mechanobiological responses in complex osteochondral systems and support scaffold design and effective mechanical stimulation protocols. Full article

The importance of learners’ cognitive and affective engagement with teachers’ feedback has been recognized, but they remained underexplored. To bridge this gap, this qualitative research aims to understand the dynamics of low-proficiency English learners’ cognitive and affective engagement with a teacher’s combined modes of feedback (CMF) in an Indonesian EFL context. A class of first-semester learners, working individually and in pairs, wrote narrative and descriptive texts and revised their texts. Data was collected with an open-ended questionnaire and a retrospective interview. The results showed that students engaged with the teacher’s CMF both cognitively and affectively but to varying degrees. Most of them were aware of the feedback by noticing and understanding the writing issues addressed by the feedback, and they used cognitive and meta-cognitive strategies that led them to have positive and negative emotional reactions and attitudinal responses. The last section discusses the pedagogical implications, limitations, and future directions for research. Full article

Deep multiscale learning has emerged as a promising venue for robust image detection and multi-object tracking in adverse conditions, but the current solutions tend to be impacted by the issues of occlusion, scale variation, and background clutter, focusing on each of them separately and restricting the generalization. In a direction to address these gaps, this piece of writing proposes a unified model that incorporates HRNet to extract high-resolution features, DETR to make use of transformers for detection, and TrackFormer to identify in an identity-preserving manner. Data was based on the MOT17 benchmark dataset, which provides various urban video sequences, including annotated bounding boxes and identities, to guarantee a test that is rigorous. The approaches were selected due to their complementary advantages: HRNet keeps fine-grained spatial information, DETR allows us to locate the objects in an accurate way, and TrackFormer tracks the trajectories across fragments. Experiments show good performance, with a mean detection AP of 70.9, precision of 76.5, recall of 72.8, MOTA of 74.8, IDF1 of 70.2, and HOTA of 63.6, maintaining real-time performance of 26 FPS with a latency of 38.5 ms per frame. In general, this work offers a globally scalable, end-to-end system for problems like surveillance and self-driving, and future work aims to address outrageously dense scenes, enhance cross-dataset generalization, and come up with lightweight systems to deploy these edges. Full article

Magnetoresistive random-access memory (MRAM), as a promising non-volatile memory technology, has attracted extensive research interest owing to its unique combination of high operating speed, exceptional endurance, low standby power consumption, and CMOS process compatibility. In this review, we provide a comprehensive overview of the technological evolution of MRAM, spanning from Toggle-MRAM to spin-transfer torque (STT)-MRAM and then to spin–orbit torque (SOT)-MRAM. The working mechanisms, performance trade-offs, and integration potential of each generation are systematically summarized. Furthermore, the diverse applications of MRAM—including embedded systems-on-chip (SoCs), edge computing, aerospace and automotive electronics, artificial intelligence accelerators, neuromorphic computing, and hardware-level security—are thoroughly discussed. We also identify key challenges hindering large-scale commercialization, such as the trade-off between write energy and speed, process complexity, storage density constraints, and cost competitiveness. Finally, emerging research directions are proposed, emphasizing short-term priorities such as write current reduction and yield improvement, as well as long-term development strategies focusing on material–device–algorithm co-optimization and ecosystem establishment. Full article

Eye-movement studies of manual production often average gaze across an entire trial, obscuring how visual information use changes once actions begin. We separated the pre-writing and writing phases in a fixed progressive Chinese calligraphy task. Thirty-seven postgraduate students completed two style-guided transfer (SGT) pages, a worked example, and two evolution-based mapping (EBM) pages; 34 contributed usable gaze data. On SGT pages, reference allocation fell from 0.626 before writing to 0.131 during writing, whereas the share of reference viewing directed to diagnostic tokens rose from 0.473 to 0.601. On EBM pages, allocation to the cue-plus-context display fell from 0.825 to 0.447 after pen onset but remained substantial; cue share and context coverage also declined. Participant-level process blocks did not improve quality models. In exploratory page-level EBM analyses, greater pre-writing context coverage was associated with higher product quality. These findings identify pen onset as a useful boundary for analyzing visual information use in constrained production: external sampling is greatest before writing, and task-specific re-access persists during execution. Because the task order was fixed, page-family differences cannot be separated from practice or scaffolding. Phase-specific area-of-interest measures can therefore add process information to product scores without treating gaze as a direct measure of cognition. Full article

The spatial organization of microscale fillers is critical for macroscopic performance, yet precise control over their distribution and orientation remains a major challenge in direct ink writing. Here, we present an acoustofluidic capillary nozzle that integrates acoustic manipulation into direct ink writing, enabling programmable in situ assembly of functional fillers during extrusion. By coupling a piezoelectric transducer with a commercial glass capillary, stable acoustic standing waves are established within the flow channel, driving suspended filler particles toward pressure nodes via acoustic radiation forces. Simulations and experiments systematically investigate how capillary geometry and material properties influence acoustic energy distribution and particle assembly behavior. In particular, rectangular capillaries generate stable multi-node standing waves, inducing periodic alignment of nickel-coated carbon fibers into ordered conductive bundles. This acoustically programmed microstructure reduces the percolation threshold from 8 wt% to 2 wt% and enhances electrical conductivity by up to 32.1-fold at identical filler contents. Meanwhile, the composites exhibit pronounced anisotropic conductivity and maintain excellent mechanical flexibility, with stable electromechanical performance under 16% bending strain and cyclic loading. This work demonstrates a simple and scalable acoustofluidic nozzle platform for programmable microstructure engineering in direct ink writing, offering new opportunities for fabricating high-performance multifunctional composites. Full article

Extrapyramidal symptoms (EPS) are motor side effects commonly induced by antipsychotic medications and can lead to measurable changes in handwriting patterns. These symptoms affect both the spatial and temporal characteristics of writing, including stroke thickness, direction and the rate of directional change. To model these complex variations, we propose a novel Liquid Generative Adversarial Network (LiquidGAN), which combines the adaptive dynamics of liquid neural networks with the data generation capability of GANs. Handwriting data were collected from 94 patients with confirmed EPS and 30 healthy controls using Archimedean spiral patterns drawn with both hands. A total of 211 images were processed for both binary and multiclass classification using a pretrained ResNet50 model. The pretrained ResNet50 achieved 92% accuracy and 97% precision in the binary classification task; however, its performance dropped significantly to 57% accuracy in multiclass classification, indicating limited capability in capturing fine-grained EPS severity variations. In contrast, the proposed LiquidGAN demonstrated excellent performance in the binary classification task, achieving 97% accuracy and 98% precision. More importantly, LiquidGAN substantially outperformed the baseline in the more challenging multiclass setting, achieving 70% accuracy and precision across four classes (mild, moderate, severe, and control). This shows that the diverse dataset from the liquidGAN significantly improves the HOG-ANN classification and effectively captures complex and subtle handwriting variations associated with different EPS severity levels that conventional models such as ResNet50 fail to distinguish. In addition, LiquidGAN generated diverse and realistic synthetic handwriting samples, yielding improved Fréchet Inception Distance (FID), precision, and recall compared with style GAN. These findings demonstrate that handwriting biomarkers, when analyzed through dynamic generative learning, offer an effective and non-invasive approach for monitoring extrapyramidal side effects in clinical settings. Full article

Background and Objectives: Temporomandibular disorders (TMDs) encompass a broad spectrum of functional and structural abnormalities of the temporomandibular joint (TMJ). Conventional diagnostic tools, although essential, often fail to capture the underlying biochemical mechanisms driving disease progression. Synovial fluid (SF), by virtue of its direct proximity to intra-articular tissues, represents an accessible biological matrix for identifying molecular signatures of inflammation, cartilage degradation, lubrication failure, oxidative stress, and angiogenic activation. The objective of this review is to synthesize current evidence on SF proteomics in TMD and evaluate its potential translational value in precision medicine. Materials and Methods: A narrative review of the literature was conducted on PubMed to identify human studies focused on SF proteomic and biochemical biomarkers in TMD. Eligible studies included original research articles assessing SF composition in relation to specific TMJ pathologies, diagnostic categories, or clinical phenotypes. Extracted data included study design, sample characteristics, analytic methodology, biomarkers investigated, and key findings. Google Gemini (Google LLC, Mountain View, CA, USA) was used as an AI-assisted tool to support language editing and manuscript writing during the preparation of this article. The use of this tool was limited to linguistic refinement; all scientific content, data interpretation, and conclusions were formulated and verified by the authors. Results: Across the analyzed studies, TMD phenotypes—particularly disc displacement with or without reduction (DDwR, DDwoR) and osteoarthritis (OA)—were characterized by consistent alterations in cytokines (IL-1β, IL-6, IL-8, TNF-α), extracellular matrix (ECM) components (aggrecan, glycosaminoglycans (GAGs), decorin, MMP-2, MMP-9), lubrication molecules (lubricin/PRG4), oxidative stress mediators (myeloperoxidase (MPO), nitric oxide (NO), glutathione peroxidase (GPX)), adipokines (chemerin, resistin, adiponectin), and angiogenic factors (vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2)). Recent liquid chromatography–tandem mass spectrometry (LC–MS/MS) analyses further revealed phenotype-specific protein clusters and pathways related to inflammation, ferroptosis, hypoxia signaling, and proteoglycan metabolism. Conclusions: Current evidence suggests that SF proteomics and multi-analyte biomarker profiling offer a promising, hypothesis-generating approach for understanding the biological mechanisms underlying TMD. The integration of proteomic, metabolic, and inflammatory markers holds future potential for diagnostic panel development; however, prospective clinical validation is still required before SF-based molecular profiling can be implemented as a precision medicine tool in TMJ disorders. Full article