Search Results (211)

Diffusion solidification is an effective method to produce non-dendritic microstructures and reduce casting defects in magnesium alloys. However, the influence of precursor alloy superheat on the solidification behavior and the resulting microstructure remains insufficiently understood. In this study, pure magnesium was used as the high thermal mass (HTM) alloy and three Mg-Al-Zn alloys with different aluminum and zinc contents were used as the low thermal mass (LTM) alloys. The effects of superheat on grain morphology, solute diffusion, and constitutional supercooling were investigated through a combination of experimental casting and numerical simulation using Ansys Fluent and Matlab. The results show that the solidified interface consists of five distinct regions, including two base metals, two transition zones, and a central controlled diffusion solidification zone. A higher superheat of the HTM alloy relative to the LTM alloy promotes a wider transition zone and finer globular grains, whereas equal or lower superheat leads to columnar or rosette structures. The Mg-30 wt.%Al-3.5 wt.%Zn alloy with moderate aluminum content produces fine globular grains due to a thinner constitutional supercooling layer and a higher degree of supercooling, which suppresses grain growth and increases nucleation rate. In contrast, the Mg-55 wt.%Al-6.5 wt.%Zn alloy with high aluminum content forms coarse rosette and columnar grains. Among the conditions investigated, the combination of 10 °C HTM and 5 °C LTM superheats tends to promote the formation of fine equiaxed grains. Increasing superheat above this range reduces supercooling and coarsens grains, while decreasing superheat inhibits interface diffusion and promotes solute segregation. The findings provide a theoretical basis for designing precursor alloy compositions and superheat parameters in controlled diffusion solidification of magnesium alloys. Full article

Robust image-to-image correspondence is a fundamental challenge for camera-based Visual Simultaneous Localization and Mapping (SLAM). Conventional approaches primarily rely on isolated local feature matching or optical flow prediction, which often suffer from limited robustness under large parallax, high computational overhead, and cumulative drift errors during long-sequence tracking. To address these limitations, we propose SPTNet (SuperPoint Tracking Network), an efficient multi-task neural network that tightly couples feature detection, description, and dense optical flow prediction within a unified architecture. The fundamental innovation of SPTNet is a Hybrid Tracking Module (HTM) governed by a novel Predictor-Corrector mechanism. Specifically, the dense optical flow field acts as a temporal prior to constrain the descriptor matching search space, while the descriptors act as a correction signal, eliminating flow-induced drift at each frame through spatially constrained Sinkhorn optimization. This synergy enables efficient feature reuse via a shared backbone, minimizing redundant computation. Comprehensive experiments on indoor and outdoor datasets demonstrate that SPTNet attains a false matching rate as low as 1.8% at a 5-pixel threshold on HPatches, substantially reduces cumulative drift on long-sequence SLAM benchmarks, and maintains a high execution speed of 35 FPS on standard GPUs, demonstrating a highly compact footprint advantageous for prospective embedded robotic deployment. Full article

Rapid, low-cost ethanol quantification is vital for beverage quality control, biofuel production, and pharmaceutical applications, yet current approaches are costly, reagent- or label-dependent, or rely on spectroscopy with substantial sample preparation. We introduce a purely cell-based, label-free biosensor that exploits temperature-gradient-induced spontaneous detachment of Saccharomyces cerevisiae from a chip surface. The readout is the detachment half-time, t_d50, derived from time-resolved changes in interfacial thermal resistance, R_th, at the solid–liquid interface. Cells were pre-exposed to ethanol (0–70% v/v) and the detachment kinetics monitored using the heat transfer method (HTM). Under these conditions, cells display a pronounced non-monotonic t_d50 response with a peak around 20% v/v ethanol. Overall, the t_d50 rises from ~45 min (0% ethanol) to ≳10 h (20%) and then decreases, with no detachment at 60% and beyond. Critically, cell quality gates the detachment window. Fresh yeast responds up to ~50%, whereas aged yeast ceases to detach above ~8%, demonstrating a dual-function assay. Complementary measurements show that ethanol decreases surface tension monotonically, as expected, while optical/SEM imaging reveals aggregation above the detachment window. Requiring only a heater and a temperature probe, this platform offers a compact and low-cost strategy for ethanol sensing. Its applicability in a complex matrix is further demonstrated using whiskey diluted to selected alcohol concentrations, which produced responses consistent with the ethanol calibration trend. Potentially, it also offers a thermal assay for real-time monitoring of microbial cell quality across biotechnology and bioengineering applications. Considering ethanol as a proxy for drugs, the strategy may also support label-free drug screening on cells. At a fundamental level, the non-monotonic effect of ethanol, and especially the sharp maximum at 20%, remains unresolved and invites further studies. Full article

Radiation therapy is widely used for cancer treatment. To improve therapeutic efficacy, traditional radiosensitizers are often used in combination. However, their toxic side effects necessitate urgent development of safer alternative biogenic radiosensitizers. Herein, a green approach was used to synthesise ZnO NPs, CuO NPs, and ZnO-CuO NCs using S. africana Luteus, and their ability to enhance the radiosensitizing effect of proton irradiation on Michigan Cancer Foundation-7 (MCF7) breast cancer cell line was evaluated. The biogenic nanoparticles are characterised in detail through several analytical techniques, including Ultraviolet-visible (UV-Vis) spectroscopy, X-ray diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, and Scanning Electron Microscopy (SEM). Interestingly, the NPs showed concentration-dependent effects on MCF7 viability, with CuO NPs exhibiting the strongest effect (IC₅₀ = 42.90 µg/mL), followed by ZnO-CuO NCs (71.12 µg/mL) and ZnO NPs (103.43 µg/mL). Proton irradiation produced a dose-dependent decrease in clonogenic survival of MCF7 cells, and ZnO-CuO NCs displayed the highest enhancement of proton-induced cell death, with a Dose Enhancement Factor (DEF) of 1.69, compared with CuO NPs (1.46) and ZnO NPs (1.09). Holotomographic microscopy (HTM) data further confirmed that ZnO-CuO NCs impaired cellular macromolecules more than the individual NPs. Findings from this study suggest that the biogenic NPs are promising radiosensitizers for cancer radiotherapy. Full article

Geometric errors in CNC machine tools accumulate along the tool path and directly affect machining accuracy. Traditional serial-chain-based volumetric error models, such as those based on the homogeneous transformation matrix (HTM) or screw theory, often exhibit ambiguous geometric definitions, weak traceability to measurement points, and increased computational cost due to repeated coordinate transformations and inverse mappings, limiting their suitability for real-time control. To overcome these challenges, this study proposes a parallel-type unified error vector transfer (EVT) framework, based on the Abbe and Bryan principles. In this framework, axis error motions are directly expressed as vectors and transferred to the tool center point (TCP), where they are superimposed to obtain total error contributions. Building on this principle, a unified normal volumetric error model (NVEM) is formulated using survival and sign factors. The unified NVEM is applicable to various types of three-axis machining centers, including horizontal configurations. In other words, differences in coordinate system definitions can be reconciled through coordinate transformation, allowing the unified NVEM to be consistently applied. Furthermore, a real-time error compensation controller (RECC) is embedded into the CNC kernel to compute compensation values within each interpolation cycle, ensuring deterministic and low-latency operation without external computation. Experimental validations on an XYFZ-type vertical machining center demonstrate that the proposed framework improves positioning accuracy by more than 72% and machining accuracy by 60.4%. These results confirm the feasibility, efficiency, and universality of the parallel-type unified EVT framework for real-time volumetric error compensation. Here, ‘parallel-type’ denotes the parallel superposition of independent error vector contributions, rather than a parallel kinematic mechanism. Full article

Short-term load forecasting is a key capability for smart-grid operation, but real smart-meter streams are affected by missing values, communication noise, and non-stationary consumption patterns. This paper studies forecasting using raw smart-meter data collected from domestic consumers in a medium-sized city in southern Spain. In particular, we assess Hierarchical Temporal Memory (HTM), a biologically inspired online sequence learner, against a family of Long Short-Term Memory (LSTM)-based recurrent baselines. HTM offers continual adaptation and avoids a separate training phase, whereas LSTM relies on offline supervised training and may require retraining or fine-tuning under distribution shift. For five-step-ahead forecasting, HTM achieved a test RMSE of 251 kWh (about 15% of average consumption). After hyperparameter optimization, the best tested LSTM configuration achieved a test RMSE of approximately 250 kWh under clean conditions, indicating nearly identical point accuracy between the two approaches. Under synthetic Gaussian-noise injection, however, HTM remained comparatively stable, whereas the optimized LSTM configuration degraded markedly under the tested perturbation protocol. In addition, HTM exhibited a lower runtime in the tested CPU-based implementation. These findings suggest that HTM is a viable online alternative for aggregated smart-meter forecasting, offering competitive accuracy together with a favorable operational profile under the specific evaluation setup considered here. Full article

Polymeric hole-transport materials (HTMs) play a pivotal role in improving the efficiency, stability, and scalability of perovskite solar cells (PSCs). Owing to their structural tunability, polymeric HTMs enable effective control over energy-level alignment, charge transport, interfacial interactions, and film formation. This review summarizes recent advances in polymeric HTMs, including conjugated-backbone polymers, donor–acceptor (D–A) copolymers, and emerging architectures such as hyperbranched, ionic, chelating, and anchorable polymer systems. Particular emphasis is placed on structure–property–performance relationships and interfacial engineering strategies that govern device efficiency and long-term operational stability in PSCs. Finally, the key challenges and future directions for developing scalable and robust polymeric HTMs are discussed. Full article

Primary open angle glaucoma (POAG) is one of the leading causes of irreversible blindness and is associated with dysfunction of the trabecular meshwork (TM), a three-dimensional (3D) structure that regulates aqueous humor outflow and, consequently, intraocular pressure (IOP). IOP is the only modifiable factor for glaucoma. Outflow facility is the inverse of aqueous humor outflow resistance caused by the presence of the TM and adjacent tissues, and reflects the TM’s central role in IOP control, representing the most physiologically relevant measure of human trabecular meshwork (HTM) function. Therefore, development of ex vivo systems to study outflow facility and IOP regulation is critical for advancing glaucoma research. We present a comprehensive review of bioengineering approaches to generation of 3D HTM models using synthetic, natural, and hybrid hydrogels, micro- and nanofabricated synthetic substrates or porous scaffolds, and microfluidic devices. These 3D HTM systems have been designed to capture key features such as topography, stiffness, and fluid flow in the conventional outflow pathway. In particular, we highlight HTM models that recapitulate IOP regulation and allow measurement of outflow facility, which directly reflect pressure-dependent outflow resistance in dynamic HTM physiology and glaucoma pathophysiology. By integrating these bioengineering approaches with emerging stem cell technologies, this review offers an evidence-based landscape overview and framework for designing next-generation 3D human-relevant TM models for outflow physiological studies and IOP-modulating drug discovery. Full article

Background/Objectives: Diabetes mellitus (DM) is associated with a doubled prevalence of elevated intraocular pressure (IOP) caused by trabecular meshwork (TM) dysfunction. Chronic hyperglycaemia leads to oxidative stress and fibrotic remodeling of the TM. We previously identified the Sigma-1 receptor (S1R) as a novel anti-fibrotic target by demonstrating that its agonist, fluvoxamine (FLU), is protective in diabetes-related renal fibrosis. Here, we investigate its potential to mitigate ocular fibrosis. Methods: First, we wanted to verify in different in vivo models (high-fat diet/streptozotocin (HFD/STZ) rats, db/db mice) that type 2 DM (T2DM) leads to fibrotic remodeling of the TM. Then, in vitro, we assessed the effect of FLU (15 µM) on hyperglycaemia-induced (HG, 25 µM) fibrosis, oxidative stress and endogenous nitric oxide (NO) production. Results: In T2DM models, excessive accumulation of collagen, α-smooth muscle actin (αSMA), fibronectin (Fn) and F-actin was observed in the eyes. Ocular fibrosis was accompanied by IOP elevation (13.7 vs. 18.7 mmHg) in db/db mice. In human TM cells (HTM5), FLU decreased HG-induced cell proliferation (14% vs. 24%) and upregulated S1R protein expression. Furthermore, FLU suppressed the expressions of key fibrotic elements, including transforming growth factor-β2 (TGF-β2) by 37%, Fn by 49%, collagen type 1 (COL1A1) and type 4 (COL4A1) by 24% and 45%, respectively. FLU also reversed HG-induced F-actin accumulation by 39% and enhanced intracellular NO levels by 34%. Crucially, FLU decreased ROS generation by half, demonstrating its protective effect against HG-induced oxidative stress. Conclusions: These findings highlight the potential of S1R activation as a promising therapeutic target to alleviate hyperglycaemia-induced injury to the TM by modulating multiple molecular pathways. Full article

Dysregulated extracellular matrix (ECM) deposition and epithelial–mesenchymal transition (EMT) in the trabecular meshwork (TM) contribute to glaucoma-associated fibrotic remodeling, and lysophosphatidic acid (LPA) potently induces these profibrotic responses in human trabecular meshwork (HTM) cells. We investigated whether an ethanolic extract of Diospyros kaki leaves (EEDK) attenuates LPA-induced fibrosis and explored the underlying mechanisms. HTM cells were stimulated with LPA and treated with ethanol-based EEDK extracts. Expression of ECM/fibrosis-related genes (FN1, ACTA2, COL1A1, COL3A1, COL4A1, COL6A2, CCN2) and EMT markers (CDH2, VIM, SNAI1) was assessed, along with cell migration using a wound-healing assay. Upstream regulatory pathways were examined via transcription factor prediction, AP-1 reporter assays, and analyses of MAPK/AP-1 signaling. Among the extracts tested, the 70% ethanol EEDK extract showed the strongest antifibrotic activity, significantly reducing LPA-induced ECM gene/protein expression and inhibiting HTM cell migration in a dose-dependent manner, whereas the 90% ethanol extract showed minimal effects. LPA robustly activated MAPK-dependent AP-1 signaling, and either pharmacologic inhibition of MAPK pathways or treatment with 70% ethanol EEDK comparably suppressed AP-1 activity and decreased downstream ECM/EMT gene expression. Thus, 70% ethanol EEDK mitigates LPA-induced TM fibrosis by inhibiting MAPK/AP-1-mediated transcription, supporting its potential as an antifibrotic strategy for glaucoma. Full article

Crown width (CW) is a critical metric for characterizing tree-canopy dimensions; however, its direct measurement remains labor-intensive and is often impractical in inaccessible crowns. Consequently, CW is frequently derived from projections, which are susceptible to multiple sources of imprecision, including canopy density, crown irregularity, terrain heterogeneity, and the observer’s vantage point, especially in structurally complex natural forests. While deep neural network (DNN) models show substantial potential for CW prediction, their performance in heterogeneous forests remains uncertain. We developed DNN models integrated with a Height Threshold Method (HTM) to predict individual-tree CW in the natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Our study further compared the relative importance of feature engineering versus model architectural complexity in predictive accuracy and identified the key ecological variables governing CW. The model performance was evaluated through the coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Field surveys of 34 representative sample plots produced 1884 individual-tree records. The main results were as follows: (1) all DNNs avoided overfitting, and were statistical stable under ten-fold cross-validation; (2) the optimized DNN3-2 model (tuned hidden layer count, neurons/hidden layer, L₂ regularization, and dropout) achieved peak performance, explaining 69% of CW variance with residuals with stable variance and excellent coverage properties; (3) tree size, neighborhood competition, species identity, and site quality were the most important predictors; and (4) stand parameters calculated from competitive neighborhoods defined by the HTM, particularly mean stand crowding, Simpson’s index (1-D), and Shannon’s index (H′), significantly improved prediction accuracy. By integrating DNN with the HTM, our approach allows for accurate prediction of individual-tree CW in natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Full article

Stroke often leads to persistent upper limb dysfunction that impairs activities of daily living, yet objective biomechanical indicators for precise assessment remain limited. This study aimed to characterize phase-specific impairments in energy output, torque stability, and muscle coordination during the hand-to-mouth (HTM) task and to explore their potential for improving rehabilitation evaluation. Motion data from 20 stroke patients and 20 healthy controls were recorded using wearable surface electromyography and inertial measurement unit systems. A musculoskeletal model was applied to calculate joint torque, mechanical work, torque smoothness, and a novel torque-based co-contraction index across four movement subphases. These phase-specific metrics demonstrated significant correlations with clinical motor impairment scores, confirming their clinical validity. Significant dynamic features were then selected to construct machine learning models for group classification. Stroke patients showed reduced output capacity, increased torque fluctuations, and abnormal co-contraction patterns that varied across subphases. Among the classifiers, the quadratic support vector machine achieved the best performance, with an accuracy of 84.6% and an AUC of 0.853, surpassing models based on whole-task features. These findings demonstrate that phase-specific biomechanical features sensitively capture neuromuscular deficits in stroke survivors and highlight the potential of phase-specific biomechanics to inform future individualized rehabilitation assessment and treatment planning. Full article

Personal comfort systems (PCSs), which provide targeted heating or cooling to specific body parts, have emerged as a promising solution to enhance occupant comfort while reducing energy use in buildings. Among the many factors influencing PCS performance, heat transfer mechanisms (HTMs) play a pivotal role. However, a critical gap remains in the literature regarding the identification of optimal HTMs for achieving both thermal comfort and energy efficiency in PCSs. To address this gap, our study investigates the impact of conduction, convection, and radiation in PCSs on thermal comfort enhancement and energy performance under both heating and cooling modes. A meta-analysis was conducted, extracting data from 64 previous studies to evaluate the effects of HTMs of PCSs on thermal sensation vote (TSV), overall comfort (OC) and corrective energy power (CEP). Results indicate that PCSs typically improve users’ thermal sensation and comfort by about one scale unit in both heating and cooling modes. Radiative HTM is the most effective individual method, while combined conductive and convective HTMs perform best overall. Most PCSs operate efficiently, consuming less than 200 W/°C, with conduction in heating and convection in cooling being recommended for optimal comfort and energy efficiency. These findings suggest that selecting optimal HTMs for PCSs is crucial for achieving maximum comfort performance and energy savings. Data on combined HTMs of PCSs remain limited, underscoring the need for further research in this area. Future research should prioritize optimizing HTMs, especially radiative and combined methods, to maximize comfort and energy savings in PCS design. Full article

We investigated the influence of gold deposition on the magnetic behavior, biocompatibility, and bioactivity of CoFe₂O₄ (MCF) nanomaterials (NMs) functionalized with sodium citrate (Cit) or glycine (Gly). The resulting multifunctional plasmonic nanostructured materials (MCF-Au-L, where L is Cit, Gly) exhibit superparamagnetic behavior with magnetic saturation of 59 emu/g, 55 emu/g, and 60 emu/g, and blocking temperatures of 259 K, 311 K, and 322 K for pristine MCF, MCF-Au-Gly, and MCF-Au-Cit, respectively. The MCF NMs exhibit a small uniform size (with a mean size of 7.1 nm) and an atomic ratio of Fe:Co (2:1). The gold nanoparticles (AuNPs) show high heterogeneity as determined by high-resolution transmission electron microscopy (HR-TEM) and energy-dispersive X-ray spectroscopy (EDX). The UV-Vis spectroscopy of the composites reveals two localized surface plasmons (LSPs) at 530 nm and 705 nm, while Fourier Transformed-Infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) confirm the presence of Cit and Gly on their surface. Subsequent biocompatibility tests confirm that MCF-Au-L NMs do not exert hemolytic activity (hemolysis < 5%). In addition, the CCK-8 viability assay tests indicate the higher sensitivity of cancerous cells (A549) to the photoactivity of MCF-Au compared to healthy Detroit 548 (D548) cell lines. We use advanced microscopy techniques, namely atomic force, fluorescence, and holotomography microscopies (AFM, FM, and HTM, respectively) to provide further insights into the nature of the observed photoactivity of MCF-Au-L NMs. In addition, in situ radiation, using a modified HTM microscope with an IR laser accessory, demonstrates the photoactivity of the MCF-Au NMs and their suitability for destroying cancerous cells through photodynamic therapy. The combined imaging capabilities demonstrate clear morphological changes, NMs internalization, and oxidative damage. Our results confirm that the fabricated multifunctional NMs exhibit high stability in aqueous solution, chemical solidity, superparamagnetic behavior, and effective IR responses, making them promising precursors for hybrid cancer therapy. Full article

Mitochondrial metabolism in the trabecular meshwork (TM) plays a critical role in maintaining intraocular pressure homeostasis by supporting the energy-demanding processes involved in aqueous humour outflow. In primary open-angle glaucoma, oxidative stress impairs mitochondrial function, leading to TM dysfunction. Therefore, understanding and targeting mitochondrial health in TM cells could offer a novel therapeutic strategy. Pyrroloquinoline quinone (PQQ) is a redox cofactor with antioxidant and mitochondrial-enhancing properties. However, its effects on human TM (HTM) cells remain largely unexplored. This study examined PQQ cytoprotective effects against H₂O₂-induced oxidative stress in HTM cells. Seahorse analyses revealed that PQQ alone improves mitochondrial respiration and ATP production. Moreover, PQQ mitigates H₂O₂-induced cellular damage and preserves mitochondrial function by normalising proton leak and increasing ATP levels. Furthermore, TEM and confocal microscopy showed that PQQ can partially alleviate structural damage, restoring mitochondrial network morphology, thereby leading to reduced cell death. Although these protective effects seem not to be mediated by changes in mitochondrial content or activation of the SIRT1/PGC1-α pathway, they may involve modulation of SIRT3, a key factor of mitochondrial metabolism and homeostasis. Overall, these results suggest that PQQ may represent a promising candidate for restoring mitochondrial function and reversing oxidative damage in HTM cells. Full article