Search Results (28,285)

Background: Image-based dietary assessment provides a more intuitive approach for nutritional monitoring and health management. However, in multi-category bowl-based meals, food boundary adhesion, spatial stacking, and staple-food occlusion by upper-layer dishes still affect the accuracy of volume, weight, and nutritional composition prediction. Methods: This study proposes a nutrition prediction method for bowl-based foods by integrating semantic segmentation, multi-view three-dimensional reconstruction, and occlusion compensation. The improved DBP-FDSNet was used to extract food-category masks from top-view RGB images, while detail enhancement, boundary-assisted supervision, and spatial position encoding were incorporated to improve the segmentation quality of food boundaries and adhesion regions. The visible food surface inside the bowl was reconstructed using a bowl instance model and RGB-TSDF-based multi-view fusion, and the two-dimensional semantic results were mapped into the height-field parameter domain for category-level volume integration. For partially occluded, severely occluded, or completely invisible staple foods, a layered compensation strategy was introduced to reduce staple-food volume prediction errors and the erroneous assignment of upper-layer food volume. Food weight and whole-bowl Calories, Fat, Carbohydrate, and Protein were finally predicted using food density and a nutritional composition database. Results: DBP-FDSNet achieved a meanIntersectionoverUnion ($mIoU$) of 80.51% and a BoundaryF1 Score ($bF1$) of 85.73%. At the whole-bowl level, the MeanAbsolutePercentageError ($MAPE$) values for Calories, Fat, Carbohydrate, Protein, and total food mass were 13.23%, 18.51%, 14.18%, 13.35%, and 10.85%, respectively. Conclusions: The method improves the stability of category-level volume and nutritional composition prediction in complex bowl-based meal scenarios, providing a feasible solution for image-based dietary assessment and intelligent nutrition management. Full article

In recent decades, humanoids have become more popular in various applications. However, their applications in human life are more than those in industry. In this paper, a humanoid is used to capture the sets of point clouds of an object for three-dimensional reconstruction. The structured light camera is widely used across diverse 3D scanning applications due to its high resolution, rapid acquisition capability, and adaptability to various material surfaces. Therefore, the humanoid developed by our team holds a structured light camera which captures the point clouds of an object put on a platform for the reconstruction of its 3D digital model. The platform is rotated so that the structured light camera can capture the image of all view angles on the object. Meanwhile, the structured light camera captures point clouds, and the camera of the humanoid recognizes the QR code on the platform so that the sets of point clouds can be distinguished by view angles on the object. Then, the automated registration process of the point cloud sets for a 3D model based on the point-to-plane iterative closest point (ICP) algorithm is proposed. The process incorporates preprocessing techniques, such as downsampling and normal vector estimated from plane, and utilizes the ICP algorithm for registration, ultimately achieving markerless and precision automatic merging of multi-view point cloud data. Experimental results demonstrate that the proposed method with the humanoid can effectively improve the completeness and accuracy of 3D reconstruction models, significantly reduce manual intervention, and enhance the system’s versatility and practical feasibility. Key parameters adjusted for more efficient computation of the ICP algorithm are revealed. In addition, the experimental results of the proposed ICP compared with G-ICP are also included. Full article

Stick–slip vibration, reversal, axial impact, and dynamic instability are major challenges in deep drilling operations and are closely associated with nonlinear bit–rock interaction. To investigate these phenomena, this study develops a nonlinear axial–torsional coupled dynamic model of a drill-string system by integrating rock surface morphology evolution with a Stribeck dry friction model. The drill string is discretized into a distributed lumped-parameter model with coupled axial and torsional degrees of freedom. A surface morphology matrix is introduced to simulate the rock-cutting process, while the Stribeck friction model is employed to characterise the nonlinear frictional behaviour at the bit–rock interface. Time-domain simulations, bifurcation analysis, and frequency spectrum analysis are performed to investigate the dynamic responses of the system. The results indicate that rock surface morphology evolution significantly influences the contact conditions and frictional behaviour at the bit–rock interface, and together with dry friction induces transitions among steady-state, multi-periodic, and chaotic motions. Stick–slip vibration is accompanied by axial impact, bit bounce, and a reduction in the dominant torsional vibration frequency. In addition, variations in both driving and frictional parameters can trigger dynamic instability and state transitions. The proposed model provides an effective framework for analysing nonlinear drilling dynamics and offers theoretical guidance for drill-string vibration suppression, drilling parameter optimisation, and efficient drilling in complex formations. Full article

The modernization of traditional Chinese medicine (TCM) is significantly impeded by the elusive material basis of its meridian system and by a lack of objective, quantitative diagnostic standards. Recent breakthroughs in photonic technologies and optoelectronic chips offer transformative paradigms to address these systemic bottlenecks. This review systematically evaluates the complete academic and engineering chain of “Photonic TCM,” spanning fundamental mechanisms, optical diagnostics, advanced therapeutics, and core chip-level technologies. Specifically, we analyze how ultra-weak photon emission (UPE), two-photon microscopy, and infrared thermography can objectify meridian dynamics and acupuncture pathways. For clinical translation, laser acupuncture has emerged as a robust, non-invasive modality for managing disorders such as chronic pain and insomnia, supported by cumulative evidence-based data. At the device level, vertical-cavity surface-emitting laser (VCSEL)-based photonic computing chips enable ultrafast herbal medicine recognition, while flexible optoelectronics and lab-on-a-chip systems lay the technical groundwork for wearable neuromodulation. Crucially, this review concludes that the Photonic TCM paradigm is transitioning from isolated clinical validation to integrated engineering implementation. We identify biological tissue scattering and parameter heterogeneities as the primary bottlenecks. To navigate these challenges, we propose that the field’s future should converge toward edge-computing-driven wearable closed-loop systems and multi-dimensional optical big data ecosystems. Ultimately, these technological trajectories will steer TCM from an empirical discipline toward a data-driven, precise, and standardized medical science. Full article

Volatile sulfur compounds (VSCs) are important contributors to mango volatile profiles but are challenging to analyze due to their trace concentrations, susceptibility to transformation, and interference from the complex fruit matrix. This study investigated how key extraction parameters—material chemistry, surface area-to-volume ratio, sorbent volume, temperature, and time—affect VSC extraction using mango as a complex botanical model. Three sorptive extraction configurations were evaluated: HiSorb (PDMS and DVB/CWR/PDMS) and a high-capacity combined headspace thin-film solid-phase microextraction and stir bar sorptive extraction (HS-TFSPME-SBSE) system. The optimized HS-TFSPME-SBSE configuration (40 °C, 150 min) provided the broadest VSC coverage, achieving limits of detection of 0.1–0.6 μg/kg and good linearity (R² > 0.9927). In spiked mango puree (10 μg/kg), HS-TFSPME-SBSE detected methyl mercaptan and diallyl trisulfide, which were not recovered using HiSorb (PDMS). Application to three mango cultivars (Golden Honey, Sindhura, and Palmer) revealed broader VSC profiles and enabled differentiation of cultivars and tissues (flesh and peel) through principal component analysis (PCA). Distinct cultivar-associated VSC patterns were observed, including elevated dimethyl disulfide in Golden Honey and ethyl 3-(methylthio)-cis-2-propenoate in Sindhura. These findings demonstrate the suitability of HS-TFSPME-SBSE for sensitive profiling of trace VSCs in complex fruit matrices. Full article

The cold V-bending of Grade 4 titanium bone plates at room temperature is a critical forming operation that must be optimized to control strain localization and springback and to reduce the risk of surface cracking. This study proposes a combined experimental, numerical, and artificial intelligence-based approach for the analysis and optimization of this process. Tensile tests were first performed to characterize the mechanical behavior of the material and to calibrate the constitutive law used in the finite element model. The numerical model was then validated through comparison with experimental V-die bending results. A design of experiments was subsequently applied to investigate the effects of sheet thickness, die shoulder distance, punch radius, and punch displacement on two key responses: equivalent plastic strain (PEEQ) and spring back. The results show that sheet thickness and die shoulder distance are the most influential parameters. In addition, artificial neural network models were developed to predict process responses, and Bayesian regularization showed the best overall predictive performance among the tested ANN training algorithms, namely Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient. The proposed framework provides a basis for optimizing the forming of titanium orthopedic implants. Full article

In this study, the chemical leaching and bioleaching behaviors of a complex carbonate-rich sphalerite ore were statistically evaluated using Response Surface Methodology (RSM) based on Central Composite Design (CCD) and the effects of process parameters on Zn and Fe recovery were assessed. The maximum Zn recovery obtained in the chemical leaching process was 59.16%, while the maximum Zn recovery in the biological leaching process was determined to be 37.26%. Statistical analyses showed that the solid-to-liquid ratio and leaching time had a significant effect on chemical leaching performance, while the effect of Fe(II) concentration on biological leaching performance was limited. During biological leaching, pH increases occurred due to the ore’s carbonate-rich structure, and periodic sulfuric acid adjustments were made to maintain suitable biological leaching conditions. The findings indicate that carbonate-rich mineral structures significantly influence both chemical and biological leaching behavior, and that this effect must be taken into account in the processing of complex sphalerite ores. Full article

This study investigates the dynamic response and local stability of gravity-anchored foundations constructed in layered argillaceous sandstone under the coupled effects of wet–dry cycling degradation and cyclic vehicle loads. Based on in situ direct shear tests and FLAC3D 7.0 numerical simulations, a concrete–rock interface model, a rock mass direct shear model, and a three-dimensional dynamic model of the anchored foundation were developed. The parameters of the interface model were validated using the results of the direct shear tests. Wet–dry cycling degradation was subsequently incorporated to analyze the cyclic shear response of the interface and rock mass under different numbers of cycles. Cyclic vehicle loads were modeled as increments in main cable tension with an equivalent sinusoidal waveform. The results indicate that as the number of wet–dry cycles increases, the cyclic shear hysteresis loops shift overall toward lower shear stress levels. Peak shear stress decreases by approximately 49.26–51.64% compared to the natural state, and the hysteresis loop area decreases significantly. This indicates that wet–dry cyclic degradation weakens the cyclic shear resistance and energy dissipation capacity of the contact surface and rock structural planes. Dynamic analysis results for the anchor foundation indicate that wet–dry cycling degradation significantly increases the displacement response levels of the rock mass near the front toe and rear heel. Specifically, under the n = 20 condition, the displacement at the last peak increased by approximately 109.3–123.9% compared to the undisturbed state; simultaneously, the local plastic zones in the rock mass surrounding the anchorages gradually expanded, and the local safety factors of the rock mass near the toe and heel decreased overall. This study elucidates the degradation mechanisms and dynamic behavior of gravity anchors under the combined action of environmental and operational loads, providing a basis for the design and safety assessment of foundations for long-span suspension bridges. Full article

Various models exist for predicting the sound absorption coefficient of porous materials, including the capillary model within the Rayleigh model. However, many of these models require an acoustic parameter known as ventilation resistance, which is difficult to determine theoretically for fibrous materials such as wool. This study theoretically estimated the sound absorption coefficient of glass wool using computed tomography (CT) images. Voids within the glass wool were approximated as clearances in two parallel planes. Sound absorption characteristics were theoretically estimated by determining the propagation constant and characteristic impedance within these voids. Furthermore, the theoretical analysis accounted for the tortuosity of the material. During CT image processing, corrections were applied to approximate the actual fiber surface area by accounting for the fiber inclination relative to the direction of sound wave incidence. This correction was determined by approximating the fiber cross-section visible in the CT image as an ellipse and using the resulting ellipticity. A two-microphone impedance measurement tube was used to measure the normal incident sound absorption coefficient. The proposed method provides fundamental insights into the model-based development of sound-absorbing materials and is expected to contribute to cost reduction by eliminating the need for conventional air permeability tests. Full article

This study evaluates the influence of heat treatment parameters on the microstructure and corrosion resistance of CA-50 low-carbon steel rebars (0.20–0.25 wt.% C) processed by the Thermex route. A full 2³ factorial design combined with response surface methodology was employed to investigate the effects of residence time (15–35 min), heating rate (5–15 °C/min), and soaking temperature (730–850 °C). Corrosion behavior was assessed by linear potentiodynamic polarization and electrochemical impedance spectroscopy in 3.5 wt.% NaCl solution. The corrosion potential (E_corr) varied between −520.6 and −618.1 mV, with optimal values close to −535 mV obtained at low heating rates and short residence times. Polarization resistance (R_p) ranged from 70.4 kΩ to 166.6 MΩ, with the highest value observed for treatment at 790 °C, 10 °C/min, and 25 min, representing an increase of more than fivefold compared to the reference condition. Statistical analysis revealed that residence time and heating rate significantly affect E_corr (R² = 96.8%), while R_p is governed exclusively by residence time (p = 0.004). Microstructural analysis correlated refined and homogeneous ferritic–pearlitic structures with improved corrosion resistance, whereas grain coarsening led to severe electrochemical degradation. Full article

Accurate estimation of daily net radiation (R_{n_daily}) at high spatial resolution (1 km) over the Tibetan Plateau (TP) is crucial for understanding land surface energy budgets and climate dynamics. This study proposes a densely connected multilayer perceptron (DenseMLP)-based transfer learning framework, with a two-stage strategy (coarse pre-training on GLASS R_{n_daily}, followed by fine-tuning on limited TP ground observations) using MODIS land surface parameters and auxiliary data to generate 1 km R_{n_daily}. When evaluated on the training set, the proposed model achieves an overall R² of 0.87, MAE of 16.06 W m⁻², RMSE of 21.94 W m⁻², and a near-zero bias of 0.07 W m⁻². On an independent test set, the model maintains robust performance with R² = 0.83, MAE = 17.43 W m⁻², RMSE = 22.55 W m⁻², and bias = −1.12 W m⁻². The method exhibits consistently low bias across individual sites (mostly within ±3.7 W m⁻²) and accurately captures seasonal variability. When applied to the entire TP for 2018, the 1 km R_{n_daily} product reveals clear aspect-related terrain effects and a distinct annual cycle. This framework effectively mitigates site-dependent errors, providing a useful reference for long-term R_n product development over the TP. Full article

CoCrFeMnNi high-entropy alloy (HEA) coatings were fabricated on an S41500 stainless steel substrate by laser cladding and subsequently strengthened using machine hammer peening (MHP) at three hammering energies of 1.7 J, 3.5 J, and 5.0 J. The effects of MHP treatment on the phase structure, surface morphology, microhardness, and tribological properties of the coatings were systematically investigated. The results showed that all coatings retained a single-phase face-centered cubic (FCC) structure after MHP treatment, indicating excellent microstructural stability during impact-induced strengthening. With increasing hammering energy, the surface morphology gradually evolved from discrete hammering indentations to a more continuous orange-peel-like texture, while the surface roughness initially increased and then decreased. MHP significantly enhanced the surface hardness of the coatings. In particular, the MHP3.5 sample exhibited the highest surface hardness of approximately 420 HV, representing an increase of about 120% compared with the untreated coating. Under dry sliding conditions at a load of 30 N, the MHP3.5 sample exhibited the lowest and most stable friction coefficient, maintaining a steady-state value of approximately 0.40–0.45. Its specific wear rate decreased by nearly 45% compared with that of the untreated coating. The improved wear resistance was mainly attributed to the combined effects of strain hardening, grain refinement, and dislocation strengthening induced by machine hammer peening. Considering the hardness, friction coefficient, and specific wear rate results together, a hammering energy of 3.5 J was identified as the most suitable MHP parameter under the low-load wear conditions investigated in this study. Full article

This study investigates the electroosmotic flow (EOF) of viscoelastic Jeffreys fluids in a pH-regulated parallel-plate nanochannel, with a focus on analyzing the effects of solution pH, background salt concentration, and alternating current (AC) electric field frequency on flow characteristics. In micro- and nanoscale fluidic systems, surface charge characteristics critically govern electrokinetic flow. The surface charges in this study originate from the protonation and deprotonation reactions of silanol (SiOH) groups on the channel walls. Different from the constant surface electric potential assumed in existing studies, the surface electric potential here varies with solution pH and background salt concentration. By modulating solution pH and thereby tuning surface charge density, active and reversible control of EOF can be realized. By solving the coupled Poisson–Boltzmann equation, momentum equation, and Jeffreys constitutive equation, we obtain an analytical solution for the electric potential distribution and semi-analytical solution for the velocity field. The results show that under the chosen parameter conditions, the relaxation time λ₁ enhances the velocity amplitude, while the retardation time λ₂ weakens it. The EOF velocity amplitude of Jeffreys fluids is enhanced by greater pH deviation from the isoelectric point, lower ionic concentration, and higher electric field frequency. In nanochannel flows, the effect of the oscillating Reynolds number on the velocity amplitude is negligible. Full article

This paper presents Geo-Esthetics, an application-oriented workflow that uses remote sensing imagery as source morphology for generative design. The study addresses a design problem: how can large-scale terrestrial textures be extracted, abstracted, and organized as pattern references for parametric and visual design? Nine representative geomorphological settings were selected. For each case, Sentinel-2 imagery was cropped into a 2 km × 2 km geographic window, enhanced using spectral-index selection and Contrast Limited Adaptive Histogram Equalization (CLAHE), and used as an image prompt in Midjourney v6.0. A consistent prompt structure and parameter setting were applied. Four variants were generated for each case and screened according to topological fidelity, level of abstraction, and design applicability. Box-counting dimension and lacunarity were calculated to compare morphological complexity between source images and generated patterns. The cases show that hydrological, tectonic, desert, agricultural, and reef morphologies can be translated into design-oriented pattern prototypes for paving, façades, interfaces, acoustic elements, and biomimetic surfaces. The contribution of this work lies mainly in design methodology: it provides a documented workflow for connecting Earth observation data, generative AI, and design ideation, while retaining clear boundaries around model reproducibility, prompt sensitivity, case representativeness, and perceptual evaluation. Full article

To reduce environmental pollution caused by volatile organic compounds (VOCs) released during asphalt application, various porous materials have been used to adsorb asphalt VOCs due to their rich pore structures. However, asphalt VOCs are so complex that emission reduction mechanisms still require further study. In this study, Materials Studio was used to simulate the molecular dynamics of asphalt VOC adsorption by ZSM-5 zeolite. The adsorption heat, capacity, and energy of ZSM-5’s adsorption of the main asphalt VOCs was obtained by means of molecular simulation to reveal the adsorption rules and selectivity. Zeolite model simulations with different structures were run to investigate possibilities for the optimization of ZSM-5. In addition, the actual VOC emission reduction effects of ZSM-5 in asphalt were compared with the MS simulation results. The VOC emission reduction mechanism was discussed based on both microscopic simulations and macroscopic verification. The results show that hydrocarbon derivative VOCs are more likely to be adsorbed due to their higher polarity. The smaller molecules of these VOCs are easier to adsorb because they occupy a smaller pore volume. When several molecules are mixed, competitive adsorption occurs. The selective adsorption probabilities of n-hexane, 1-methylcyclopentene, and toluene increase. In relation to the structure of zeolites, the Si/Al ratio and pore size of zeolites can both affect adsorption ability. A low Si/Al ratio can increase the number of surface acid active sites, while a micro–mesoporous structure increases the pore volume. The actual emission reduction data confirm that computational simulation has high accuracy in evaluating VOC emission reduction based on physical adsorption. Low-Si/Al-ratio and micro–mesoporous zeolites show better emission reduction ability for non-benzene VOCs than high-Si/Al-ratio and microporous zeolites. The emission reduction efficiency is up to 44%. However, the aromatization reaction was more easily catalyzed by zeolites, leading to the discrepancy between the simulated adsorption data and the actual situation. In future work, the boundary conditions and parameter settings of the simulations should be changed to achieve greater accuracy. Full article