This study examines fluid flow and heat transfer in three-dimensional micro-scale porous domains formed by simple cubic (SC), body-centered cubic (BCC), and face-centered cubic (FCC) sphere packings under continuum and slip-flow regimes. A multi-relaxation-time lattice Boltzmann (MRT-LB) model was developed to capture velocity slip and temperature jump at fluid–solid interfaces. In continuum flow, strong flow obstruction and extended low-velocity regions were observed near solid boundaries, whereas slip flow reduced wall-induced stagnation and suppressed trailing-sphere vortices. Thermal results show an early temperature rise in continuum flow, while slip flow leads to lower peak temperatures due to enhanced near-wall convection. The Nusselt number increases with Reynolds number but decreases with Knudsen number and porosity, reflecting reduced effective surface area and intensified temperature jump effects. Friction factors decrease with increasing Reynolds number for all lattice types; however, low-porosity BCC structures exhibit the highest drag due to reduced inlet velocities. Permeability increases with Knudsen number and porosity, with SC and FCC configurations showing greater enhancement because of higher average flow velocities. To reduce computational cost, a deep-learning-based surrogate model was trained to replicate MRT-LB predictions of velocity and temperature fields. The surrogate achieves high accuracy with low prediction error, enabling rapid evaluation of slip-flow heat transfer and efficient parametric design of micro-porous thermal systems.
Classifying code snippet-based questions is essential for teaching, preparing assessment materials, and supporting intelligent learning systems in programming education. Traditional frequency-based encodings, such as TF-IDF, often fail to capture the contextual semantics within code-related questions. This study employs contextualized embeddings generated by the large language model Text-Embedding-3-Large (TE3L) to evaluate their effectiveness in classifying code-related questions. It further investigates which classifier architecture best complements the TE3L representation. Using a small-scale dataset of 171 SQL certification-style questions representative of course-level repositories, we analyze the classification complexity reduced by the TE3L scheme compared to TF-IDF. Then, we investigate classification performance under various classifier architectures with TE3L embeddings, including single models, boosting, and stacking ensembles. Results demonstrate that the TE3L scheme significantly reduces classification complexity and improves performance compared to the TF-IDF. Single classifiers, particularly the support vector machine with a linear kernel and the stochastic gradient descent classifiers, performed the best with the TE3L scheme and achieved an 11-percentage-point relative improvement over the benchmark in the weighted macro-average F1 score. The boosting and stacking techniques did not enhance performance, reflecting the challenges of ensemble learning under small-sample, imbalanced conditions. This work highlights the practical value of using LLM-based embeddings to automate question classification in low-resource educational contexts, supporting teachers in building intelligent assessment tools without requiring deep expertise in NLP or machine learning.
As the use of autonomous mobile robots (AMRs) has increased in material handling systems, it has become essential to incorporate their unique characteristics and constraints into the pickup and delivery problem. Additionally, request scheduling and charging arrangements should be considered simultaneously to maintain battery feasibility during transportation tasks. Realistic charging and discharging assumptions must also be integrated into the model. Minimizing request delays is crucial, as delays can negatively impact productivity. This study addresses the scheduling problem in material handling systems, aiming to minimize the total tardiness of transportation requests while considering the specific characteristics of AMRs. This study also considers the partial recharging policy, nonlinear charging, and load-dependent discharging. A mixed-integer linear programming model was developed and an adaptive large neighborhood search (ALNS) algorithm was constructed to minimize total tardiness, with travel time minimization also included. A full factorial design with seven factors was conducted, revealing that layout, transportation request size, the number of AMRs, and battery levels are significant factors influencing total tardiness, while all factors significantly affect travel time. The results underscore the importance of considering charging and discharging mechanisms in effectively solving this problem.
This study utilizes COMSOL Multiphysics (version 6.0) to design a planar ultra-broadband optical absorber with a multilayer configuration. The proposed structure consists of seven stacked layers arranged from bottom to top: W (h1, acting as a reflective substrate and transmission blocker), WSe2 (h2), SiO2 (h3), Ni (h4), SiO2 (h5), Mo (h6), and SiO2 (h7). One key finding of this study is that, when all other layer thicknesses are fixed, variations in the Mo layer thickness systematically induce a redshift in both the short- and long-wavelength cutoff edges. Notably, the long-wavelength cutoff exhibits a larger shift than the short-wavelength edge, resulting in an increased absorption bandwidth where absorptivity remains above 0.900. The second contribution is the demonstration that this planar structure can be readily engineered to achieve ultra-broadband absorption, spanning from the near-ultraviolet and visible region (360 nm) to the mid-infrared (6300 nm). An important characteristic of the proposed design is that the thickness of the h7 SiO2 layer influences the cutoff wavelength at the short-wavelength edge, while the thickness of the h6 Mo layer governs the cutoff position at the long-wavelength edge. This dual modulation capability allows the proposed optical absorber to flexibly tune both the spectral range and the bandwidth in which absorptivity exceeds 0.900, thereby enabling the realization of a wavelength- and bandwidth-tunable optical absorber.
Efficient and durable non-noble-metal electrocatalysts are essential for alkaline oxygen evolution reaction (OER). Herein, sulfurized FeCo layered double hydroxide (FeCoLDH-S) catalysts were directly fabricated on nickel foam via a two-step hydrothermal process, and the effect of sulfurization temperature was systematically investigated. Sulfurization at 180 degrees C resulted in a porous nanosheet architecture composed of crystalline Ni3S2 coupled with low-crystallinity iron oxyhydroxide species. X-ray photoelectron spectroscopy revealed positive shifts in Ni 2p binding energies, Fe3+-dominant surface states, and enhanced surface hydroxylation, indicating effective electronic modulation and favorable surface chemistry. Benefiting from these features, the FeCoLDH-S catalyst sulfurized at 180 degrees C exhibited improved OER activity, reduced charge-transfer resistance, favorable reaction kinetics, and stable long-term operation in alkaline electrolyte. This work demonstrates that controlled sulfurization is an effective strategy for tailoring interfacial structure and surface chemistry toward advanced OER electrocatalysts.
