As transistor densities surge and boost frequencies intensify transient CPU heat fluxes, passive thermal management with phase change materials (PCMs) offers a zero-power alternative. However, the low thermal conductivity of PCMs demands high-conductivity fins, and no existing study has employed topology optimization to design a multi-layer stacked PCM heat sink for long-duration cooling. The present study's novelty is a four-row PCM heat sink with aluminum fins generated by density-based topology optimization (SIMP, 20% solid fraction, Helmholtz filtering) under steady operating conditions. The optimized geometry is simulated via a transient enthalpy-porosity CFD model resolving conjugate heat transfer, laminar natural convection, and phase change, validated against experimental data (RMSE=2.44 K, NRMSE = 3.26%). Over eight hours of continuous CPU load, sequential melting occurs: the lowest row fully melts in 66 min, the second in 275 min, while the third reaches a liquid fraction of 0.54 and the top remains solid. The heat-sink base temperature stabilizes at 355.72 K (≈82.6 °C) after 8 h, which lies near the upper limit of typical commercial CPU operating temperatures, with a difference of only 3.51 K, compared to 388.36 K for a conventional single-layer radial-fin heat sink after just 1 h—a 42.4 K reduction, reflecting the combined effect of the multi-layer configuration and the topology-optimized fin geometry. Latent heat (2.6 kJ per row) dominates the lower blocks, while natural convection is suppressed to micrometre-per-second velocities.
Trading strategies are commonly utilized to identify optimal trading signals and mitigate trading volatility. Enforcing a trading strategy to include technical indicators from different categories results in resilient and robust performance. To that end, we take the Diverse Technical Indicator Pool (DTIP) into consideration. This paper proposes a memetic-based optimization algorithm for finding a more diverse technical indicator-based strategy, which enables robust and adaptable performance across varying market regimes. The DTIP ensures that selected technical indicators are drawn from distinct categories: volatility, momentum, volume, and trend, thereby providing resilient signals that resist overfitting. Building on previous research, this study conducts a rigorous comparative analysis of three local search algorithms (FA, PSO, and SA) within the memetic framework. Empirical results from a walk-forward validation on the US, Taiwan, and Cryptocurrency markets demonstrate that SA consistently yields the highest stability and risk-adjusted returns. Furthermore, the proposed framework is shown to significantly outperform a machine learning baseline, particularly in high-volatility assets like Bitcoin, where it reduced volatility while maintaining superior profitability.
Polymer gears are increasingly applied in precision transmission systems due to their low weight, corrosion resistance, and acoustic advantages, although their limited wear resistance continues to restrict long-term durability. To address this limitation, this study investigates epoxy-based composite gears reinforced with metal powders and modified with solid lubricants, aiming to enhance load-bearing and sliding wear performance. Epoxy composites are prepared by incorporating copper, iron, or aluminum powders as mechanical reinforcements and introducing polytetrafluoroethylene (PTFE), molybdenum disulfide, tungsten disulfide, or graphite as solid lubricants to improve lubricity. Ball-on-disc tribological tests under a 2 N normal load and a sliding speed of 0.1 m/s indicate that PTFE-modified composites exhibit the lowest steady-state friction coefficients, reaching 0.203, 0.276, and 0.352 for copper-, iron-, and aluminum-filled systems. Wear track measurements further show substantial reductions in wear depth and width, with PTFE-modified composites demonstrating improvements of 73.93
Coating extends the service life of infrastructure, reducing maintenance needs and supporting sustainability goals. Fly ash geopolymer coatings are promising alternatives to organic and cementitious systems due to their low-carbon, VOC-free composition and chemical compatibility with cementitious substrates. This study investigates the collective impact of mix chemistry, application method and substrate type governing the performance of geopolymer coatings. Coatings were applied by spraying and brushing at varying mix proportions onto steel, mortar, and plywood substrates. Results show that the S/L ratio controls microstructural integrity: low ratios promote shrinkage cracks, while an S/L ratio of 2.0 produces denser matrices and improved pencil hardness (HB). The substrate type emerged as the dominant factor in determining adhesion and durability. Mortar exhibited the strongest interfacial bonding, confirmed by cohesive failure (100
As a powerful detection technique, the surface-enhanced Raman scattering (SERS) has gained significant attention owing to its ability to reveal unique fingerprint information. Especially, the flexible SERS substrate due to its exceptional features such as portability, ease of integration of nanomaterials, rapid in-situ and on-site detection makes it an ideal platform for the real-time detection. This paper proposes a flexible SERS substrate based on carrot cellulose nanofibrils (CCNFs) modified with polyetheramine (M2070) via the freeze-drying technique followed by the photochemical decoration of gold nanoparticles (AuNPs). The amphiphilic structure of M2070 promotes the abundant chelation sites, facilitating the uniform growth and strong adherence of AuNPs throughout the CCNF-M2070 matrix. The fabricated flexible AuNPs@CCNF-M2070 SERS substrate exhibit superior Raman enhancement, low limit of detection of 1.08 × 10–10 M, excellent mechanical durability for over 100 cycles of bending and twisting test, high homogeneity, and reproducibility towards the detection of pesticide, thiram with a relative standard deviation value of less than 10
