Search Results (10,251)

Cascade hydropower scheduling is a high-dimensional, tightly constrained multi-objective optimization problem in which classical genetic and evolutionary algorithms struggle to find feasible solutions. Under random initialization, algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Non-dominated Sorting Genetic Algorithm III (NSGA-III), and the Constrained Two-Archive Evolutionary Algorithm (C-TAEA) rarely produce any feasible solution when the feasible region occupies a vanishingly small fraction of the search space. This paper presents a three-phase framework that combines physics-guided deep learning with evolutionary computation to accelerate both NSGA-II and NSGA-III. The method trains a Physics-Embedded Temporal Convolutional Network (PeTCN) as a differentiable surrogate model that explicitly incorporates physical constraints, applies gradient-based inverse optimization to obtain a feasible or near-feasible solution of high quality, and warm-starts NSGA-II or NSGA-III with that solution for efficient Pareto front exploration. Experiments on a real-world six-station cascade system show that, under a 1500 s fixed-time budget across 20 independent runs, Boosted NSGA-II and Boosted NSGA-III both find feasible solutions in all runs. Boosted NSGA-II and Boosted NSGA-III both reach the first feasible solution within roughly 50–60 generations of Phase 3 search on average, whereas standard NSGA-II produces no feasible run within the same budget and standard NSGA-III requires thousands of generations among its successful runs. The mean final hypervolume reaches $43.84\times {10}^6$ for Boosted NSGA-II and $46.52\times {10}^6$ for Boosted NSGA-III, and both boosted algorithms reach a target hypervolume of $35.00\times {10}^6$ in all 10 target-hypervolume runs. These results demonstrate that coupling physics-embedded surrogates with gradient-based initialization is an effective strategy for constrained multi-objective problems in which feasible solutions are extremely sparse. Full article

Weak-lensing observations of the Perseus Cluster now indicate a massive sub-halo associated with NGC 1264 and a connecting mass bridge in a system long treated as a benchmark relaxed cool-core cluster. Perseus is also known from X-ray observations to host large-scale gas sloshing and an ancient cold front extending to several hundred kiloparsecs. This paper uses Perseus as a motivation for a narrower population question: do nominally relaxed clusters retain merger history information in residual mass–gas offsets after the obvious signatures of an active merger have faded? A candidate remnant stress–energy interpretation is introduced as one possible covariant language for such a long-lived structure, but the empirical test does not require acceptance of that interpretation. The work then carries out a literature-based pilot test using the cold front outer radius as an independent merger history proxy, published mass–gas or gas tracer offsets for relaxed/cool-core systems, and a separate control cohort of actively dissociative mergers. The resulting three-regime comparison separates young active mergers, relaxed low-offset systems, and relaxed systems with sourced offsets above 5 kpc. For all seven Regime 3 (relaxed, offset $>5$ kpc) systems with vetted cold front/history proxies and sourced mass–gas offset measurements, the directional rank-order association has the predicted sign, ${\rho }_s=0.68$, with $p_{\mathrm{one}-\mathrm{sided}}\approx 0.047$ ($p_{\mathrm{two}-\mathrm{sided}}\approx 0.094$, $N=7$). The one-sided statistic crosses the conventional $5\%$ threshold. The sample mixes lensing–X-ray centroid offsets, BCG/X-ray peak offsets, and weak-lensing sub-halo separations, and the result is not a decisive population detection: it is a suggestive directional signal in a small heterogeneous archival pilot. Its significance is that a framework-derived directional diagnostic, specified before the sample was assembled, is non-zero in the predicted sense and can now be tested with a homogeneous weak-lensing/X-ray/SZ survey. Full article

Volume phase holographic gratings (VPHGs) are high-performance dispersive elements characterized by high diffraction efficiency and low noise. When used as dispersive components in imaging spectrometers for CO₂ detection, they can significantly enhance instrument performance, detection capability, and measurement accuracy. However, for short-wave infrared (SWIR) applications requiring high dispersion and operational efficiency, traditional design approaches struggle to effectively balance the trade-offs among multidimensional diffraction performance metrics, resulting in low optimization efficiency. Furthermore, as spectrometers require dispersive elements, established fabrication methods lack robust methodologies for producing large-area VPHGs. To address these gaps, we developed both a design approach and a fabrication process for VPH gratings tailored to CO₂ detection. On the design front, we propose a novel method that integrates a multi-objective simulated annealing optimization algorithm with Kogelnik’s coupled-wave theory. The optimized gratings achieve diffraction efficiencies of 95.35% (TE polarization) and 82.21% (TM polarization) across the target spectral range, with polarization sensitivity maintained below 6.57%. For fabrication, we developed holographic plate fabrication via a blade-coating technique coupled with an optimized aging protocol. A medium-to-large aperture holographic recording and exposure system with a wavefront error better than λ/25 RMS was developed. Post-processing conditions were systematically optimized based on experimental diffraction efficiency measurements, enabling the successful fabrication of VPHGs. It is explicitly noted that the experimental validation of the fabricated VPHGs is limited to the 1.620–1.630 μm wavelength range, while the full target design range of 1.620–1.650 μm has not been experimentally verified in this work. This work provides a valuable reference for the selection of dispersive elements for next-generation CO₂ detection satellites. The designed gratings fully meet application requirements, while the established fabrication process lays a solid foundation for the production of high-performance VPHGs. Full article

Hydrogen enrichment of compression ignition (CI) engines has emerged as a promising strategy to simultaneously enhance thermal efficiency and reduce carbon-based emissions. This study numerically investigates how hydrogen enrichment affects engine performance and emissions in methanol–diesel dual-fuel CI engines, a combustion mode gaining increasing attention for replacing fossil diesel with sustainable fuels, particularly in hard-to-abate sectors such as maritime transport. The simulations are based on the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, incorporating the RNG k–ε turbulence model, the Eddy Dissipation Concept (EDC) for turbulence–chemistry interaction, and the G-equation for turbulent premixed flame propagation. The numerical model is validated against experimental data for in-cylinder pressure and heat release rate at 45% methanol substitution ratio (by energy). The results indicate that increasing the hydrogen enrichment ratio (HER, defined on an energy basis) from 5% to 20% raises the Sauter mean diameter (SMD) of the diesel fuel from 20.2 µm to 28.0 µm (+38%), driven by reduced aerodynamic breakup intensity associated with modified gas-phase properties under hydrogen enrichment. Furthermore, hydrogen’s elevated adiabatic flame temperature and superior mass diffusivity intensify combustion, raising peak in-cylinder pressure from 75.2 to 79.1 bar (+5.2%), amplifying the peak heat release rate from 129 to 211 J/°CA (+63.6%), and elevating maximum in-cylinder temperature from 1542 to 1735 K (+193 K). Under the investigated CFD operating conditions, these thermodynamic gains translate into an engine-level 6% improvement in indicated thermal efficiency and a 14% reduction in indicated specific fuel consumption (accounting for hydrogen, methanol, and diesel) at HER 20%. On the emissions front, CO₂ declines by 24% in direct proportion to the carbon-containing fuel mass displaced by hydrogen substitution, while NOₓ increases approximately twofold from 0.10 g/kWh at HER 0 to 0.21 g/kWh at HER 20, driven by peak temperature elevation. These findings establish hydrogen-enriched methanol–diesel dual-fuel combustion as a viable pathway toward high-efficiency, low-carbon CI engine operation for heavy-duty transport applications. Full article

Dissimilar 7075-T6 and 6061-T6 aluminum alloy joints were fabricated using pulsed metal inert gas (P-MIG) welding with ER5356 filler wire. The effects of welding current (224 A, 234 A, and 244 A) on macro-morphology, microstructure, mechanical properties, and corrosion behavior were systematically investigated. As welding current increased, the top and bottom reinforcements first increased and then decreased, reaching maximum values at 234 A, while the front weld width exhibited the opposite trend. The weld zone consisted of equiaxed and dendritic grains, with partial remelting of AlFeMnSi intermetallic compounds observed in the heat-affected zones. The microhardness and tensile strength of the joints followed a similar trend of first decreasing and then increasing with welding current, achieving a maximum tensile strength of 203.9 MPa at 244 A, corresponding to 89.5% of the 6061-T6 base metal strength. Corrosion resistance varied across regions depending on the evaluation method. In intergranular corrosion tests, the 7075-HAZ showed the highest susceptibility due to grain boundary segregation of Mg and Zn. In electrochemical tests, the WZ exhibited the poorest corrosion resistance. For the 7075-HAZ, optimal corrosion resistance was achieved at 234 A, attributed to a stable passive film and uniform precipitate distribution. These findings provide valuable guidance for optimizing P-MIG welding parameters for dissimilar 7075/6061 aluminum alloy joints. Full article

Speed information is crucial for controlling distributed electric-drive loaders, especially for driving and operation. Due to complex working conditions, the wheels of the loader often experience different conditions, leading to inaccurate speed estimation. To solve this, this paper proposes a multi-sensor fusion speed estimation method based on a Finite State Machine (FSM). The method uses the FSM to identify the wheel states and adaptively switches between the weighted average method and integration method to estimate the vehicle’s speed accurately. When all wheels are slipping, the acceleration integration method is used, starting from the latest trustworthy speed estimate. When the wheels are not slipping, the speed is estimated using the weighted average of the trustworthy wheels. Additionally, the method addresses the relative motion between the front and rear vehicle bodies caused by articulated steering by using an articulated steering projection method to ensure accurate wheel state estimation from IMU signals. Simulation and hardware-in-the-loop experiments show that the proposed method can accurately estimate vehicle speed under various road conditions. Specifically, under low-adhesion road conditions with all four wheels in a slipping state, it improves speed estimation accuracy by over 75% compared to traditional methods such as simple averaging, selective averaging, and pure integration. Full article

Ship classification in synthetic aperture radar (SAR) imagery is essential for maritime surveillance but remains challenging due to limited resolution, insufficient textural details, and difficulties in effectively fusing multimodal information. Existing methods either rely on handcrafted features with limited adaptability or employ simplistic fusion strategies that fail to fully exploit the complementary guidance across modalities. To address these issues, we propose a multimodal fusion network based on a learnable feature preprocessing front-end (LFPF-MFN), which integrates polarimetric, textural, and geometric information in an end-to-end learnable manner. Specifically, LFPF-MFN introduces a learnable preprocessing front-end to embed scattering and enhanced textural features. Meanwhile, geometric information from the Automatic Identification System (AIS) is incorporated through textual embedding, and effective multimodal fusion is achieved via a bidirectional cross-attention mechanism. Extensive experiments on the OpenSARShip 2.0 dataset demonstrate that the proposed method achieves state-of-the-art performance in both three-class and six-class classification tasks, validating the effectiveness of each designed module and the superiority of the multimodal fusion strategy. Full article

Reducing carbon emissions during construction is essential for meeting dual carbon targets. Current green scheduling methods assume fixed emission factors, overlooking time-dependent variations driven by grid peak-valley patterns. Under interval duration uncertainty coupled with tight dynamic carbon budgets, conventional algorithms struggle with sparse feasible solutions and slow Pareto front convergence. We formulate a bi-objective interval RCPSP model with time-varying carbon emission factors that minimizes both interval makespan and total carbon emissions. A possibility degree measure converts scalar carbon budgets into linearized hard constraints. To solve this NP-hard problem, we propose the Knowledge-Driven Interval Multi-Objective Evolutionary Algorithm (KD-IMOEA), which integrates four components: Knowledge-Driven Initialization (KDI), Adaptive Time-window Carbon-aware Decoding (TCD), Carbon Budget-aware Repair Mutation (CBM), and Interval Pareto Elite Archive (IPA), forming an end-to-end carbon-aware optimization pipeline. We validate KD-IMOEA on J30 through J120 benchmark instances; results show it outperforms four established algorithms, including NSGA-II, in both convergence and distribution, with hypervolume (HV) gains up to 6.3%. A green building case study confirms that KD-IMOEA exploits spatiotemporal decoupling to identify float time and assign energy-intensive machinery to lower-carbon operating profiles. At the optimal compromise makespan of 169.5 days, this strategy cuts carbon emissions by 3.07% over traditional baselines, enabling management-driven emission savings without extending project duration. Full article

To address the scientific challenges associated with complex microscopic pore structures and the unclear mechanisms of miscible gas injection in typical low-permeability carbonate reservoirs in the Middle East, online nuclear magnetic resonance (NMR) imaging experiments were conducted during water-alternating-CO₂ miscible flooding. The microscopic oil mobilization mechanisms were quantitatively investigated for different pore structure types and at various displacement stages. The results indicate that water-alternating-CO₂ miscible flooding achieves a relatively high degree of oil mobilization in large and medium pore–throat structures. This behavior is likely associated with Jamin-type flow resistance effects and flow regulation induced by gas–water alternating slugs. Differences in microscopic oil mobilization are mainly observed in mesopores (0.3–1.5 μm). The recovery degrees of mesopores in Cores 1, 2, and 3 reach 89%, 94.2%, and 78%, respectively, contributing 93.7%, 80.6%, and 50.9% to the total oil recovery. The degree of microscopic heterogeneity controls the distribution of remaining oil in core slices after breakthrough of the displacement front. In Core 1, the signal amplitude exhibits a gradual and uniform decline, indicating that gas–water alternating injection suppresses gas channeling and improves mobility control. In Core 2, the signal amplitude decreases more rapidly with increasing heterogeneity. In Core 3, the signal disparity continues to intensify, leading to the formation of dominant gas–water channeling pathways, while low-permeability pore–throat structures evolve into typical bypassed oil zones. As the CO₂–oil contact front progressively advances toward the outlet end, the swept volume gradually decreases due to the development of preferential flow channels. Consequently, significant remaining oil accumulation occurs near the outlet region. Full article

Flexible honeycomb skins offer a promising route for achieving continuous shape adaptation in morphing aircraft. In practical service, however, the skin must simultaneously accommodate large in-plane deformation while maintaining sufficient out-of-plane load-bearing capacity, which poses a fundamental design challenge. To address this trade-off, this study investigates an SMA-based U-shaped honeycomb under combined tensile deformation and aerodynamic pressure. A parametric finite element model incorporating SMA superelasticity is established, and an automated Abaqus–modeFRONTIER framework is developed for multi-objective optimization under dual loading conditions. The curvature radius, parallel-segment length, and middle-beam length are selected as design variables. The optimization objectives are defined as minimizing the maximum local strain under a prescribed tensile displacement and reducing the Z-direction displacement under aerodynamic loading as an indicator of out-of-plane bending resistance. The resulting Pareto front reveals the trade-off between flexibility and load-bearing capacity, and the sensitivities of the key geometric parameters are analyzed. Compared with the initial design, a representative optimized solution reduces the maximum local strain by 58.5% and the Z-direction displacement by 61.3%. These results provide a numerical basis for the design of SMA-based flexible skins for morphing aircraft. Full article

Crashes involving freight and commercial vehicles impose substantial human and economic costs, yet most severity studies pool vehicle types or focus exclusively on heavy trucks, masking class-specific risk mechanisms. This study estimates separate Random Parameters Binary Logit models with heterogeneity in means and variances for three vehicle categories—heavy-duty multi-axle trucks (n = 6512), two-axle trucks (n = 2656), and light-duty pickup trucks (n = 23,477)—using 32,645 crash records from Thailand’s national highway network (May 2022–December 2024). Pairwise transferability tests rejected parameter transferability, with four of six comparisons exceeding the 97 percent confidence level (three of these above 99 percent; χ² = 85.38 to 240.01), confirming that disaggregate estimation is statistically warranted. Three core findings emerge: First, although barrier medians, cut-in-front maneuvers, and sideswipe crashes affect severity in consistent directions across all vehicle types, their magnitudes differ sharply: the protective effect of barrier medians is nearly six times larger for two-axle trucks (ME = −0.160) compared to heavy-duty trucks (ME = −0.028). Second, several determinants are class-specific: dark unlit conditions elevate severity only for two-axle trucks (ME = 0.128), flush medians only for heavy-duty trucks (ME = 0.040), and raised medians only for light-duty pickups (ME = 0.042). Third, no random parameter is common to all three models. Pooled models, therefore, impose misleading homogeneity assumptions; vehicle-type-specific estimation is essential for targeted safety policy. Full article

Background: Multi-objective optimization (MOO) is widely used in engineering design and energy systems to represent trade-offs through Pareto fronts. Yet practical deployment requires moving from a non-dominated set to an implementable preferred design, and this decision step is often treated implicitly. Many studies equate decision support with improved Pareto front generation or visualization, while decision-maker preferences are assumed, weakly specified, or not elicited from stakeholders. Methods: A two-phase scoping evidence synthesis with PRISMA-informed reporting was adopted to map the literature and synthesize explicit Pareto-front decision-support mechanisms. Phase 1 produced a broad evidence map of how Pareto-front decision support is framed and clustered studies by primary contribution, while Phase 2 conducted a focused synthesis of explicit Pareto-front decision-support methods using refined searches in Scopus and SpringerLink. Results: Phase 1 mapped 46 studies; only 10 reported an explicit reproducible Pareto front solution-selection mechanism. Phase 2 included 17 studies and identified four method families: post hoc scoring and ranking, compromise aggregation, interactive preference-guided exploration, and preference elicitation and learning. Conclusions: The literature remains dominated by Pareto front generation and exploration rather than reproducible final solution selection; future work should strengthen preference elicitation, transparency, sensitivity analysis, and uncertainty-aware recommendation stability. Full article

Ion-exchange-strengthened 0.7 mm aluminosilicate glass offers a promising route to lightweight, mechanically robust front covers for building-integrated photovoltaic (BIPV) modules, but systematic characterization at sub-millimeter thicknesses remains limited. This study investigated 100 × 60 × 0.7 mm glass samples subjected to Na⁺/K⁺ ion exchange (6 h, 430 °C, KNO₃) and characterized mechanical and optical properties relevant to BIPV applications. Depth of layer (DOL) was cross-validated using three independent methods, mass gain diffusion modeling (31–37 μm), elasto-optic measurements (FSM-6000: 38–42 μm), and EDS Na/K depth profiling (35–40 μm), confirming consistent strengthened layer depth of 35–40 μm. Surface compressive stress measured 733 MPa (Series 2) and 773 MPa (Series 3), significantly exceeding conventional PV cover glass (490–515 MPa, 1 mm thickness). Vickers hardness increased by 17.7% (490 → 596 HV, p < 0.0001), demonstrating enhanced damage tolerance. Spectrophotometric analysis (200–2400 nm) showed transmittance >91% (380–2000 nm) and >92% (600–2000 nm) for both as-received and strengthened glass, confirming no optical degradation (p = 0.29–0.41). The 78–83% mass reduction relative to standard 3.2–4 mm glass, combined with superior CS/DOL and preserved optical performance, establishes ion-exchanged 0.7 mm aluminosilicate glass as a strong material-level candidate for next-generation lightweight BIPV modules. Future work requires module-scale mechanical validation (bending, impact testing per EN/IEC standards) and techno-economic assessment to verify system-level benefits. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Hydrotreatment is vital for producing high-quality liquid fuels in petroleum refining and its air coolers are critical components prone to severe corrosion under high-temperature and high-pressure conditions. Ammonium salts from NH₃-HCl and NH₃-H₂S reactions, particularly ammonium chloride precipitated during cooling, readily deposit on tube surfaces. Strong temperature gradients and complex flow conditions may severely affect air cooler inlets and front sections. To enhance the refining process reliability, an experimental setup was established to investigate the water injection removal of ammonium chloride particle deposits in air cooler tube bundles. Results show that water injection effectively removes ammonium chloride particles. Particle size has a minor influence, whereas inlet velocity, temperature, and water injection rate significantly affect removal efficiency. Increasing inlet velocity from 2 to 5 m/s, temperature from 80 to 110 °C, and water injection rate all enhance removal efficiency. Furthermore, differences between two-row tubes were also observed: the second-row tube exhibits a higher removal ratio due to liquid film formation, which increases Reynolds number and shear force, thereby enhancing dissolution. These findings provide experimental support for optimizing water injection strategies to mitigate corrosion, improving hydrotreatment unit reliability and safety, ensuring the continuous operation of the petroleum and fuel processing industry. Full article