Search Results (1,539)

This paper addresses a dynamic multi-period pricing problem that incorporates time-varying contextual information and inventory constraints. Sales are modeled as a function of both price and a multidimensional context vector, which may include factors such as customer location, income, loyalty, competitor prices, and promotional activity. This formulation captures complex market dynamics over a finite selling horizon. The problem is formulated as a quadratic programming model, and two alternative solution approaches are proposed. The first uses a multivariate regression model to approximate the sales function linearly, allowing an exact quadratic programming solution that serves as a benchmark. The second is a ‘learnheuristic’ algorithm that combines a nonlinear sales learning model with metaheuristic optimization to generate high-quality pricing strategies under realistic operational constraints. Computational experiments demonstrate the effectiveness of the proposed learnheuristic approach. Full article

Two mineral-based solid residues, namely coal gangue (CG) and phosphorus tailings (PT), two of the largest solid waste streams in the mining industry, were used as the sole metal feedstocks to fabricate a novel MgCaFeAl layered double hydroxide (LDH-GT) via a 700 °C calcination, acid leaching and hydrothermal coprecipitation route, with simultaneous synthesis of white carbon black from the reaction byproducts. Under optimized conditions (total metal load is 150 mg kg⁻¹, LDH-GT dose is 0.09 g, pH from 6 to 7), the synthesized material achieved concurrent immobilization efficiencies of 76.28%, 99.96%, and 99.95% for Cr (VI), Cd (II) and Ni (II), respectively, within a 24 h reaction period. TCLP leachability decreased by 82 to 91% relative to the untreated soil. After three wetting, drying and freeze–thaw cycles, the leached concentrations of all three metals remained below 0.3 mg L⁻¹, confirming excellent long-term stability. Mechanistic analyses revealed that Cr (VI) was mainly sequestered through interlayer anion exchange and surface complexation, whereas Cd (II) and Ni (II) were immobilized via isomorphic substitution into the LDH lattice, precipitation as carbonates, and incorporation into Fe/Mn oxides. A 7-day mung bean bioassay showed that LDH-GT amendment increased seed germination from 50% to 73%, enhanced root and shoot biomass by 1.1- to 1.6-fold, and decreased plant Cr, Cd, and Ni contents by over 80%. The 16S rRNA sequencing further demonstrated that LDH-GT reversed the decline in microbial α diversity induced by heavy metal stress, restored aerobic chemoheterotrophic and sulfur cycling functional guilds, and reduced pathogenic signatures. This study provides the demonstration of a waste-to-resource LDH that achieves efficient, durable remediation of multi-metal-contaminated soils, offering a scalable route for coupling solid waste valorization with in situ site restoration. Full article

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R² = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R² = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R² = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices. Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

With the rapidly growing global demand for clean energy, offshore wind power has become an important renewable energy source. To clarify how the principal dimensions affect the performance of a 15 MW-class floating wind turbine platform in 100 m water depth, this paper proposes a steel-concrete hybrid semi-submersible platform and systematically performs a parametric sensitivity analysis. The platform adopts a three-column configuration with heave tanks. The upper columns and cross braces are made of steel, while the lower hexagonal columns, pontoons, and heave tanks are constructed from concrete, significantly reducing steel consumption while satisfying structural and stability requirements. Focusing on three key design variables—draft, column spacing, and column diameter—this study establishes a unified normalized sensitivity analysis framework. It quantitatively evaluates their influence on platform mass, intact stability, natural periods, and fully coupled dynamic responses (including surge, heave, pitch motions, and mooring line tensions) under both operational and extreme conditions. The results reveal distinct roles of the principal dimensions in governing the platform dynamics: column spacing is the most sensitive parameter for tuning pitch response, restoring stiffness, and stability; increasing draft effectively suppresses heave and pitch responses but has only a limited effect on low-frequency surge motions; and column diameter strongly affects the natural periods of heave and pitch. Notably, dynamic responses exhibit significant nonlinear characteristics with variations in column diameter. When the diameter exceeds 110–120% of the baseline value, the peak pitch response under extreme sea states shows a deteriorating inflection point, accompanied by an accelerated surge in peak mooring loads. This indicates that excessive increases in column diameter may cause wave excitation forces to become dominant, thereby compromising the overall dynamic safety of the system. This paper identifies the governing geometric parameters for different motion modes and their control boundaries, providing a quantifiable and generalizable basis for the multi-objective collaborative design and cost reduction optimization of 15 MW steel-concrete hybrid semi-submersible floating wind turbine platforms. Full article

Aiming at the inherent defects of the Horned Lizard Optimization Algorithm (HLOA), such as insufficient global exploration capability, premature convergence to local optima, and inadequate balance between exploration and exploitation, this paper proposes an enhanced Improved Horned Lizard Optimization Algorithm (IHLOA) integrated with multi-strategy improvements. Firstly, the Fuch chaotic mapping is introduced for population initialization, which enhances the ergodicity and diversity of the initial population by leveraging the pseudo-random and aperiodic characteristics of chaotic sequences, laying a high-quality foundation for subsequent optimization searches. Secondly, the golden sine strategy is embedded into the iterative update process to dynamically adjust the search step size and direction. This strategy utilizes the periodic amplitude variation in the sine function and the golden section coefficient to balance the global exploration for potential optimal regions and local exploitation for refined optimization, thereby accelerating convergence speed while avoiding local stagnation. Finally, the orthogonal crossover strategy is incorporated in the late iteration stage to promote effective information interaction between parent and offspring populations. By means of chromosome segment exchange and elitist retention mechanisms, this strategy reduces dimensional search blind spots and further enhances the algorithm’s ability to capture high-quality solutions. Comprehensive experimental evaluations are conducted based on classical benchmark test functions and eight state-of-the-art meta-heuristic algorithms. The results demonstrate that the IHLOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability across 30-D, 50-D, and 80-D scenarios. For practical path planning applications, the IHLOA achieves remarkable performance improvements: in single-goal path planning, it reduces the path length by 2.54–87.64% compared with benchmark algorithms; in multi-goal path planning, it realizes a 1.24–7.99% reduction in path length and an 11.91% average reduction in the number of turning points relative to the original HLOA. Additionally, the IHLOA exhibits excellent robustness and adaptability in dynamic obstacle environments, effectively shortening the path length and reducing robot stuck times. This research not only enriches the improvement framework of meta-heuristic algorithms but also provides a high-efficiency optimization solution for mobile robot path planning in complex environments. Full article

Accurate and timely information on tobacco cultivation is essential for macro-level regulation, tobacco monopoly management, and farmer support in China. Remote sensing offers an efficient means for mapping tobacco, yet most studies focus on mid- to late-season identification, while early-season detection remains underexplored. Therefore, we integrated multi-source remote sensing data with the Google Earth Engine (GEE) platform to quantify separability between tobacco and competing crops, determined the optimal early-season phenological window and discriminative features, and developed an early-season tobacco mapping approach using a stacked generalization ensemble. The results show that January–February period provides the most effective early identification window for Changting County, with near-infrared (NIR) and water-sensitive features contributing most to class discrimination. The stacked ensemble achieved an overall accuracy of 87.17%, with producer and user accuracies of 85.97% and 92.28%, respectively, outperforming individual classifiers. At the township scale, the mapped tobacco area was strongly associated with tobacco purchase volumes (R² = 0.819), supporting the reliability of the derived maps. Spatially, tobacco fields exhibited a pronounced corridor-like pattern, primarily influenced by proximity to water systems and terrain conditions. The proposed workflow provides tobacco management agencies with timely, spatially explicit information to support precise and intelligent tobacco production management. Full article

The electrochemical reduction of carbon dioxide (CO₂RR) into value-added chemicals using renewable electricity is a pivotal strategy for achieving a sustainable carbon cycle. However, this process is plagued by intrinsic challenges, including poor product selectivity, competing hydrogen evolution, and catalyst instability. Metal–organic frameworks (MOFs), with their highly designable periodic structures, atomically dispersed active sites, and tunable pore microenvironments, have emerged as a uniquely versatile platform to address these issues. This review articulates a multi-scale design philosophy that enables precise steering of the CO₂RR pathway. We systematically elaborate on hierarchical tuning strategies, beginning with molecular-scale engineering of active sites (metal nodes and organic ligands) to define intrinsic activity and intermediate binding. This is synergistically integrated with the optimization of electronic structure and charge transport to overcome conductivity bottlenecks, meso-scale modulation of crystal morphology and defects to enhance mass transport and site accessibility, and the construction of heterogeneous interfaces for tandem catalysis and synergistic effects. Through this coherent, cross-scale design framework, MOF-based catalysts demonstrate exceptional capability in the precise control of reaction pathways, leading to remarkably selective synthesis of target high-value products, from C₁ compounds (CO, HCOOH, CH₄, CH₃OH) to C₂⁺ species (C₂H₄, C₂H₅OH) and urea. Finally, we outline future directions centered on dynamic mechanistic understanding, electrode engineering for industrial current densities, and stability enhancement, thereby providing a comprehensive material design guideline to advance CO₂RR technology. This work positions MOFs as a quintessential tunable catalytic platform for the sustainable conversion of CO₂. Full article

Under the dual strategic imperatives of carbon neutrality and digital transformation, the power industry plays a pivotal role in advancing green and low-carbon development. Green Total Factor Productivity (GTFP) provides a comprehensive measure of efficiency in the power sector under energy and environmental constraints. Using panel data from 31 Chinese provinces over the period 2012–2023, this study employs a super-efficiency Slacks-Based Measure (SBM) model, kernel density estimation, standard deviation ellipse analysis, the Gini coefficient, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the spatiotemporal evolution, regional disparities, and digital-driven improvement pathways of power industry GTFP. The results indicate that national power-sector GTFP exhibits a fluctuating upward trend, accompanied by pronounced regional heterogeneity. A distinct spatial pattern has emerged, characterized by rapid improvement in the western region, relative stability in the eastern region, contraction in the central region, and persistent lagging in the northeastern region. Spatially, the distribution has evolved from an initial east–west dual-core structure to a three-tier gradient pattern led by the west, stabilized in the east, and depressed in the central region. Kernel density estimation reveals a clear multi-peak polarization trend, while standard deviation ellipse analysis shows a relatively stable spatial center with continuously expanding dispersion along the northeast–southwest axis. Further analysis demonstrates that interregional differences remain the primary source of overall inequality, with rapidly widening intraregional disparities in the western region. Configurational analysis identifies five digital-economy-driven pathways to high GTFP, highlighting that no single optimal configuration exists. Instead, multiple combinations of technological, organizational, and environmental conditions jointly facilitate GTFP enhancement. These findings provide empirical evidence to support differentiated and precision-oriented policy design for promoting coordinated digital transformation and green development in China’s power industry. Full article

The study of turbulent Prandtl number is important for turbulent flows with heat transfer. Despite its importance, no universal model exists that is able to capture its behavior for a range of molecular Prandtl numbers. In this study, new turbulent Prandtl number models were developed using multi-gene gene expression programming (MGGEP) based on direct numerical simulation (DNS) data for heated periodic channel flows. DNS datasets covering both medium- and low-Prandtl-number fluids were employed to construct more universal closures suitable for Reynolds-averaged Navier–Stokes (RANS) simulations. Two case studies were conducted. In the first case study, turbulent Prandtl number models optimized for air (Pr = 0.71) were obtained using the friction Reynolds number and normalized wall distance as the physical inputs. In the second case study, generalized models applicable to both medium- and low-Prandtl-number fluids (down to Pr = 0.025) were developed. A novel Galilean-invariant local Reynolds number parameter was introduced to accurately capture the near-wall behavior and spatial variations in turbulent heat transfer. The resulting models demonstrated mean percent relative errors below 3% for the turbulent Prandtl number compared with the DNS data, while existing models in the literature show errors of up to 26.7%. In terms of root mean square error for the periodic channel flow, medium Prandtl number studies showed root mean square error reduction from 0.0596 to 0.0302, and low-Prandtl-number studies exhibited root mean square error reduction from 0.3128 to 0.0256 when MGGEP models and models from the literature are compared with respect to turbulent Prandtl number. The models were also validated using the turbulent periodic pipe flow problem, where the mean percent relative error for the turbulent Prandtl number decreased from 31.0% to 5.8%. The developed models were subsequently implemented in RANS simulations, showing that the proposed turbulent Prandtl number models lead to highly accurate temperature predictions for the periodic channel flow problem. Full article

The existing fruit tree yield prediction methods mainly rely on fruit period images or long-term meteorological and soil data, which make it difficult to meet the needs of early yield prediction. In addition, the flowering period images contain complex spatial distribution and severe overlap between flowers, which makes it challenging to directly extract stable structural indicators related to yield. Most existing research has focused on simple statistical indicators such as the number of flowers, while the spatial clustering structure of flowers and their relationship with yield have not been fully explored. Therefore, this article proposes an early apple yield prediction based on flowering stage image thinning simulation characteristics. In this study, blossom images and fruit maturity yield data from 100 apple trees were collected, with flower mask images extracted through standardized image processing. First, the traditional DBSCAN clustering algorithm was enhanced by integrating a KDTree acceleration structure and an adaptive multi-scale mechanism, forming the adaptive multi-scale clustering algorithm (AMS-DBSCAN) to achieve efficient identification of flower clusters and individual flowers. Based on this, two flower thinning simulation strategies based on density and spatial uniformity were designed to model artificial thinning rules and construct multi-dimensional, interpretable phenotypic features. Then, the original statistical features were fused with strategy-generated features and optimized using Lasso. We compared multiple models including XGBoost, BPNN, and SVR for yield prediction. The experimental results showed that XGBoost achieved good predictive performance under the hybrid feature set (R² = 0.856, RMSE = 3.098), which was further improved to R² = 0.900 after feature optimization with Lasso. The results demonstrate that the proposed method enables reliable early yield estimation, providing a new reference for precision management and early decision-making in fruit tree cultivation. Full article

The transition to sustainable energy systems requires the effective integration of offshore wind energy with hydrogen production. In this context, the paper assesses the potential for offshore hydrogen production in eight locations, three of which are located in the Black Sea, using data from the ERA5 database (period 2016–2025) at a height of 10 m and then extrapolated to a height of 150 m. The methodology includes estimating the annual energy production for four types of offshore turbines (Siemens Gamesa (Zamudio, Spain) SG 14-236 DD, Vestas (Aarhus, Denmark) V236-15.0, GE (Rotterdam, The Netherlands) Haliade-X 13, and MingYang (Guangdong, China) MySE12-242) and correlating it with six electrolyzer configurations (PEM and AWE) in gross and net scenarios, as well as analyzing the energy compatibility related to the number of electrolyzers. The novelty of the study lies in the integrated multi-site approach and in the direct quantification of the relationship between wind production and electrolysis requirements for different turbine–electrolyzer combinations. The results indicate a variation in gross annual energy production (AEP) in the range of 45.65 to 81.11 GWh/year, while the net scenario, accounting for operational losses, ranged from 37.75 to 67.05 GWh/year, and hydrogen production between 327 and 1075 t/year, highlighting that the optimal performance is determined by the compatibility between turbine and electrolyzer and the specific energy consumption rather than the nominal power. The ${\mathrm{N}}_{\mathrm{net}}$ analysis shows that, in most cases, the energy produced by a single turbine is insufficient for the full operation of large capacity electrolyzers, resulting in a sub-unit utilization rate and necessitating the use of multiple turbines to reach the nominal operating regime. The analysis is limited to a technical assessment based on historical climatological data, excluding economic aspects, grid constraints, and variations in equipment performance over time. The results underscore the importance of integrating the sizing of offshore wind–hydrogen systems with local resources and energy conversion efficiency. Full article

Addressing the challenge of coordinated dispatch between wind/solar and thermal power in new energy grids, this research proposes a thermal power unit output prediction method based on a Transformer-BiLSTM hybrid deep learning model. First, a simulated annealing algorithm optimizes the output configuration of solar thermal power plants to mitigate fluctuations in wind and solar combined generation. An ant colony-greedy algorithm is then integrated to determine the optimal dispatch data for thermal power units, constructing a high-quality training dataset under physical constraints. In the model design, a bidirectional long short-term memory network captures short-term temporal features, while the Transformer’s multi-head self-attention mechanism models long-term dependencies. The model innovatively incorporates the learnable positional encoding to enhance temporal awareness. Experimental results demonstrate accurate predictions, with the power constraint mechanism effectively correcting over-limit forecasts. This ensures 98.7% of predictions during low-load periods comply with unit technical specifications. Compared to existing methods, this model avoids data limitations and manual feature engineering bottlenecks through the end-to-end wind–solar–thermal mapping, providing a high-precision solution for dispatch decisions in renewable-dominated grids. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

As modern power markets integrate more renewable generation, day-ahead energy clearing remains the central procurement step, while flexibility products are procured to ensure that the cleared energy schedule can be operated securely. This paper proposes a closed-loop framework linking the Power Exchange (PX) and the Independent System Operator (ISO) to bridge energy-market settlement and network-feasible operation. The PX performs staged day-ahead clearing with energy settled first, followed by aAutomatic generation control (AGC) and spinning reserve (SR) procured from the residual headroom of committed (energy-awarded) units. The ISO then validates the cleared schedule using an equivalent current injection (ECI)-based screening. This paper uses a single-period (single-hour) IEEE 30-bus case setting; multi-period scheduling and intertemporal constraints are not modeled. When congestion is detected, power-flow tracing identifies the main contributors and guides a minimal-change redispatch. The ISO-feasible dispatch is then sent back to the PX for re-clearing, aligning prices and welfare with an executable operating point. The resulting nonconvex clearing problems with valve-point effects and prohibited operating zones are solved by Artificial Protozoa Optimizer with Social Learning (APO–SL) and evaluated against representative metaheuristic baselines. IEEE 30-bus studies show that off-peak and average-load cases pass ISO screening directly, whereas the peak case tightens reserve headroom (SR capped at 39.08 MW) and triggers congestion. After ISO feedback and energy re-clearing, line loadings return within limits. The ISO-feasible dispatch changes the marginal accepted offer and lifts the MCP (3.73 → 4.38 $/MWh). The welfare value reported here follows the paper’s settlement-based definition (purchase total minus accepted offer cost), and it increases accordingly (113.77 → 190.17 $/h). Full article