Search Results (1,294)

Portfolio optimization is a cornerstone of modern financial decision-making, traditionally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return maximization, mean absolute deviation (MAD) minimization, and entropy-based diversification into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average magnitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and entropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved diversification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allocation, and decentralized finance, especially under high-uncertainty market conditions. Full article

Pipeline leak detection through acoustic signal measurement faces critical challenges, including insufficient utilization of time-frequency domain features, poor adaptability to noisy environments, and inadequate exploitation of frequency-domain prior knowledge in existing deep learning approaches. This paper proposes a Multi-Scale Frequency-Aware Transformer (MSFAT) architecture that integrates measurement-based acoustic signal analysis with artificial intelligence techniques. The MSFAT framework consists of four core components: a frequency-aware embedding layer that achieves joint representation learning of time-frequency dual-domain features through parallel temporal convolution and frequency transformation, a multi-head frequency attention mechanism that dynamically adjusts attention weights based on spectral distribution using frequency features as modulation signals, an adaptive noise filtering module that integrates noise detection, signal enhancement, and adaptive fusion functions through end-to-end joint optimization, and a multi-scale feature aggregation mechanism that extracts discriminative global representations through complementary pooling strategies. The proposed method addresses the fundamental limitations of traditional measurement-based detection systems by incorporating domain-specific prior knowledge into neural network architecture design. Experimental validation demonstrates that MSFAT achieves 97.2% accuracy and an F1-score, representing improvements of 10.5% and 10.9%, respectively, compared to standard Transformer approaches. The model maintains robust detection performance across signal-to-noise ratio conditions ranging from 5 to 30 dB, demonstrating superior adaptability to complex industrial measurement environments. Ablation studies confirm the effectiveness of each innovative module, with frequency-aware mechanisms contributing most significantly to the enhanced measurement precision and reliability in pipeline leak detection applications. Full article

Multi-modal knowledge graph completion (MMKGC) aims to complete knowledge graphs by integrating structural information with multi-modal (e.g., visual, textual, and numerical) features and leveraging cross-modal reasoning within a unified semantic space to infer and supplement missing factual knowledge. Current MMKGC methods have advanced in terms of integrating multi-modal information but have overlooked the imbalance in modality importance for target entities. Treating all modalities equally dilutes critical semantics and amplifies irrelevant information, which in turn limits the semantic understanding and predictive performance of the model. To address these limitations, we proposed a modality information aggregation graph attention network with adversarial training for multi-modal knowledge graph completion (MIAGAT-AT). MIAGAT-AT focuses on hierarchically modeling complex cross-modal interactions. By combining the multi-head attention mechanism with modality-specific projection methods, it precisely captures global semantic dependencies and dynamically adjusts the weight of modality embeddings according to the importance of each modality, thereby optimizing cross-modal information fusion capabilities. Moreover, through the use of random noise and multi-layer residual blocks, the adversarial training generates high-quality multi-modal feature representations, thereby effectively enhancing information from imbalanced modalities. Experimental results demonstrate that our approach significantly outperforms 18 existing baselines and establishes a strong performance baseline across three distinct datasets. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Load forecasting is a key component of power network planning and an essential approach to achieving the efficient cooperative optimization of integrated economic energy services. To improve the accuracy of the power load prediction and ensure the stable dispatch of power grid, this paper takes County A as a case study. The fish bone diagram method is applied to analyze the influence of four categories of factors on the county’s power load, and stepwise regression, the unit energy consumption method, and an optimized grey model are adopted to forecast and analyze the planned load of the county over the past 5 years. In addition, the spatial load density method, the optimized grey prediction model, and the General Regression Neural Network (GRNN) are used to predict and analyze the county’s planned power grid load based on data from the past ten years. The Ordered Weighted Averaging (OWA) operator is then applied to integrate the results, and the predictive performance of different methods is assessed with an evaluation function. The results show that this combined multi-method approach achieves a higher accuracy. It also accounts for the evolving political, economic, and social conditions of the country, making the predictions more useful for power grid planning. Based on these findings, corresponding countermeasures and suggestions are proposed to support the improvement of spatial planning for electric power facilities in County A. Full article

Physics-Informed Neural Networks (PINNs) represent a transformative approach to solving partial differential equation (PDE)-based boundary value problems by embedding physical laws into the learning process, addressing challenges such as non-physical solutions and data scarcity, which are inherent in traditional neural networks. This review analyzes critical challenges in PINN development, focusing on loss function design, geometric information integration, and their application in engineering modeling. We explore advanced strategies for constructing loss functions—including adaptive weighting, energy-based, and variational formulations—that enhance optimization stability and ensure physical consistency across multiscale and multiphysics problems. We emphasize geometry-aware learning through analytical representations—signed distance functions (SDFs), phi-functions, and R-functions—with complementary strengths: SDFs enable precise local boundary enforcement, whereas phi/R capture global multi-body constraints in irregular domains; in practice, hybrid use is effective for engineering problems. We also examine adaptive collocation sampling, domain decomposition, and hard-constraint mechanisms for boundary conditions to improve convergence and accuracy and discuss integration with commercial CAE via hybrid schemes that couple PINNs with classical solvers (e.g., FEM) to boost efficiency and reliability. Finally, we consider emerging paradigms—Physics-Informed Kolmogorov–Arnold Networks (PIKANs) and operator-learning frameworks (DeepONet, Fourier Neural Operator)—and outline open directions in standardized benchmarks, computational scalability, and multiphysics/multi-fidelity modeling for digital twins and design optimization. Full article

Cooperative longitudinal–lateral trajectory optimization is essential for unmanned ground vehicle (UGV) platoons to improve safety, capacity, and efficiency. However, existing approaches often face unstable formation under low penetration rates and rely on fragmented control strategies. This study develops a cooperative longitudinal–lateral trajectory tracking framework tailored for UGV platooning, embedded in a hierarchical control architecture. Dual-stage multi-objective Model Predictive Control (MPC) is proposed, decomposing trajectory planning into pursuit and platooning phases. Each stage employs adaptive weighting to balance platoon efficiency and traffic performance across varying operating conditions. Furthermore, a traffic-aware organizational module is designed to enable the dynamic opening of UGV-dedicated lanes, ensuring that platoon formation remains compatible with overall traffic flow. Simulation results demonstrate that the adaptive weighting strategy reduces the platoon formation time by 41.6% with only a 1.29% reduction in the average traffic speed. In addition, the dynamic lane management mechanism yields longer and more stable UGV platoons under different penetration levels, particularly in high-flow environments. The proposed cooperative framework provides a scalable solution for advancing UGV platoon control and demonstrates the potential of unmanned systems in future intelligent transportation applications. Full article

To reproduce successfully, non-parasitic brook lamprey must accumulate all nutrients needed for growth and reproductive development during a multi-year larval stage while feeding on low-quality detritus. We used total length and wet mass data of American brook lamprey (Lethenteron appendix) in 14 streams across four watersheds in southeastern Minnesota, USA, to examine Fulton’s condition factors (Fulton K = [g wet mass/mm TL³] × 10⁶) of both lamprey ammocoetes (or larvae, n = 717) and spawning adults (n = 154) and developed preliminary standard mass equations for both life stages to allow for calculations of relative weights, a first attempt for any lamprey species. Condition factors and relative weights were most variable through the first year or two of the larval stage, with both condition factors and relative weights rising slightly through the remainder of the larval phase. Relative weights of most late-stage larvae ranged from 90 to 110% with condition factors at or slightly above 1.5. The standard mass equation for American brook lamprey larvae based on the top 25% heaviest individuals across the length range was: log₁₀ wet mass (g) = 2.7078 log₁₀ total length (mm)—5.115. Adult male American brook lampreys were slightly but not significantly longer than females at most of the sites examined, and condition factors and relative weights differed between the sexes only at one site. Overall, adult condition factors averaged 2.0, and relative weights averaged 86.9% and did not change significantly across the total length range (137 to 214 mm). The standard mass equation for American brook lamprey adults based on the top 25% healthiest or fittest individuals across the length range was: log₁₀ wet mass (g) = 2.7411 log₁₀ total length (mm)—5.047. American brook lamprey adults and ammocoetes approaching metamorphosis generally exhibited good condition factors and relative weights. Both adult and ammocoete condition factors and relative weights, along with adult lengths, differed significantly among streams. Length-wet mass data are needed from more populations of American brook lamprey across its range to build a more robust relative weight model for the species. Full article

Distribution-level long-term load forecasting with hourly resolution is essential for modern power systems operation, yet it remains challenging due to complex temporal patterns and error accumulation over extended horizons. This study proposes a Multi-Resolution Residual LSTNet framework integrating Discrete Wavelet Transform (DWT), Long Short-Term Memory Networks (LSTNet), and Normalized Linear (NLinear) models for accurate one-year ahead hourly load forecasting. The methodology decomposes load time series into daily, weekly, and monthly components using multi-resolution DWT, applies direct forecasting with LSTNet to capture short-term and long-term dependencies, performs residual correction using NLinear models, and integrates predictions through dynamic weighting mechanisms. Validation using five years of Korean distribution feeder data (2015–2019) demonstrates significant performance improvements over benchmark methods including Autoformer, LSTM, and NLinear, achieving Mean Absolute Error of 0.5771, Mean Absolute Percentage Error of 17.29%, and Huber Loss of 0.2567. The approach effectively mitigates error accumulation common in long-term forecasting while maintaining hourly resolution, providing practical value for demand response, distributed resource control, and infrastructure planning without requiring external variables. Full article

Aiming at the challenges of design complexity and parameter adjustment difficulties in existing distributed controllers, a novel power flow sensitivity-based distributed cooperative control approach is proposed for voltage regulation and power sharing in droop-controlled DC distribution networks (DCDNs). Firstly, based on the power flow model of droop-controlled DCDNs, a comprehensive sensitivity model is established that correlates bus voltages, voltage source converter (VSC) loading rates, and VSC reference power adjustments. Leveraging the sensitivity model, a discrete-time linear state-space model is developed for DCDNs, using all VSC reference power as control variables, along with the weighted sum of the voltage deviation at the VSC connection point and the loading rate deviation of adjacent VSCs as state variables. A distributed consensus controller is then designed to alleviate the communication burden. The feedback gain design problem is formulated as an unconstrained multi-objective optimization model, which simultaneously enhances dynamic response speed, suppresses overshoot and oscillation, and ensures stability. The model can be efficiently solved by global optimization algorithms such as the genetic algorithm, and the feedback gains can be designed in a systematic and principled manner. The simulation results on a typical four-terminal DCDN under large power disturbances demonstrate that the proposed distributed control method achieves rapid voltage recovery and converter load sharing under a sparse communication network. The design complexity and parameter adjustment difficulties are greatly reduced without losing the control performance. Full article

Taipei’s metropolitan region faces frequent typhoon impacts that test its urban resilience. This study examines the relationship between Transit-Oriented Development (TOD) urban spatial forms and Taipei’s resilience against typhoons, considering both physical urban morphology and planning factors. We apply a Dynamic Analytic Network Process (DANP), an integrated DEMATEL-ANP multi-criteria approach to evaluate and prioritize key resilience-related spatial and planning factors in TOD areas. Rather than using GIS flood modeling, we emphasize empirical indicators derived from local data, including urban density, transit accessibility, historical typhoon flood impacts, infrastructure vulnerability, and demographic exposure. An extensive literature review covers TOD principles, urban resilience theory, and DANP methodology, with a particular emphasis on the Taiwanese context and case studies. Empirical results reveal that specific TOD characteristics indeed enhance typhoon resilience. High-density, mixed-use development around transit can reduce overall exposure to hazards by curbing sprawl into floodplains and enabling efficient evacuations. Using DANP, we find that infrastructure robustness and emergency planning capacity emerge as the most influential factors for resilience in Taipei’s TOD neighborhoods, followed by land use and management and transit accessibility. Weighted rankings of Taipei’s districts suggest that centrally located TOD-intensive districts score higher in resilience metrics, while peripheral districts with flood-prone areas tend to lag. The Discussion explores these findings, considering planning policies—noting that TOD can bolster resilience if coupled with adaptive infrastructure and inclusive planning—and compares them with examples like Singapore’s integrated land use and transit strategy, which dramatically reduced flood risk. The study concludes with policy implications for integrating TOD and climate resilience in urban planning, and contributions of the DANP approach for complex urban resilience evaluations. Full article

In situ leaching represents an efficient and safe method for uranium mining, where a suboptimal well flow rate distribution leads to solution imbalances between wells, forming stagnant zones that increase operational costs. This study examines a real technological block from the Budenovskoye deposit, applying reactive transport modeling to optimize well flow rates and reduce operational time and reagent consumption. A reactive transport model was developed based on mass conservation and Darcy’s laws coupled with chemical kinetics describing sulfuric acid interactions with uranium minerals ($U{O}_2$ and $U{O}_3$). The model simulated a technological block with 4 production and 18 injection wells arranged in hexagonal cells over 511–542 days to achieve 90% uranium recovery. Six approaches for well flow rate redistribution were compared, based on different weighting factor calculation methods: advanced traditional, linear distance, squared distance, quadrilateral area, and two streamline-based approaches utilizing the minimum and average time of flight. The squared distance method achieved the highest efficiency, reducing operational costs by 5.7% through improved flow redistribution. The streamline-based methods performed comparably and offer potential advantages for heterogeneous conditions by automatically identifying hydraulic connections. The reactive transport modeling approach successfully demonstrated that multi-criteria optimization methods can improve ISL efficiency by 3.9%–5.7% while reducing operational costs. Full article

With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing heuristic algorithms suffer from slow convergence speeds and susceptibility to local optima. To address these challenges, this paper constructs a multi-objective base station site selection model that simultaneously minimizes costs, maximizes coverage contributions, and minimizes interference. It achieves quantitative balance among objectives through normalization and weight fusion, while introducing constraints to ensure engineering feasibility. Concurrently, the genetic algorithm underwent targeted optimization by introducing an adaptive migration strategy based on population diversity and a cosine-type parameter adjustment strategy. This approach was integrated with the particle swarm optimization algorithm to balance exploration and exploitation while mitigating premature convergence. Experimental validation demonstrates that the improved algorithm achieves faster convergence and greater stability compared to traditional genetic algorithms and particle swarm optimization, while satisfying engineering constraints such as base station quantity, coverage, and interference. This research provides an efficient and feasible solution for intelligent base station site planning. Full article

Vertical greening (VG) delivers measurable urban ecosystem benefits, yet maintenance is constrained by at-height safety risks, heterogeneous facade geometries, and low labor efficiency. Although unmanned aerial vehicles show promise, most studies optimize isolated modules rather than providing a user-oriented, system-level pathway. This paper proposes a closed-loop, multi-method framework integrating the Decision-Making Trial and Evaluation Laboratory-Analytic Network Process, the Functional Analysis System Technique, and the Theory of Inventive Problem Solving. DEMATEL-ANP models causal interdependencies among requirements and derives prioritized weights,; FAST decomposes functions and localizes conflicts, and TRIZ converts those conflicts into principle-guided structural concepts—establishing a traceable requirements → functions → conflicts → structure pipeline. We illustrate the approach at the prototype level with Rhino–KeyShot visualizations under near-facade constraints, showing how prioritized requirements propagate into candidate UAV architectures. The framework structures the identification and resolution of tightly coupled technical conflicts, supports adaptability in facade-proximal scenarios, and provides a transparent mapping from user needs to structure-level concepts. Claims are restricted to methodological feasibility; comprehensive quantitative field validation remains for future work. The framework offers a reproducible methodological reference for the systematic design and decision-making of intelligent UAV maintenance systems for VG. Full article

Demand electrification is creating new operating conditions in distribution networks—such as congestion, overloads, and voltage issues—that have traditionally been addressed through network expansion planning (NEP). As an alternative, this work proposes the use of non-wire alternatives (NWAs) based on hybrid photovoltaic–storage (ESS) plants and analyzes their siting and sizing under uncertainty conditions. To this end, a MINLP model with a DistFlow representation is formulated to determine generation and storage locations and capacities, minimizing investment while satisfying current and voltage limits. Different uncertainty management methodologies are compared: robust optimization, equivalent probabilistic profile, weighted multi-scenario, and multi-scenario with penalty. The results on the CIGRE MV network show that the robust approach guarantees feasibility in the worst case, albeit with a high investment cost. In contrast, methods based on averages or simple weightings fail to adequately capture adverse conditions, while the multi-scenario optimization with expected penalty emerges as the most effective option, balancing investment and overload reduction. In conclusion, the explicit consideration of uncertainty in NWA planning is essential to obtaining realistic and adaptable solutions, with the expected penalty formulation standing out as the most efficient alternative for network operators. Full article