Search Results (4,006)

Transportation hub cities serve as pivotal nodes within integrated transport systems. This study reveals the corridor-oriented characteristics of comprehensive transportation system, confirms the progress of its transportation hub city development, and identifies future improvement directions based on diagnostic evaluation, taking Gansu Province, China as the research subject. To address hierarchical differentiation and structural constraints in the development of integrated transportation hubs, this study develops an evaluation framework integrating the entropy-weighted TOPSIS method, a coupling coordination model, and indicator-based diagnostic analysis. This framework was applied to 14 prefecture-level cities and autonomous prefectures in Gansu, classifying them into four hub tiers according to the comprehensive evaluation index. The results reveal a pronounced hierarchical and corridor-oriented spatial structure: Lanzhou is identified as the only Tier 1 core hub, five cities are classified as Tier 2 backbone hubs, seven cities and prefectures as Tier 3 general hubs, and Pingliang as Tier 4 terminal hub. Lanzhou exhibits the highest development level, with a comprehensive evaluation index of 0.9640, which is substantially higher than the provincial mean of 0.3867, but its radiation-driving capacity still needs to be strengthened. In terms of subsystem coordination, Lanzhou reaches the primary coordination stage with a coupling coordination degree of 0.532, while Jiuquan, Jiayuguan, and Tianshui are classified into the near-coordination stage with D values of 0.353, 0.351, and 0.321, respectively; the remaining ten units are classified as uncoordinated relatively. Based on the combined perspectives of development level and subsystem coordination, the study identifies future development directions for hub operational organization, multimodal transport integration, feeder connectivity, and industry-logistics coupling. The findings reveal the corridor-oriented characteristics and development progress of Gansu’s transportation hub system, highlight the analytical value of distinguishing hub development level from subsystem coordination, and provide empirical evidence for understanding hierarchical and functional differentiation in corridor-oriented inland regions. Full article

Power-law kinetics are observed across a wide range of physical, chemical, biological, and engineering systems, yet the thermodynamic origin of the power-law exponent remains incompletely understood. This work proposes a thermodynamic hypothesis in which power-law behavior emerges naturally from correlation-dependent contributions to the Gibbs free energy. Rather than modifying the classical Boltzmann definition of entropy, a phenomenological Correlation Constant, χ, is introduced to quantify how accumulated microstate evolution influences the accessibility of future states. The resulting correlation entropy contribution produces a free-energy term that modifies the probability of subsequent transitions and leads naturally to power-law kinetic behavior. Positive values of χ correspond to cooperative evolution in which prior evolution promotes future evolution, while negative values correspond to self-limiting behavior in which prior evolution suppresses subsequent evolution. The conventional Arrhenius-Eyring description is recovered as the special case χ = 0. The resulting framework provides a thermodynamic interpretation of the power-law exponent, establishes a connection between entropy, free energy, and kinetic evolution, and offers a unified description applicable to degradation, relaxation, diffusion, fatigue, trapping, and other evolving processes. The present work is intended as a thermodynamic hypothesis motivating further experimental and theoretical investigation of correlation-dependent kinetics. Full article

The large-scale integration of wind and solar power introduces significant volatility into electricity markets, posing challenges for accurate day-ahead price forecasting for generation companies. This paper proposes a hybrid forecasting model, CEEMD-SE-IBA-LSTM, based on hyperparameter adaptation to improve prediction accuracy. First, a similar-day selection method integrating Random Forest and an Improved Grey Ideal Value approximation identifies the most relevant historical days. Second, Complete Ensemble Empirical Mode Decomposition with Sample Entropy (CEEMD-SE) decomposes and reconstructs the price series into stable components. Third, an Improved Bat Algorithm (IBA), incorporating differential evolution and adaptive weighting, is developed to optimize two key LSTM hyperparameters: the number of hidden layer neurons, which is treated as a model architecture hyperparameter, and the learning rate, which is treated as a training hyperparameter. The number of LSTM layers and the number of training epochs are kept fixed as model settings to ensure reproducibility. Using data from the US PJM market, the proposed model is validated against six benchmarks. The results show that CEEMD-SE-IBA-LSTM achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 3.73%, a Root Mean Square Error (RMSE) of 3.57 $/MWh, and a Mean Absolute Error (MAE) of 1.95 $/MWh. The method provides accurate price trends, offering effective decision support for new energy enterprises in price bidding to enhance revenue. Full article

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article

The past few years has witnessed the rapid development of China’s logistics industry. However, the industry still faces problems such as uneven regional development, low-cost efficiency, insufficient technology application, and pressure for green transformation. To support more effective policy and strategic planning, this study used composite location entropy, spatial autocorrelation analysis, and kernel density estimation to analyze the spatiotemporal evolution of logistics industry agglomeration based on China’s city-level panel data from 2010 to 2023. Geographic detectors and geographically weighted regression were used to explore its driving mechanisms from multiple perspectives. The results indicated that (1) China’s logistics industry agglomeration exhibited a decreasing gradient from east to west and the regional disparities gradually narrowed down over time. (2) China’s logistics industry showed significantly positive spatial autocorrelation, characterized mainly by high-high and low-low clusters. Northeastern China experienced the most active and tortuous local spatial evolution of logistics agglomeration, while Eastern China exhibited high tortuosity but stable spatial structure. Western China showed a smooth evolution, and Central China followed a relatively independent evolutionary path. Spatially, China’s logistics industry presented a pattern of high concentration in the southeast and sparse distribution in the northwest, with high-value zones expanding toward the central and western regions. (3) Transportation accessibility was the primary factor influencing logistics industry agglomeration, and the interaction among factors was stronger than the effect of individual factors. Specifically, the degree of openness exhibited a driving pattern centered on coastal areas and decreasing towards inland regions; the level of commercial development showed a positive correlation in the west and a negative correlation in the east; the spatial pattern of transportation capacity shifted from a pronounced east–west polarization to a more fragmented multi-cluster distribution; and transportation accessibility demonstrated spatial heterogeneity, with positive correlation in the southeast coastal areas and negative correlation in the west. Full article

The utilization of biomass resources is of significant importance. However, the complexity of biomass thermochemical conversion processes and the performance limitations of conventional catalysts restrict the stable selection of reaction pathways and ultimately affect catalytic yields. With the rapid development of synthesis techniques and machine learning, nanoscale high-entropy alloys (HEAs) with targeted properties can now be accurately predicted and synthesized. The diverse compositions and structures of HEAs enable versatile catalytic selectivity, while their unique four core effects enhance catalytic activity and stability. This review primarily elaborates on the specific applications of HEAs in biomass thermochemical conversion. It covers the fundamental characteristics of HEAs, preparation methods, and machine learning-driven design strategies. Summarized the directional conversion and value-added research of high-entropy alloys in biomass thermal conversion intermediates. This demonstrates the excellent application adaptability of high-entropy alloys in complex reaction systems. Finally, prospects for the rational design of high-entropy alloy catalysts and their application in biomass refining technologies are outlined. Full article

Corporate balance sheets report aggregate equity and liability totals but conceal the internal allocation of financing sources across asset categories—an identification problem that conventional econometric methods cannot resolve without additional parametric assumptions. This paper develops a maximum entropy (ME) panel estimator to recover two latent scalar parameters: x ∈ (0,1), the share of equity capital directed toward long-term asset financing, and y ∈ (0,1), the corresponding debt allocation share. Grounded in maximum entropy principle, the estimator selects the unique parameter vector that satisfies the mean-level balance-sheet constraint while maximising joint Shannon entropy—the least-biassed solution consistent with observable data. The closed-form logistic representation yields a scalar Lagrange multiplier λ*, interpreted as a financing pressure index, recoverable via bisection in at most 21 iterations at tolerance ε = 10⁻⁵. Building on the ME estimates, we introduce a continuous matching alignment index M* = x* − y* that measures the degree of compliance with the financial matching principle along a continuous spectrum rather than as a binary categorisation. Applied to a ten-firm, cross-sectoral panel spanning Technology, Finance, Energy, and Automotive sectors over an observation window spanning 2001 to 2025 (with firm-specific subperiods reflecting differences in IPO dates and data availability), the framework reveals substantial heterogeneity in latent financing flows: equity allocation shares range from 30.1% (NVIDIA) to 75.1% (ExxonMobil), while debt allocation shares span 37.1% to 77.5%. Across the panel, only Meta exhibits substantial positive matching alignment, while Microsoft, ExxonMobil, Apple, and Tesla show only very slight differences that fall within the neutral band, and the remaining firms show varying degrees of structural departure from the matching benchmark; the thresholds used to summarise these descriptive labels are interpretive aids rather than re-imposed binary criteria, and the substantive ranking of firms along M* does not depend on the specific threshold values adopted. The ME solution’s entropy H(x*, y*) and the normalised diversification index D(x*, y*) describe allocation balance under the estimator’s information–theoretic criterion rather than independently observed firm complexity; in the present sample, the cross-firm ordering of these values is not recovered by firm size, leverage, or sector classification alone. These findings, based on a ten-firm case-study panel with time-invariant allocation parameters, should be interpreted as descriptive patterns of the present sample rather than statistically validated regularities. They provide a theoretically rigorous and computationally tractable identification of unobservable corporate financing flows, with potential implications for capital structure theory, financial risk assessment, and balance sheet analysis that would benefit from validation on larger and more representative samples in future work. Full article

Shannon entropy is the most common measure that one could use to check if a data source has random behaviour or not. A value close to the maximum is usually considered as evidence that the source is “random enough”. The present paper shows that this criterion alone is not enough. A deterministic logistic map driven at $r=3.9999$ reaches 94.97% of the Shannon maximum, yet it is fully predictable once we look at the built-in patterns: its permutation entropy drops to 77.01% of the maximum and its sample entropy falls to 0.67, against 2.33 for a high-quality pseudo-random generator (PRNG). Building on this observation, we combine four entropy measures—Shannon, Rényi, permutation, and sample—into a single diagnostic profile of the analyzed source. In order to validate our approach with practical, real life data, we test it on 2538 official draws of the Romanian Loto 6/49 lottery, recorded between August 1993 and April 2026. The lottery historical data set is very close to a high-quality PRNG (pseudo-random number generator) from the point of view of all four measures. We also observe that the entropy deficit of both the lottery and the PRNG decays as a power law with exponent $\alpha \approx -0.96$; in contrast, the logistic map sits at $\alpha \approx -0.07$. A Random Forest classifier trained only on the entropy profile reaches 78% accuracy on the analyzed four-way classification task (lottery, PRNG, logistic map, biased distribution), but scores 55.7% on the binary lottery-versus-PRNG task, consistent with chance. The method introduced in this study is domain-independent and applies directly to RNG certification, cryptographic key auditing, and any setting where structured pseudo-randomness has to be ruled out. Full article

Control subarea division is essential for coordinated signal control, but methods based mainly on local correlation or static topology may not adequately capture traffic-state propagation under dynamic traffic loading. This study proposes a control subarea division method that explicitly models traffic-state propagation by integrating state-guided colored random walk and path entropy analysis. Intersection correlation degree and traffic state are used to construct a state-guided colored random walk process, in which transition probabilities are updated according to network connectivity and traffic-state consistency. Path entropy characterizes propagation uncertainty, and control subareas are identified by minimizing the distribution discrepancy between node-level and subarea-level path responses. To compare partitioning schemes, five complementary metrics were adopted: variance reduction rate of spatial delay, delay reduction rate, congestion mitigation index, stop reduction rate, and queue reduction rate. A VISSIM microsimulation model with dynamic traffic loading was developed to compare the proposed method with the Whitson and Fast Newman methods. The proposed method achieved the best performance across all five metrics, with values of 41.47%, 23.77%, 25.96%, 23.59%, and 15.08%, respectively. These results indicate that the proposed method improves spatial balance and network efficiency while mitigating bottlenecks, reducing stops, and suppressing queue accumulation. Full article

Rural sustainability planning requires spatially explicit methods that integrate agricultural resource bases, ecological condition, low-carbon feasibility, community implementation support, and cultural landscape values. Although the Circular and Ecological Sphere (CES) concept offers an integrative framework for rural transition, empirical CES studies remain largely qualitative or policy-oriented. This study develops a FarmMap-integrated Python-GIS workflow for proxy-based CES-oriented spatial prioritization in Yangpyeong-gun, a peri-rural county on the eastern fringe of the Seoul metropolitan region in Korea. Public spatial and administrative datasets were integrated into thirteen indicators grouped under five CES-relevant axes. The model does not measure realized circular material flows, governance quality, resident participation, or carbon emission reduction directly; instead, it identifies where CES-relevant spatial potentials co-occur. An axis-balanced entropy model assigned equal total weight to each axis while applying entropy weighting within axes. Robustness was tested through equal-weight, axis-emphasis, raw entropy diagnostic, Monte Carlo perturbation, and spatial-scale sensitivity analyses using 100 m diagnostic, 500 m, and eup/myeon supports. The final 250 m priority surface identified the top fifth of analyzed Yangpyeong-gun area as very-high relative priority and remained stable across weighting and spatial-support diagnostics. Rural-experience villages and village enterprises had significantly higher CES scores than random background locations. The results demonstrate a reproducible first-stage spatial screening workflow for CES-oriented rural planning while clarifying the limits of proxy-based circularity, governance, and low-carbon indicators. Full article

Developing low-cost, Co-free high-entropy alloys (HEAs) that retain both high strength and useful ductility at cryogenic temperatures remains challenging because hard strengthening phases usually intensify strain localization and accelerate plastic instability. In this work, a Fe-enriched Al₁₁Cr₁₄Fe₅₀Ni₂₅ HEA was designed and processed by heavy cold rolling followed by short-time annealing at 900 °C for 10 min to construct a hierarchical heterogeneous microstructure. The alloy consists of an FCC-dominated matrix and an ordered B2 phase distributed in recrystallized and unrecrystallized domains over multiple length scales. Tensile testing shows that the alloy achieves a yield strength of 953 MPa, an ultimate tensile strength of 1160 MPa, and an elongation of 21.1% at 298 K, while these values increase to 1268 MPa, 1686 MPa, and 28.6%, respectively, at 77 K. Load–unload–reload analysis at 77 K reveals that the hetero-deformation-induced stress reaches about 804 MPa at a true strain of 25%, contributing more than 52% of the total flow stress. The superior cryogenic strength–ductility synergy is attributed to strain partitioning between soft FCC and hard B2 phases and between recrystallized and unrecrystallized regions, which promotes geometrically necessary dislocation accumulation, back-stress strengthening, and sustained work hardening. This study demonstrates that hierarchical heterostructure design provides an effective route for developing cost-conscious Co-free HEAs for cryogenic structural applications. Full article

There is increasing interest in structured layered double hydroxide (LDH)-based catalysts because they combine tunable acid–base/redox chemistry with reactor architectures that can reduce diffusion lengths, improve heat management, and lower pressure-drop penalties. This review evaluates LDH, LDH-derived oxide (LDO/MMO), reduced metal/LDO, reconstructed hydroxide-rich, and mixed dynamic states integrated into honeycomb monoliths, open-cell foams, meshes/felts, thin films, washcoats, coated plates, microchannels, capillaries, and additively manufactured lattices. To move beyond descriptive comparison, the literature is assessed using unified evaluation dimensions: operative active state, support architecture, coating/integration route, active-phase loading, coating thickness and uniformity, reactor-volume-normalized productivity or STY, ΔP/L, axial/radial thermal gradients, time-on-stream, coating loss, regeneration recovery, and pilot-readiness. Representative benchmarks illustrate both the promise and reporting gaps of the field: NiFe-LDH-derived monoliths for CO₂ methanation have reached ~70% CO₂ conversion at 300 °C with >90% CH₄ selectivity and only 0.7% post-test mass loss; NiFe-LDH/iron-foam monoliths retained 85% ozone conversion after 168 h; high-entropy LDH-derived oxides showed T50/T90 values of 246/254 °C for toluene oxidation; and Au/LDH capillary films achieved 31.9% glycerol carbonate yield and 3.78 g h⁻¹ g⁻¹ productivity. The strongest current cases are pollution abatement and CO₂ methanation, whereas biomass upgrading, fine-chemical flow, high-entropy coatings, and photo/electrocatalytic films require deeper module-level validation. Overall, structured LDH catalysts should be treated as coupled chemistry–coating–reactor systems whose performance must be judged simultaneously by activity, accessible catalyst inventory, transport efficiency, pressure drop, thermal profile, durability, regeneration, and manufacturability. Full article

To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R² followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R² values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article

Smart museums increasingly rely on digital media, interactive installations, artificial intelligence, augmented reality, and virtual reality to support cultural communication and visitor engagement. However, existing studies have mainly examined specific technologies, usability, or visitor satisfaction, while a systematic and quantitative framework for comparing interactive experience across different smart museums remains limited. To address this gap, this study proposes a hybrid multi-criteria decision-making framework for evaluating smart museum interactive experience. Based on the Strategic Experiential Modules, an evaluation system consisting of five dimensions—Sense, Feel, Think, Act, and Relate—and sixteen indicators was constructed. The Analytic Hierarchy Process was used to determine subjective weights from expert judgments, the entropy method was applied to capture the data-driven dispersion characteristics of expert evaluation data, and a game-theoretic combination weighting strategy was used to integrate the two weighting results. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare five representative smart museum cases. The results show that Zhejiang Provincial Museum achieved the highest relative closeness value (C_i = 0.9891), followed by Shanghai Museum (C_i = 0.8457) and Hunan Museum (C_i = 0.5326). Robustness analysis further showed that the ranking order remained consistent under entropy weights, AHP weights, average weights, and game-theoretic combined weights. The Friedman test indicated no significant difference in the relative closeness coefficients across weighting schemes (χ² = 1.200, p = 0.753). These findings indicate that the proposed framework can effectively identify relative strengths and weaknesses in smart museum interactive experience and provide a replicable decision-support tool for experience-oriented museum design and optimization. Full article