The evaluation of investment effectiveness in power grids oriented towards new-type power systems is a critical issue for advancing grid transformation and enhancing the scientific basis of investment decision-making. To address the current challenges—such as single-dimensional evaluation, strong subjectivity in index weighting, and insufficient consideration of risks and decision-makers’ psychological factors—this paper aims to construct a hybrid evaluation framework that comprehensively reflects both objective data and subjective decision-making preferences. First, a comprehensive evaluation index system is established, encompassing four dimensions: low-carbon performance, safety, economic efficiency, and intelligence. Second, an innovative integration of the Back Propagation Neural Network (BPNN), the CRITIC method, and the Entropy Weight Method (EWM) is conducted. The combination weights are determined through game theory to scientifically quantify the importance of each index. Based on this, the Improved Cumulative Prospect Theory (ICPT) is introduced to characterize decision-makers’ psychological behavior under uncertainty. Furthermore, by combining Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), an ICPT-GRA-TOPSIS comprehensive evaluation model is constructed. An empirical study of 13 typical urban power grids in China reveals that the proposed model can effectively identify the strengths and weaknesses of investment effectiveness across different regions, categorizing them into development tiers such as “multi-objective collaborative leading type”, “key breakthrough but unbalanced type”, and “system-lagging type”. More importantly, the sensitivity analysis of decision-making psychology demonstrates that the evaluation of investment strategies is highly dependent on decision-makers’ risk attitudes and value orientations. This provides critical quantitative decision-making references for formulating differentiated, precise investment strategies for power grids, offering significant theoretical and practical value for guiding power grid enterprises in optimizing resource allocation and supporting the construction of new-type power systems.
This study investigates the development of hybrid-reinforced polyester composites using corncob and urea particles as reinforcement for sustainable applications. Composites were fabricated by the stir casting method with varying weight fractions of corncob and urea. The mechanical and physical properties of the developed composites were evaluated, while fracture surface morphology was examined using scanning electron microscopy (SEM). The burning rates of the samples were investigated to evaluate their flame-retardant potential. The results demonstrate that incorporating corncob and urea effectively enhances stiffness-related mechanical properties, including tensile and flexural moduli and hardness. Composite containing 12 wt% urea and 3 wt% corncob exhibited the highest flexural moduli and hardness with an improvement of 122% and 45%, respectively. Composite with 3 wt% corncob and 18 wt% urea has the highest flexural strength with an increase of 44%, composite with 9 wt% corncob and 18 wt% urea has the highest tensile modulus with an improvement of 22%. In addition, it was found that the presence of corncob and urea reduced burning rates, with the sample containing 15 wt% corncob and 18 wt% urea exhibiting the lowest burning rate, indicating better flame-retardant potential. Thus, the findings indicate that corncob–urea hybrid reinforcement offers a promising, sustainable approach to enhancing the mechanical stiffness and reducing the burning rate of polyester composites. These materials have potential for use in applications requiring improved durability and low burning rate potentials while reducing reliance on conventional synthetic additives.
The Archimedes Screw hydrokinetic turbine (AST) is a promising technology for renewable energy generation in shallow, low-velocity, and bidirectional flows, but the mechanisms governing its torque production remain poorly understood. This study uses computational fluid dynamics (CFD) to investigate the performance and torque-generation mechanism of a three-flight AST inclined at 30° and operating in two configurations previously examined experimentally. Transient simulations were performed in ANSYS Fluent using a sliding mesh and flow-induced rotation approach within an unsteady Reynolds-averaged Navier–Stokes framework with the SST k–ω turbulence model. The results show that pressure forces dominate torque generation, while viscous contributions are comparatively small. Importantly, this behaviour is observed at a relatively low Reynolds number of approximately 4.5 × 104, indicating that Reynolds-number dependence becomes weak at Reynolds numbers substantially lower than those expected in practical deployments. For the first configuration, with the upstream edge of the turbine at the free surface, the CFD model predicted a maximum power coefficient of 0.85 at a tip speed ratio of 1.50, compared with an experimental value of 0.40 at 0.53. For the second configuration, with the downstream edge of the turbine at the free surface, the corresponding maximum power coefficient was 0.82 at a tip speed ratio of 1.51, compared with 0.34 at 0.54, as experimentally observed. The simulations also captured strong cyclic torque variations; the maximum variation in torque was over three times the mean value for both configurations. Comparison of the cavitation and pressure coefficients indicates little likelihood of cavitation at the experimental flow velocity but suggests possible cavitation onset at higher velocities.
Volunteer citizen scientists collected benthic macroinvertebrate samples from 35 streams throughout multiple watersheds in southeastern Minnesota, USA, during the period 1999–2013 to assess community diversity, taxa richness, and biotic integrity as indicators of water quality and general habitat conditions. In total, 452 invertebrate samples containing >46,000 organisms were collected, processed, and analyzed. Only 45% of the citizen scientists completed their 5-year sampling commitment. However, their samples generally demonstrated significant differences in total taxa richness, Ephemeroptera-Plecoptera-Trichoptera (EPT) taxa richness, Simpson and Shannon diversities, and a regional benthic index of biotic integrity (BIBI) within and/or among the watersheds examined. Streams in the two larger watersheds averaged significantly higher taxa richness and BIBI scores than those in smaller watersheds. Overall, streams in this region exhibited mostly poor or very poor biotic integrity based on their macroinvertebrate communities, indicating continued impacts from environmental stressors within these agricultural watersheds.
Artificial intelligence (AI) has rapidly become a core enabling technology in photovoltaic (PV) power systems, supporting improvements in forecasting accuracy, operational control, fault diagnosis, and system-level energy management. Despite the rapid growth of this field, a comprehensive understanding of its intellectual structure, thematic evolution, and emerging methodological directions remains fragmented. To address this gap, this study develops an integrated bibliometric-thematic analysis framework to systematically map the knowledge structure, research trajectories, and methodological frontiers of AI applications in PV power systems. The analysis is based on 4752 peer-reviewed journal articles indexed in Scopus (2006–2025). It combines performance analysis, co-citation analysis, keyword co-occurrence analysis, and bibliographic coupling to answer five structured research questions. The results demonstrate that PV power forecasting constitutes the central intellectual backbone of AI-based PV research, with the highest citation concentration and the strongest thematic connectivity across clusters. Thematic evolution analysis reveals a clear methodological transition from conventional machine learning models toward hybrid deep learning architectures, uncertainty-aware prediction frameworks, and physics-based AI integration. Furthermore, emerging research frontiers are characterized by generative learning models, multi-source data fusion strategies, and resilience-oriented fault diagnostics, while critical gaps persist in benchmarking standardization, uncertainty quantification, system-level integration, and large-scale industrial deployment. Unlike prior reviews that focus on isolated technical applications, this study provides the first integrated performance analysis and science-mapping synthesis that connects intellectual foundations, thematic evolution, and frontier innovations across the entire AI-based PV ecosystem. The findings offer a structured research roadmap and actionable guidance for researchers, PV plant operators, and policymakers aiming to design intelligent, scalable, and resilient PV energy systems that support the global low-carbon transition.
