Search Results (206)

This study presents a comprehensive multi-objective optimization framework for optimal placement and sizing of distributed generation (DG) units in electric bus (E-bus) transit systems integrated with a high-power flash charging infrastructure. An enhanced Multi-Objective Grey Wolf Optimizer (MOGWO), utilizing Euclidean distance-based Pareto ranking, is developed to minimize power loss, voltage deviation, and voltage violations. The framework incorporates realistic E-bus operation characteristics, including a 31-stop, 62 km route, 600 kW pantograph flash chargers, and dynamic load profiles over a 90 min simulation period. Statistical evaluation on IEEE 33-bus and 69-bus distribution networks demonstrates that MOGWO consistently outperforms MOPSO and NSGA-II across all DG deployment scenarios. In the three-DG configuration, MOGWO achieved minimum power losses of 0.0279 MW and 0.0179 MW, and voltage deviations of 0.1313 and 0.1362 in the 33-bus and 69-bus systems, respectively, while eliminating voltage violations. The proposed method also demonstrated superior solution quality with low variance and faster convergence, requiring under 7 h of computation on average. A five-method compromise solution strategy, including TOPSIS and Lp-metric, enabled transparent and robust decision-making. The findings confirm the proposed framework’s effectiveness and scalability for enhancing distribution system performance under the demands of electric transit electrification and smart grid integration. Full article

Carbon Capture and Storage (CCS) is a critical strategy for reducing CO₂ emissions from hard-to-abate sectors. Reliable and efficient reservoir simulation tools are essential for supporting the safe and effective deployment of CCS projects. This study presents a twofold contribution to CCS modeling in saline aquifers: (1) the validation of the Black Oil Model (BoM) as a computationally efficient alternative to compositional simulators, and (2) a systematic assessment of the impact of grid resolution on plume prediction accuracy. The BoM was benchmarked against three commercial compositional simulators—Eclipse E300, CMG-GEM, and TNavigator. The comparison focused on key aspects of CO₂ storage operations, including plume evolution to assess containment and storage security, as well as injection safety and efficiency through pressure and saturation profile analysis, evaluated across both the injection and the post-closure monitoring phases. The BoM successfully reproduced plume extent and CO₂ saturation distributions, with mean deviations of 3% during injection, 5% during post-closure, and an overall average of 4% across the entire project duration. Additionally, simulation times were reduced by a factor of four compared to compositional models. These results confirm the BoM’s practical utility as a robust and efficient tool for CO₂ storage simulation. In parallel, the study investigated the influence of vertical and lateral grid resolutions/coarsening on the accuracy of CO₂ modeling. Seven models were developed and evaluated using a hybrid qualitative–quantitative framework, consistent with the BoM validation methodology. Vertical resolution was found to be particularly critical during the monitoring phase. While a 5 m resolution proved adequate during injection, deviations in plume shape and magnitude during post-injection increased to an average of 15% compared to a fine 2 m vertical resolution model, highlighting the necessity of fine vertical discretization (≤2 m) to capture gravity-driven plume dynamics during the monitoring phase. Conversely, lateral grid resolution had a stronger effect during the injection phase. A lateral cell size of 150 m was required for accurate plume prediction, with 200 m remaining moderately acceptable for early-phase assessment and prospect ranking, whereas coarser lateral grids led to significant underestimation of plume spread and dissolution extent. These findings demonstrate that the BoM, when combined with informed grid resolution strategies, enables accurate and computationally efficient simulation of CO₂ storage in saline aquifers. The study provides practical guidelines for fluid model selection and spatial discretization, offering critical input to subsurface experts involved in CCS project development, monitoring design, and regulatory compliance. Full article

A technology that directly injects CO₂-rich industrial waste gas (CO₂-rich IWG) into underground spaces for unconventional natural gas extraction and waste gas storage has received increasing attention. The pore characteristics of coal and shale in a coal-bearing rock series before and after CO₂-rich IWG treatment are closely related to gas recovery and storage. In this study, three coals ranging from low to high rank and one shale sample were collected. The samples were treated with CO₂-rich IWG using a high-precision geochemical reactor. The changes in the pore volume (PV), specific surface area (SSA), and pore size distribution of micropores, mesopores, and macropores were analyzed. The correlations between the Langmuir volume and the PV and SSA of the micropores and mesopores were analyzed. It was confirmed that for micropores, SSA was the dominant factor influencing adsorption capacity. The effectively interconnected pore volume was calculated using macropores to characterize changes in the sample’s connectivity. It was found that the PV and SSA of the micropores in the coal samples increased with increasing coal rank. The CO₂-rich IWG treatment increased the PV and SSA of the micropores in all of the samples. In addition, for mesopores and macropores, the treatment reduced the SSA in the coal samples but enhanced it in the shale. The results of this study improve the understanding of the mechanisms of the CO₂-rich IWG treatment method and emphasize its potential in waste gas storage and natural gas extraction. Full article

Phytoliths play a crucial role in plant growth and development. This paper analyzes the characterization of the culm sheath phytoliths of Bambusa vulgaris f.vittata across different geographic environments. The extraction of phytoliths was performed using microwave digestion, and the morphology of the phytolith was observed microscopically. The culm sheaths of Bambusa vulgaris f.vittata from GXNN, XSBN, GZGD, FJFZ, and FAFU Bambusa vulgaris f.vittata were selected for the study. The results indicated that the phytolith content and concentration were ranked as FJFZ > XSBN > GXNN > FAFU > GZHN, and the phytolith content and concentration were geographically significantly different. Saddle, Rondel, Silica stoma, and Scrobiculate (>70%) were observed in culm sheaths developed in different geographic environments, and phytolith morphology assemblages are largely homogeneous by genetic conservatism, but the proportion of each morphology varies across geographic environments. The main distribution of phytolith particle size ranges from 0 to 100 μm, with the highest peak in the 10–20 μm interval, followed by a decrease, and an elevation of up to 100–200 μm, followed by a significant reduction. The small size of the phytolith morphology was influenced by climatic factors. Specifically, the length, width, and area of XSBN increased with higher precipitation levels. Similarly, both the length and width of GDGZ also increased with increased precipitation. For FJFZ, the length increased with riding temperatures, while its width increased with higher precipitation. Additionally, the width of GXNN expanded with increasing temperatures. The present study supplemented the phytoliths analysis of the culm sheaths of Bambusa vulgaris f.vittata, which provided reference value for further research on the ability of Bambusa vulgaris f.vittata in carbon sequestration and other aspects, and contributed essential data for the robust development of the bamboo industry. Moreover, bamboo plants represent a significant natural solution to climate change, offering ecological, economic, and social benefits. This further encourages the protection of natural bamboo forests, the expansion of artificial cultivation, and the vigorous promotion of the bamboo industry and bamboo products. By maximizing their critical roles in forest carbon sequestration and climate regulation, bamboo plants provide a viable solution for global climate governance. Full article

In the process of Chinese-style modernisation, the socially and culturally coordinated development of cities in the Yangtze River Economic Belt is important for promoting regional coordinated development, enhancing the balance of public services, and strengthening cultural soft power. This study used quantitative methods, including the construction of an indicator system, spatial correlation analysis, and Zipf’s rank-size rule, on data from 2011 to 2021 to analyse the capacity for coordinated social and cultural development and assessed the spatial distribution characteristics of the Yangtze River Economic Belt. The study found that the overall level of social and cultural coordination among the cities in the Yangtze River Economic Belt steadily improved; however, significant regional disparities still exist, particularly in areas such as social security and cultural integration. Spatially, a “high in the east, low in the west” pattern is observed, with the Yangtze River Delta city cluster leading development, the midstream cluster playing a supportive role, and the Chengdu–Chongqing city cluster showing significant internal disparities. Core cities such as Shanghai, Hangzhou, Wuhan, and Chengdu demonstrated driving effects in areas such as culture, education, and healthcare; however, some peripheral cities remain underdeveloped. This study suggests the need to enhance the development of the Yangtze River’s culture, promote the development of cultural industry clusters, foster the integration of various business models, leverage scientific and educational resources, optimise the cultural consumption market, and achieve the coordinated development of the social and cultural sectors, thereby enabling the Yangtze River Economic Belt to play a greater role in Chinese-style modernisation. Full article

Vision–language models learn visual concepts from the supervision of natural language. It can significantly enhance the generalizability of real-time intelligent sensing, such as analyzing camera-captured real-time images for visually impaired users. However, adapting vision–language models to distribution shifts at test time, caused by several factors such as lighting or weather changes, remains challenging. In particular, most existing test-time adaptation methods rely on gradient-based fine-tuning and backpropagation, making them computationally expensive and unsuitable for real-time applications. To address this challenge, the Training-Free Dynamic Adapter (TDA) has recently been introduced as a lightweight alternative that uses a dynamic key–value cache and pseudo-label refinement for test-time adaptation without backpropagation. Building on this, we propose TDA-L, a new framework that integrates Low-Rank Adaptation (LoRA) to reduce the size of feature representations and related computational overhead at test time using pre-learned low-rank matrices. TDA-L applies LoRA transformations to both query and cached features during inference, cost-efficiently improving robustness to distribution shifts while maintaining the training-free nature of TDA. Experimental results on seven benchmarks show that TDA-L maintains accuracy but achieves lower latency, less memory consumption, and higher throughput, making it well-suited for AI-based real-time sensing. Full article

We compare the “classical” equations of type-token systems, namely Zipf’s laws, Heaps’ law and the relationships between their indices, with data selected from the Standardized Project Gutenberg Corpus (SPGC). Selected items all exceed 100,000 word-tokens and are trimmed to 100,000 word-tokens each. With the most egregious anomalies removed, a dataset of 8432 items is examined in terms of the relationships between the Zipf and Heaps’ indices computed using the Maximum Likelihood algorithm. Zipf’s second (size) law indices suggest that the types vs. frequency distribution is log–log convex, with the high and low frequency indices showing weak but significant negative correlation. Under certain circumstances, the classical equations work tolerably well, though the level of agreement depends heavily on the type of literature and the language (Finnish being notably anomalous). The frequency vs. rank characteristics exhibit log–log linearity in the “middle range” (ranks 100–1000), as characterised by the Kolmogorov–Smirnov significance. For most items, the Heaps’ index correlates strongly with the low frequency Zipf index in a manner consistent with classical theory, while the high frequency indices are largely uncorrelated. This is consistent with a simple simulation. Full article

The Bayesian Hurst–Kolmogorov (HK) method estimates the Hurst exponent of a time series more accurately than the age-old Detrended Fluctuation Analysis (DFA), especially when the time series is short. However, this advantage comes at the cost of computation time. The computation time increases exponentially with the time series length N, easily exceeding several hours for $N=1024$, limiting the utility of the HK method in real-time paradigms, such as biofeedback and brain–computer interfaces. To address this issue, we have provided data on the estimation accuracy of the Hurst exponent H for synthetic time series as a function of a priori known values of H, the time series length, and the simulated sample size from the posterior distribution n—a critical step in the Bayesian estimation method. The simulated sample from the posterior distribution as small as $n=25$ suffices to estimate H with reasonable accuracy for a time series as short as 256. Using a larger simulated sample from the posterior distribution—that is, $n>50$—provides only a marginal gain in accuracy, which might not be worth trading off with computational efficiency. Results from empirical time series on stride-to-stride intervals in humans walking and running on a treadmill and overground corroborate these findings—specifically, allowing reproduction of the rank order of $\widehat{H}$ for time series containing as few as 32 data points. We recommend balancing the simulated sample size from the posterior distribution of H with the user’s computational resources, advocating for a minimum of $n=50$. Larger sample sizes can be considered based on time and resource constraints when employing the HK process to estimate the Hurst exponent. The present results allow the reader to make judgments to optimize the Bayesian estimation of the Hurst exponent for real-time applications. Full article

Marine litter threatens ocean ecosystems, and nautical tourism, as a source of litter, contributes significantly. This paper presents a qualitative and quantitative study of seafloor litter in the Bay of Selehovica in the northern Adriatic Sea. The bay is accessible only by sea and is attractive to nautical tourism vessels. The survey was conducted using a remotely operated vehicle across 22,100 m² of seafloor, before and after the tourist season (summer) in 2024. The analysis shows a 25.90% increase in litter items after one season. The predominant litter category is plastic, followed by glass, metal, rubber, and textiles. The abundance of marine litter increased from 1.3 to 1.7 items per 100 m² in the post-season, reflecting a measurable rise in litter density. Due to non-normal data distribution (Shapiro–Wilk test, p < 0.001), the Wilcoxon Signed-Rank Test was used, revealing a statistically significant increase in marine litter (W = 0, p < 0.001) with a large effect size (Cohen’s d = 0.89). A strong positive correlation between the pre- and post-season values was observed (Spearman’s r = 0.96, p < 0.001), suggesting that areas with higher initial litter levels tend to accumulate more over time. The results point to the necessity of targeted management strategies to reduce the pressure of nautical tourism on marine ecosystems and to protect the marine environment. Full article

In the new paradigm where the digital economy is profoundly reshaping urban spatial organization, how the platform economy transcends traditional geographical constraints to restructure the urban system has become a strategic issue in urban geography and regional economics. This study develops an innovative measurement framework based on Business-to-Business (B2B) e-commerce enterprises to analyze platform-driven urban systems across 337 Chinese cities. Using spatial autocorrelation, rank-size distributions, and urban scaling laws, we reveal spatial differentiation patterns of cities’ B2B platforms. Combining Ordinary Least Squares (OLS) and random forest models with Partial Dependence Plots (PDP), Individual Conditional Expectations (ICE), and Locally Weighted Scatterplot Smoothing (LOWESS), we uncover non-linear mechanisms between platform development and urban attributes. Results indicate that (1) B2B platforms exhibit “superliner agglomeration” and “gradient locking”, reinforcing advantages in top-tier cities; (2) platform effects are non-linear, with Gross Domestic Product (GDP), Information Technology (IT) employment, and service sector shares showing threshold-enhanced marginal effects, while manufacturing bases display saturation effects; and (3) regional divergence exists, with eastern consumer-oriented platforms forming digital synergies, while western manufacturing platforms face path dependence. The findings highlight that platform economy evolution is shaped by a “threshold–adaptation–differentiation” mechanism rather than neutral diffusion. This study provides new insights into urban system restructuring under digital transformation. Full article

In today’s data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms process data locally, thereby reducing data transfer and exposure to breaches, while at the same time improving scalability thanks to data distribution across multiple sources. Top-k queries are a key tool in vertically distributed scenarios and are widely applied in critical applications involving sensitive data. Classical top-k algorithms typically resort to sorted access to sequentially scan the dataset and to random access to retrieve a tuple by its id. However, the latter kind of access is sometimes too costly to be feasible, and algorithms need to be designed for the so-called “no random access” (NRA) scenario. The latest efforts in this direction do not cover the recent advances in ranking queries, which propose hybridizations of top-k queries (which are preference-aware and control the output size) and skyline queries (which are preference-agnostic and have uncontrolled output size). The non-dominated flexible skyline ($\mathrm{ND}$) is one such proposal, which tries to obtain the best of top-k and skyline queries. We introduce an algorithm for computing $\mathrm{ND}$ in the NRA scenario, prove its correctness and optimality within its class, and provide an experimental evaluation covering a wide range of cases, with both synthetic and real datasets. Full article

This study aimed to investigate the impact of local canopy environmental characteristics on the regeneration of common tree species in the understory of broadleaved Korean pine forests, thus deepening the understanding of species coexistence and forest growth cycle mechanisms. This study focused on nine tree species found in the Liangshui National Nature Reserve in Heilongjiang Province, China. We stratified trees by height and simulated the LAI distribution of each class using Voronoi polygons. These layers were overlaid to generate an integrated LAI spatial map. All these procedures were integrated into the self-developed R package Broadleaf.Korean.pine.LAI, which was used to calculate individual-level canopy environment indicators, including average local LAI, local LAI standard deviation, canopy percent, vertical distribution tendency degree, local coniferous LAI, and local broadleaf LAI. These indicators were then compared with the average values of uniformly distributed understory sampling points. A principal component analysis (PCA) was conducted to reduce the dimensionality of the local canopy environmental characteristics for both the uniformly distributed points and regeneration habitats of each tree species, resulting in comprehensive canopy environmental characteristics. Wilcoxon rank-sum tests were applied to assess the significance of differences between the regeneration habitats and the understory average, as well as between the regeneration habitats of seedlings and saplings within the same species. Cliff’s delta effect size was used to evaluate the impact of each environmental factor on the transition of regeneration from seedlings to saplings. The results showed that, based on both individual canopy environmental indicators and composite indices derived from principal component analysis, seedlings tended to regenerate in areas with higher canopy coverage, whereas saplings were more commonly established in relatively open habitats. Clear differences exist between the regeneration habitats of coniferous and broadleaf species, with coniferous species tending to regenerate in areas with higher local broadleaf LAIs compared with broadleaf species. The effect size analysis showed that canopy percent, vertical distribution tendency degree, average local LAI, and local coniferous LAI have greater impacts on the transition from seedlings to saplings, while the effect of local broadleaf LAI is relatively small. These findings suggest that strong shade tolerance allows species to establish seedling banks under canopy patches, while interspecific differences in growth response to microhabitats shape their roles in the forest growth cycle. Future research should explore the physiological responses and trait characteristics of tree regeneration under varying canopy patch environments. Long-term monitoring of regeneration processes—including invasion, growth, and mortality—across different canopy patches will help elucidate the mechanisms shaping understory spatial patterns. Full article

In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications. Full article

This systematic review and meta-analysis aims to: (1) characterize the distribution of published inhibition of emergence (IE₅₀, IE₉₀, and IE₉₅) reference values for pyriproxyfen and methoprene in Culex pipiens [L.], Aedes aegypti [L.], and Aedes albopictus [Skuse]; (2) generate combined-effect IE values using a DerSimonian and Laird (DL) random-effects model to establish benchmarks for future resistance assessments; and (3) compare these combined-effect IE values with previously published literature. A systematic search was conducted in PubMed, SciELO, J-STAGE, and Google Scholar up to 10 February 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible studies were primary, peer-reviewed literature that aligned with World Health Organization (WHO) protocols for insect growth regulator (IGR) resistance testing, specifically those reporting susceptible reference IE values from continuous immersion dose-response bioassays analyzed using probit regression. A total of 72 unique studies that aligned with WHO protocols were assessed for publication bias using a funnel plot and Egger’s regression. Sensitivity and subgroup analyses were conducted to evaluate individual study contributions to the overall combined effect. Heterogeneity (I²) and combined effect values were estimated for 18 different species/active ingredient/IE concentration subgroup pairings. Heterogeneity (I²) ranged from 29.32 to 99.78% between the 18 subgroups, indicating inconsistency within the literature. The DL combined effect IE₅₀ varied from 0.048 ppb for Cx. pipiens exposed to pyriproxyfen to 1.818 ppb for Ae. albopictus exposed to methoprene. A certainty analysis indicated that 1 combined effect value exhibited high certainty, 8 out of 18 pairings were moderately certain, 6 exhibited low certainty and 3 exhibited very low certainty. The main causes of uncertainty (ranked) were inconsistency between studies, imprecision of the combined effect size, and possible publication bias. Our findings indicate that (1) robust DL combined effect IE₅₀ values could be established for all species/IGR pairings, providing essential benchmarks for future resistance assessments; (2) substantial heterogeneity among susceptible laboratory colonies complicates resistance detection in field-collected mosquitoes; and (3) a significant portion of the literature relies on reference mosquito strains that are likely not fully susceptible, further complicating resistance detection. This study was not registered and was supported by the North Shore Mosquito Abatement District. Full article

Efficient and safe extraction of coalbed methane is essential for reshaping China’s energy composition. This study integrates CO₂ adsorption, N₂ adsorption, and corrected mercury intrusion porosimetry (MIP) data to analyze the full pore size distribution (PSD) of six coal samples from the Qinshui and Tiefa Basins. By applying multifractal theory, we identified key heterogeneity features across different coal ranks, followed by a discussion of the factors influencing these parameters. The results indicate the following: (1) Coal matrix compressibility significantly impacts MIP results when mercury intrusion pressure exceeds 10 MPa, with corrected mesopore and macropore volume reductions ranging from 59.85–96.31% and 3.11–15.53%, respectively. (2) Pore volume distribution varies with coal rank, as macropores dominate in low-rank coal, while micropores contribute most in medium- and high-rank coal, accounting for over 90% of the total specific surface area. Multifractal analysis of CO₂, N₂, and corrected MIP data confirms notable multifractal characteristics across the full pore size range. (3) As the degree of coalification increases, as indicated by the rise in the R_o,max value, there is a notable negative correlation observed among the multifractal parameters D_min-D₀, D₀-D_max, Δα, and H. A positive correlation exists between moisture content and volatile matter content with D_min-D₀, Δα, and H, while a significant negative correlation is shown between the concentration of minerals and D_min-D₀, Δα, and H. There exists a favorable correlation between inertinite concentration and D₀-D_max. This work presents a theoretical foundation and empirical proof for the secure and effective extraction of coalbed methane in the researched region. Full article