As the core driving force of urban development, the spatial distribution and quantitative changes of population are of key significance to urban planning. Based on the GIS center of gravity model, grey GM(1, 1) model, BP neural network and GA-BP neural network, this study used the year-end permanent resident population data of Guangzhou City from 2010 to 2024 to explore the evolution path of population center of gravity and the future population number of Guangzhou City. By comparing the three forecasted population models, it was found that GA-BP neural network had the best prediction effect and predicted the population in 2035 accordingly; ① The results show that the population center of gravity in Guangzhou City shifted to the southeast from 2010 to 2024, with the largest shift from 2020 to 2024 and will continue to shift to the southeast in the future; ② It is estimated that by 2035, the population of Liwan, Yuexiu, Huangpu and Conghua will be basically stable, the population of Haizhu, Tianhe and Baiyun will decrease, and the population of Panyu, Huadu, Nansha and Zengcheng will increase; ③ The central urban area needs to continue to relieve the high-density population, and Nansha and Panyu should prevent the risk of “separation of industry and city” and simultaneously support public service facilities. The research results can provide a scientific basis for the territorial spatial planning and policy formulation of Guangzhou City, help to enhance the city's competitiveness and sustainable development capacity, and provide a reference for the high-quality development of urbanization in China.

Driven by the digital economy, the digital transformation of the construction waste recycling industry has emerged as an inevitable pathway to advance the sector's in-depth integration and high-quality upgrading. Developing scientifically sound and effective transformation strategies is therefore critical to addressing the current bottlenecks constraining industrial development. To tackle the challenge of stakeholder collaboration amid this industrial transformation, this study first constructs a four-party evolutionary game model involving the government, developers, construction firms, and recycling enterprises. It then systematically analyzes the strategy selection logic of each stakeholder and the stability conditions of the system's equilibrium points. Further, the study integrates system dynamics (SD) to establish an SD-evolutionary game hybrid model. Scenario simulations are conducted focusing on core variables, including the scale of government subsidies, enterprises' digital transformation costs, transformation benefits for individual stakeholders, and collaborative transformation gains for two or three parties. These simulations help unveil the dynamic evolutionary patterns and feedback mechanisms underlying multi-stakeholder strategies, ultimately leading to the proposal of collaborative strategies for the industry's digital transformation. The results indicate three key findings: ① Government subsidies positively incentivize enterprises' willingness to transform, but such subsidies require dynamic adjustment—excessively high or low subsidy levels will diminish stakeholders' transformation motivation. ② The additional benefits gained by developers, construction firms, and resource utilization enterprises through collaborative transformation exhibit a significant positive correlation with each stakeholder's willingness to pursue digital transformation. ③ The improvement of regional digital infrastructure and the development level of the digital economy directly influence the effectiveness of transformation cost control, while enhancing collaborative innovation capabilities within the industrial chain constitutes the core pathway to increasing stakeholders' collaborative benefits. This research not only provides theoretical support for clarifying the multi-stakeholder behavioral logic of the construction waste recycling industry in the context of digital transformation but also offers practical references for governments to formulate industrial collaborative governance policies and for enterprises to optimize their transformation decisions.

"Zero-waste City" is a new urban development model and governance concept. Based on the evaluation index system of "Zero-waste City" construction pilot work plan in China, and 233 questionnaire data analysis, the research used the applied management entropy theory and busselator model, to investigate and evaluate the system dissipation structure of "Zero-waste City" management system. It concluded that, the dissipation structure of "Zero-waste City" management system in China has not yet been formed, and the main factors affecting the formation of "Zero-waste City" in China are the number of units for the construction of zero-waste cities, the amount of safe disposal of industrial hazardous materials, the coverage rate of medical waste collection and disposal systems, the amount of waste discharged from construction, the penetration rate of sanitary toilets in rural areas, the political completion rate of informal landfill sites, the formulation of policy regulations or policy documents in cities without waste, and the quantification of household waste. The formation of a "Zero-waste City" needs to reduce its internal positive entropy environment, and requires the government, enterprises, and the public to establish a sufficiently large negative entropy external environment to enable "Zero-waste City" management system in China to evolve into a dissipative structure. Finally, the suggestion was put forward to construct a "Zero-waste City".

Sugarcane provides over 70% of the world's sugar raw materials, making an accurate prediction of its maturity period is crucial for ensuring sugar production and optimizing harvest resource allocation. Satellite remote sensing offers a powerful tool for large-scale crop growth monitoring, yet challenges persist in understanding the underlying mechanisms of maturity prediction. This study addresses this gap by focusing on two key meteorological factors affecting sugarcane development—temperature and solar radiation—using effective accumulated degree days (AGDD) and accumulated solar radiation (ASR) as driving variables. We developed a hybrid remote sensing-meteorological maturity prediction model through three key steps. First, normalized difference vegetation index (NDVI) time series were reconstructed using Double-Logistic models with spatial-temporal fused Landsat-MODIS time series datasets. Second, three critical growth stages including germination, tillering and elongation, were extracted from the reconstructed NDVI time series. Third, the AGDD-based and ASR-based maturity prediction models initiated from each identified growth stage were developed. The predicted maturity dates were validated using the multi-year phenological data from Guangxi and Yunnan provinces. The validation results demonstrated that the ASR-based models consistently outperformed AGDD-based models across all tested growth stages, and models initialized from the germination stage achieved the highest accuracy (RMSE=17.7 days, RRMSE=5.3%). This research establishes a methodological framework for large-scale sugarcane maturity prediction, offering technological support for precise agriculture management in sugarcane production systems.

Influenza viruses are major respiratory pathogens that can cause large-scale human outbreaks and pose a serious threat to global health. Many historical influenza pandemics have originated from swine or avian influenza viruses or reassortant hybrids. The development of interpretable and generalizable predictive models for human-to-human transmission is therefore critically important for early warning systems. This study presents a deep learning model incorporating attention mechanisms to predict the transmission risk of swine influenza virus to humans. The model combines convolutional neural networks and bidirectional gated recurrent units to capture local patterns and long-range dependencies in viral genomic sequences. Its key innovation lies in an interpretable attention mechanism that not only improves performance but also identifies and visualizes critical genomic regions related to host adaptation, such as PB1, PA, and HA segments. Using five-fold cross-validation, the model achieved an average AUROC exceeding 0.99, outperforming baseline models by approximately 0.150 in AUROC. The attention weights showed strong agreement with established biological evidence, confirming the model's biological relevance. In summary, the proposed model demonstrates high predictive accuracy and provides valuable support for public health surveillance and early warning systems against influenza transmission.

Vehicle-to-Infrastructure (V2I) technology, as a key component of the Vehicle Infrastructure Cooperative System (VICS), holds potential for alleviating traffic congestion, enhancing safety, and reducing emissions. This study aims to optimize signalized intersection control through V2I to minimize vehicle emissions. The spatiotemporal influence range of intersections was determined as 300 m based on DSRC communication. A dynamic speed control model and an emission model based on specific power method were established. Simulations were conducted via MATLAB to compare vehicle operational efficiency and emissions with and without V2I control. Simulation parameters included a control area of 600 m and traffic volume of 900 pcu/h. Results show that,V2I control significantly reduced emissions of CO, CO₂, NO_x, and HC by 13.5%, 9.6%, 19.9%, and 11.1%, respectively, while improving traffic efficiency, demonstrating its effectiveness in energy conservation and emission reduction.

The involvement of microRNAs (miRNAs) in various diseases has been established. Thus, discovering miRNA-disease associations (MDAs) aids in the understanding of disease pathogenesis and makes it easier to develop effective therapeutics that target miRNAs. Compared with traditional biological experimental methods, identifying MDAs through computational methods has been proven to be low-cost and efficient. This study introduces the MDAES method for MDA prediction by integrating miRNA expression data and similarity networks. In MDAES, the optimal kernels of miRNAs and diseases are first constructed through multiple kernel learning (MKL) from their similarity kernels. Next, MDAES applies a regression model to obtain feature representations of miRNAs as well as diseases. Finally, by integration of the similarity network features of miRNAs and diseases, as well as the features of miRNA expression data as input to the deep neural network (DNN), MDAs are predicted. As a result, MDAES exhibits good performance on three benchmark datasets (AUCs >0.95) and outperforms the currently employed methods. Case study assessments of both breast and lung cancers demonstrate that 96% and 100%, of the top 30 predictions are linked to the two cancers, respectively. Overall, MDAES has great potential as a reliable and practical model to identify MDAs.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most economically active regions in China. Its extensive fish pond aquaculture activities play an important role in the sustainable supply of regional aquatic products. However, the carbon balance contribution caused by the high-intensity aquaculture activities in these fish ponds is still lacking in quantitative research. This study used the Sentinel-3 Ocean and Land Colorimeter (OLCI) data, combined with a semi-analytical inversion algorithm based on key bands (Oa08, Oa09, Oa017), and performed atmospheric correction through the SeaDas software to improve the reflectance accuracy, providing reliable data for carbon concentration inversion, and then estimating the carbon concentration of phytoplankton in fish ponds in the GBA from 2016 to 2024.The results show that: ① Overall, the carbon concentration of phytoplankton in fish ponds in the GBA presents a spatial distribution of high in the northwest and low in the southeast, mainly showing a trend of gradually weakening from inland cities to coastal cities, and the concentration differences between different cities are obvious. ② The seasonal changes in the carbon concentration of phytoplankton in aquaculture fish ponds in the GBA are more significant than the interannual changes. The high concentration season is autumn, with an average concentration of 2.193 mg/L; followed by summer and winter, with 1.314 and 1.465 mg/L respectively; the lowest concentration is in spring, which is 0.632 mg/L. ③ The high concentration areas are mainly distributed in inland areas such as Zhaoqing City and Foshan City in the northwest, and the low concentration is mainly distributed in the coastal areas in the southeast. ④ The interannual variation of phytoplankton carbon concentration showed a statistically insignificant increasing trend, and showed a slight upward trend year by year in the Sen's slope estimation, with an annual increase rate of 0.01~0.05 mg/L, mainly concentrated in Huizhou, Foshan and Hong Kong. The study verified the application potential of the Sentinel-3 OLCI satellite in estimating carbon flux in aquaculture areas, clarified the spatiotemporal dynamic changes of phytoplankton carbon concentration in small fish pond ecosystems, and its important impact on the regional carbon cycle. The results provide a scientific basis for optimizing environmental management in the GBA and studying carbon dynamics in similar aquaculture systems.

MicroRNAs (miRNAs) play key regulatory roles on cellular functions such as apoptosis and angiogenesis. The pathogenesis of complex diseases such as cancer is fundamentally linked to dysregulation of these cellular functions. Quantifying impact on abnormal cellular functions of miRNAs enables an understanding of the mechanism of disease progression and identifying novel therapeutic targets. Although traditional molecular biological experiments can verify the relationship between miRNAs and abnormal cellular functions in diseases, they have significant limitations such as long experimental cycle and high cost. To address such problems, in this paper, the impact of miRNAs on abnormal cellular functions in disease by the shortest path between differentially expressed miRNAs and functional genes was quantified. The proposed method is applied to lung cancer and colorectal cancer datasets to identify miRNAs associated with abnormal disease functions. The results of literature and KEGG pathway enrichment analysis show that SP-MFA can effectively identify miRNAs associated with abnormal cellular functions in diseases.

The engineering investigation industry is currently undergoing a paradigm shift from experience-driven to data- and intelligence-driven practices. This paper constructs an integrated analytical framework incorporating digital transformation theory, human-machine collaboration principles, and a structured analytical model to systematically deconstruct AI integration mechanisms in engineering investigation.Through multi-case empirical studies of three core technologies—intelligent lithological core identification, rapid access to investigation results, and intelligent drawing review—this research reveals the inherent pathways for AI-driven reconstruction of investigation workflows within a data-algorithm-platform architecture. The findings demonstrate that AI applications significantly enhance investigation efficiency, data consistency, and quality control capability. Specifically, drawing review efficiency improved by 72%, layer consistency reached 92%, and the violation rate of mandatory provisions was reduced by 81%.This success relies on an analytical framework following the vertical logic of theory-model-implementation-application and integrating horizontal key elements such as data-algorithm-domain-platform. However, challenges such as insufficient technology generalization, a shortage of interdisciplinary talent, absence of industry standards, and uncertainty regarding return on investment remain major implementation barriers.This paper proposes that the industry should evolve toward intelligent equipment, platform ecosystems, and data-driven services, transitioning from passive investigation to proactive prediction through the development of a "geological brain." This study provides a systematic framework combining theoretical depth with practical operability for the intelligent transformation of the engineering investigation industry.

Artificial light at night (ALAN) can intensify ecological barriers caused by land development in rapidly urbanizing regions by reducing nocturnal habitat suitability and low-light spatial continuity. To reveal the spatial pattern and connectivity bottlenecks of dark ecological spaces in a highly urbanized bay area, this study develops a dark-conservation-oriented “ecological source-resistance surface-corridor network” framework for the Guangdong-Hong Kong-Macao Greater Bay Area. Ecological sources were identified by integrating MSPA, InVEST habitat quality assessment, and Conefor connectivity analysis, with nighttime light constraints further applied to delineate dark ecological sources. Based on conventional resistance factors, ALAN was incorporated into the composite resistance surface as an independent ecological stressor. Dark ecological corridors were then extracted using circuit theory, and key nodes were identified. Results show that 147 dark ecological sources were identified, mainly distributed in less urbanized areas such as Zhaoqing, Jiangmen, Huizhou, and northern Guangzhou. The composite resistance surface exhibited a “high in the center and low in the periphery” pattern. A total of 330 dark ecological corridors, totaling 3 766.624 km, were extracted, along with 87 ecological pinch points and 115 ecological barrier points. These findings indicate that incorporating ALAN into ecological security pattern construction can effectively reveal connectivity risks in nocturnal ecological processes and provide scientific support for dark ecological network optimization and zoned urban lighting governance in the Greater Bay Area.

With the widespread use of the internet, identity authentication has become increasingly important in information systems, while password authentication remains one of the most commonly used authentication methods. In recent years, advances in deep learning have promoted the application of generative adversarial networks (GANs) to password guessing. However, GAN-based password guessing models, such as PassGAN, still suffer from relatively low guessing efficiency. Compared with the traditional password guessing models, these methods generally need to generate more candidate passwords to achieve the same cracking rate and often produce a large number of duplicate candidates. To improve the diversity of generated passwords and improve the guessing rate, a password guessing model named DPAC-Pass is proposed based on reinforcement learning and cross-entropy reward, which adopts the Actor-Critic algorithm in the password guessing task and uses cross-entropy as a single-step reward to guide the model training. The proposed DPAC-Pass model and the PCFG, SeqGAN, and AC-Pass models in the literature are trained on the LinkedIn passphrase set and tested on LinkedIn, RockYou and CSDN set, and compared in terms of the generated password features, guessing efficiency, password diversity, and quality of unique passwords. The experimental results show that the password cracking rate is improved with the proposed DPAC-Pass model by enhancing diversity of generated passwords so that password length and character composition can be more accurately captured.

Let b and k be positive intrgers with k≥b, and let G be a simple graph. A b-fold k-coloring of G is an assignment of b distinct colors to every vertex, from a set of k colors, such that adjacent vertices do not have any colors in common. The b-fold chromatic number of G, denoted by Χ_b(G), is the minimum number k such that a b-fold k-coloring of G exists, i.e., Χ_b(G)=min{k|there exists a b-fold k-coloring of G}. In this paper, we discuss the relation between the b-fold chromatic number of the join, weak product, and composition of two graphs and the b-fold chromatic number of their original graphs. We also obtain the relation between the b-fold chromatic number of Mycielski's transformation graph of a graph G and the b-fold chromatic number of the original G.

The health-oriented operation and maintenance (O&M) of in-building water supply and drainage systems currently relies predominantly on the "threshold alarm" paradigm. While this approach can detect anomalies, it struggles to reveal the root causes of failures, predict degradation trends, or guide targeted maintenance-a limitation particularly pronounced in aging building systems characterized by complex deterioration mechanisms and dynamic operating conditions. To achieve a fundamental paradigm shift from "passive alarm" to "active mechanism tracing" in health assessment, this paper proposes a novel health assessment theory integrating multi-field coupling mechanisms with lightweight digital twins. At its core lies a physicochemical model of synergistic multi-material deterioration under the coupled effects of water quality, temperature, and stress fields, which addresses the longstanding bottleneck of "unclear mechanisms." A composite assessment model combining the dynamic adaptive entropy weight method with a dynamic Bayesian network is further developed to resolve the challenge of "quantification imbalance" in dynamic uncertainty characterization. On this basis, a lightweight digital twin architecture driven by the aforementioned "mechanism kernel" is designed, which overcomes the limitations of "superficial integration" and "paradigm misalignment" through closed-loop mechanism-data iteration. A simulation case study on the deterioration process of a typical water supply riser verifies the logical feasibility of the proposed theory and method in state identification, adaptive weight adjustment, and trend prediction. The results demonstrate that the proposed theory and method provide a systematic solution for interpretable assessment, predictive maintenance, and precise decision-making regarding the health status of building water supply and drainage systems, advancing the paradigm transformation of infrastructure O&M from experience-driven to mechanism-data fusion-driven.

In the context of the continuous advancement of the "Healthy China" strategy, the public's awareness of the importance of physical and mental health has deepening, leaded to structural changes in pubbcusage patterns and preferences regarding natural parks. This study takes natural exammes in Guangzhou as the research object, based on mobile signaling data from 2019, 2020, and 2022, and systematically analyzes the differentiation characteristics and dynamic trends of natural park use preferences from the dimensions of spatiotemporal patterns, park types, and population groups. The results show that: First, in terms of time, visitor numbers during holidays are much higher than on weekdays, indicating a clear holiday preference. Meanwhile, the proportions of female, underage, and elderly visitors are low and further declined during the pandemic. Sencond, in terms of space, residents show a strong preference for the main urban area. However, after the pandemic, the share of visitors to non-main urban natural parks rose from 21.95% to 30.40%. Some outer suburban parks even exceeded pre-pandemic visitor levels, indicating an increased willingness for long-distance travel. At the same time, young adults tend to travel longer distances, while minors and the elderly mainly visit natural parks in the main urban area and rarely visit those in non-main areas, reflecting intergenerational spatial differentiation. Third, in terms of type, the differences in usage intensity among parks have narrowed. The average daily visitor numbers of wetland parks, forest parks, and scenic areas are roughly equal. This reflects a more balanced use of natural park types, and the public pays more attention to factors such as openness and crowd density. These changes indicate that major public health events have driven a systemic shift in the public's preferences for using natural parks. This study will provide a scientific basis for the spatial optimization of natural parks, the construction of healthy cities, and the response of large urban green spaces to public health emergencies.

The concept of neighbor sum distinguishing I－total coloring of graphs is defined. By using mathematical induction, the structural coloring method, and combinatorial analytic method, we mainly study the neighbor sum distinguishing I－total coloring of unicyclic graphs, and obtained the neighbor sum distinguishing I－total chromatic number for unicyclic graphs.

Steel-precast ultra-high-performance concrete (UHPC) composite beams utilizing high-strength bolts as shear connectors exhibit advantages such as superior mechanical performance and convenient disassembly. Currently, research on the flexural performance of such composite beams remains insufficient. To this end, one such specimen was designed and subjected to a four-point bending test to investigate its flexural behavior. The test results indicate that the composite beam specimen primarily experienced bolt shear fracture as the dominant failure mode, and significant flexural cracks were observed at the bottom of the UHPC slab beneath the loading points. The rigid plastic analysis method and the simplified analysis method recommended by Eurocode 4 were employed to calculate the flexural capacity. The ratios of the calculated values to the experimental values were 0.98 and 0.80, respectively, demonstrating that the former is more applicable for predicting the ultimate flexural capacity of this type of composite beam.

To mitigate the grid-connected current distortion and double-frequency power oscillations caused by an unbalanced RL-filter in grid-connected photovoltaic (PV) inverters, this paper proposes an enhanced Single-Degree-of-Freedom Active Disturbance Rejection Control (SDOF-ADRC) strategy. First, a mathematical model of the grid-connected inverter with non-ideal filters is established. Theoretical derivation reveals that filter parameter imbalance introduces DC disturbances. It also generates significant 100 Hz AC disturbances in the synchronous rotating frame, which severely degrades power quality. To address these complex disturbances, a resonant controller is integrated into the extended state observer of the traditional SDOF-ADRC. This allows the improved observer to estimate DC disturbances while effectively observing and compensating for corresponding AC disturbance. Consequently, the system achieves simultaneous estimation and suppression of both DC and AC disturbances. The Bode plots analysis demostrates that the improved strategy maintains the closed-loop bandwidth while achieving high observation gain at 100 Hz. Compared with traditional PI control and standard SDOF-ADRC, this method provides superior disturbance attenuation in specific frequency bands. MATLAB/Simulink simulations verify the effectiveness of the proposed method, which maintains a simple and efficient control architecture. While effectively reducing dq-axis current ripples, this strategy significantly enhances system robustness against filter parameter perturbations and improves dynamic response. Overall, the approach demonstrates significant practical value for engineering applications.

To explore the sound absorption performance of porous carbon materials, this study prepared three-dimensional ordered porous carbon materials by the template method, and investigated the influence of porous carbon materials prepared at a temperature of 600-1 000 ℃ on the sound absorption performance under a temperature gradient of 100 ℃. Using porous alumina as the template, a mixture of phenolic resin, zinc acetate (Zn(CH₃COO)₂), and tetraethyl orthosilicate (TEOS) was employed to fabricate the 3DOPC sound-absorbing materials. The pore structure, surface morphology, and crystallinity were characterized by nitrogen adsorption-desorption analysis, scanning electron microscopy (SEM), and X-ray diffraction (XRD). The acoustic properties were evaluated using the impedance tube method. The appearance of the characteristic peaks of ZnO in XRD verified the successful thermal decomposition of zinc acetate into ZnO. Acoustic measurements revealed that the 3DOPC material achieved an average sound absorption coefficient of 0.89 in the frequency range of 3 000-4 000 Hz. This work provides an effective strategy for synthesizing porous carbon materials for sound absorption applications.

This paper investigates the n-fold convolution μ^*n of an in-homogeneous self-similar measure μ on a line, generated by a family of contractive mappings satisfying the equal contraction condition and the open set condition. It is proved that if the in-homogeneous term measure ν of μ also satisfies a certain Fourier integral decay condition, then the Hausdorff dimension, the L^q-dimension, and the entropy dimension of the n-fold convolution μ^*n converge to 1 as n→∞.

Using the 65-meter Shanghai Tianma Radio Telescope, we carried out mapping observations of molecular H₂CO (2₁₁-2₁₂) and H¹³₂CO (2₁₁-2₁₂) (with rest frequencies of 14.488 GHz and 13.779 GHz, respectively) toward the SgrB2 giant molecular cloud complex in the Galactic center. The H₂CO (2₁₁-2₁₂) line was detected at all 128 observed positions. Follow-up observations of H¹³₂CO (2₁₁-2₁₂) were carried out for 89 sources exhibiting strong H₂CO (2₁₁-2₁₂) emission, and its line emission (signal-to-noise ratio>3) was successfully detected at 18 positions. We analyzed the detected spectral lines to derive parameters including integrated line intensities, and calculated the line intensity ratios of the two molecules at each position. The contour maps show that the spatial distributions of the line intensities of the two molecular gases are similar, both exhibiting a higher intensity in the central region and a gradual decrease toward the periphery. However, the spatial distribution of their intensity ratio, i.e., I (H₂CO (2₁₁-2₁₂))/I (H¹³₂CO (2₁₁-2₁₂)), presents distinct characteristics, with relatively low values in the gas-rich central region. Our analysis indicates that this phenomenon arises from the saturation effect of the H₂CO (2₁₁-2₁₂) line due to its extremely high abundance in the central region. After considering effects such as optical depth, the measured isotopic abundance ratio determined in this work is consistent with the latest results derived from C³⁴S/¹³C³⁴S observations.