Search Results (446)

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

Variable-thrust liquid rocket engines are essential for precision landing in deep-space exploration, reusable launch vehicle recovery, high-accuracy orbital maneuvers, and emergency obstacle evasions of space drones. However, with the increasingly complex space missions, challenges remain with the development of different technical schemes. In view of these issues, this paper systematically reviews the technology’s evolution through mechanical throttling, electromechanical precision regulation, and commercial space-driven deep throttling. Then, the development of key variable thrust technologies for liquid rocket engines is summarized from the perspective of thrust regulation and control strategy. For instance, thrust regulation requires synergistic flow control devices and adjustable pintle injectors to dynamically match flow rates with injection pressure drops, ensuring combustion stability across wide thrust ranges—particularly under extreme conditions during space drones’ high-maneuver orbital adjustments—though pintle injector optimization for such scenarios remains challenging. System control must address strong multivariable coupling, response delays, and high-disturbance environments, as well as bottlenecks in sensor reliability and nonlinear modeling. Furthermore, prospects are made in response to the research progress, and breakthroughs are required in cryogenic wide-range flow regulation for liquid oxygen-methane propellants, combustion stability during deep throttling, and AI-based intelligent control to support space drones’ autonomous orbital transfer, rapid reusability, and on-demand trajectory correction in complex deep-space missions. Full article

Achieving sustainable development goals remains a core issue in global development. In response, China has proposed the development of new quality productive forces (NQPFs) through innovative thinking, emphasizing that fostering NQPFs is both an intrinsic requirement and a pivotal focus for advancing high-quality development. Concurrently, the intelligent transformation of the manufacturing sector serves as a critical direction for China’s economic restructuring and upgrading. This paper places “new quality productive forces” and “intelligent transformation of manufacturing” within the same analytical framework. Starting from the logical chain of “new quality productive forces—three major mechanisms—intelligent transformation of manufacturing,” it concretizes the value implications of new quality productive forces into a systematic conceptual framework driven by the synergistic interaction of three major mechanisms: the mechanism of revolutionary technological breakthroughs, the mechanism of innovative allocation of production factors, and the mechanism of deep industrial transformation and upgrading. This study constructs a “3322” evaluation index system for NQPFs, based on three formative processes, three driving forces, two supporting systems, and two-dimensional characteristics. Simultaneously, it builds an evaluation index system for the intelligent transformation of manufacturing, encompassing intelligent technology, intelligent applications, and intelligent benefits. Using national time-series data from 2012 to 2023, this study assesses the development levels of both NQPFs and the intelligent transformation of manufacturing during this period. The study further analyzes the impact of NQPFs on the intelligent transformation of the manufacturing sector. The research results indicate the following: (1) NQPFs drive the intelligent transformation of the manufacturing industry through the three mechanisms of innovative allocation of production factors, revolutionary breakthroughs in technology, and deep transformation and upgrading of industries. (2) The development of NQPFs exhibits a slow upward trend; however, the outbreak of the pandemic and Sino-US trade frictions have caused significant disruptions to the development of new-type productive forces. (3) The level of intelligent manufacturing continues to improve; however, from 2020 to 2023, due to the impact of the COVID-19 pandemic and Sino-US trade conflicts, the level of intelligent benefits has slightly declined. (4) NQPFs exert a powerful driving force on the intelligent transformation of manufacturing, exerting a significant positive impact on intelligent technology, intelligent applications, and intelligent efficiency levels. Full article

Beech forests in the Italian peninsula are actively managed and they also support a high level of biodiversity. Hence, biodiversity conservation can be synergistic with timber production and carbon sequestration, enhancing the overall economic benefits of forest management. This study aimed to evaluate the effect of forest management regimes on bird communities in the Italian Peninsula during 2022 through audio recordings. We studied the structure, composition, and specialization of the breeding bird community in four managed beech stands (three even-aged beech stands aged 20, 60, and 100 years old, managed by a uniform shelterwood system; one uneven-aged stand, managed by a single-tree selection system) and one uneven-aged, unmanaged beech stand in the northern Apennines (Tuscany region, Italy). Between April and June 2022, data were collected through four 1-hour audio recording sessions per site, analyzing 5 min sequences. The unmanaged stand hosted a richer (a higher number of species, p < 0.001) and more specialized (a higher number of cavity-nesting species, p < 0.001; higher Woodland Bird Community Index (WBCI) values, p < 0.001; and eight characteristic species, including at least four highly specialized ones) bird community, compared to all the managed forests; moreover, the latter were homogeneous (similar to each other). Our study suggests that the unmanaged beech forests should be a priority option for conservation, while in terms of the managed beech forests, greater attention should be paid to defining the thresholds for snags, deadwood, and large trees to be retained to enhance their biodiversity value. Studies in additional sites, conducted over more years and including multi-taxon communities, are recommended for a deeper understanding and generalizable results. Full article

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

Background/Objectives: Melanoma is an aggressive type of skin cancer that poses serious health risks if not detected in its early stages. Although early diagnosis enables effective treatment, delays can result in life-threatening consequences. Traditional diagnostic processes predominantly rely on the subjective expertise of dermatologists, which can lead to variability and time inefficiencies. Consequently, there is an increasing demand for automated systems that can accurately classify melanoma lesions and retrieve visually similar cases to support clinical decision-making. Methods: This study proposes a transfer learning (TL)-based deep learning (DL) framework for the classification of melanoma images and the enhancement of content-based image retrieval (CBIR) systems. Pre-trained models including DenseNet121, InceptionV3, Vision Transformer (ViT), and Xception were employed to extract deep feature representations. These features were integrated using a weighted fusion strategy and classified through an Ensemble learning approach designed to capitalize on the complementary strengths of the individual models. The performance of the proposed system was evaluated using classification accuracy and mean Average Precision (mAP) metrics. Results: Experimental evaluations demonstrated that the proposed Ensemble model significantly outperformed each standalone model in both classification and retrieval tasks. The Ensemble approach achieved a classification accuracy of 95.25%. In the CBIR task, the system attained a mean Average Precision (mAP) score of 0.9538, indicating high retrieval effectiveness. The performance gains were attributed to the synergistic integration of features from diverse model architectures through the ensemble and fusion strategies. Conclusions: The findings underscore the effectiveness of TL-based DL models in automating melanoma image classification and enhancing CBIR systems. The integration of deep features from multiple pre-trained models using an Ensemble approach not only improved accuracy but also demonstrated robustness in feature generalization. This approach holds promise for integration into clinical workflows, offering improved diagnostic accuracy and efficiency in the early detection of melanoma. Full article

Quinoa (Chenopodium quinoa Willd.) has emerged as a climate-resilient, nutrient-dense crop with increasing global popularity because of its adaptability under current environmental variations. To address the limited understanding of quinoa’s genotypic performance under local agro-environmental conditions, this study hypothesized that elite genotypes would exhibit significant variation in agronomic, physiological, and biochemical traits. This study aimed to elucidate genotypic variability among 23 elite quinoa lines under field conditions in Faisalabad, Pakistan, using a multidimensional framework that integrated phenological, physiological, biochemical, root developmental, and yield-related attributes. The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. Moreover, Q9 and Q52 showed enhanced accumulation of antioxidant compounds such as phenolics and anthocyanins, suggesting potential for functional food applications and breeding program for improving these traits in high-yielding varieties. Furthermore, root trait analysis revealed Q15, Q24, and Q82 with well-developed root systems, suggesting efficient resource acquisition and sufficient support for above-ground plant parts. Moreover, principal component analysis further clarified genotype clustering based on trait synergistic effects. These findings support the use of multidimensional phenotyping to identify ideotypes with high yield potential, physiological efficiency and nutritional value. The study provides a foundational basis for quinoa improvement programs targeting climate adaptability and quality enhancement. Full article

Iopamidol (IPM), as a typical recalcitrant emerging pollutant and precursor of iodinated disinfection by-products (I-DBPs), is unsuccessfully removed by conventional wastewater treatment processes. This study comprehensively evaluated the ozone/peracetic acid (O₃/PAA) process for IPM degradation, focusing on degradation kinetics, environmental impacts, transformation products, ecotoxicity, disinfection byproducts (DBPs), and microbial inactivation. The O₃/PAA system synergistically activates PAA via O₃ to generate hydroxyl radicals (^•OH) and organic radicals (CH₃COO^• and CH₃CO(O)O^•), achieving an IPM degradation rate constant of 0.10 min⁻¹, which was significantly higher than individual O₃ or PAA treatments. The degradation efficiency of IPM in the O₃/PAA system exhibited a positive correlation with solution pH, achieving a maximum degradation rate constant of 0.23 min⁻¹ under alkaline conditions (pH 9.0). Furthermore, the process demonstrated strong resistance to interference from coexisting anions, maintaining robust IPM removal efficiency in the presence of common aqueous matrix constituents. Furthermore, quenching experiments revealed ^•OH dominated IPM degradation in O₃/PAA system, while the direct oxidation by O₃ and R-O^• played secondary roles. Additionally, based on transformation products (TPs) identification and ECOSAR predictions, the primary degradation pathways were elucidated and the potential ecotoxicity of TPs was systematically assessed. DBPs analysis after chlorination revealed that the O₃/PAA (2.5:3) system achieved the lowest total DBPs concentration (99.88 μg/L), representing a 71.5% reduction compared to PAA alone. Amongst, dichloroacetamide (DCAM) dominated the DBPs profile, comprising > 60% of total species. Furthermore, the O₃/PAA process achieved rapid 5–6 log reductions of E. coli. and S. aureus within 3 min. These results highlight the dual advantages of O₃/PAA in effective disinfection and byproduct control, supporting its application in sustainable wastewater treatment. Full article

In wet flue gas desulfurization and denitrification processes, nitrite accumulation inhibits denitrification efficiency and induces secondary pollution due to its acidic disproportionation. This study developed a Mn-modified ZSM-5 zeolite catalyst, achieving efficient resource conversion of nitrite in nitrogen-containing wastewater through an O₃ + Mn/ZSM-5 catalytic system. Mn/ZSM-5 catalysts with varying SiO₂/Al₂O₃ ratios (prepared by wet impregnation) were characterized by BET, XRD, and XPS. Experimental results demonstrated that Mn/ZSM-5 (SiO₂/Al₂O₃ = 400) exhibited a larger specific surface area, enhanced adsorption capacity, abundant surface Mn³⁺/Mn⁴⁺ species, hydroxyl oxygen species, and chemisorbed oxygen, leading to superior oxidation capability and catalytic activity. Under the optimized conditions of reaction temperature = 40 °C, initial pH = 4, Mn/ZSM-5 dosage = 1 g/L, and O₃ concentration = 100 ppm, the $\mathrm{N}{\mathrm{O}}_2^{-}$ oxidation efficiency reached 94.33%. Repeated tests confirmed that the Mn/ZSM-5 catalyst exhibited excellent stability and wide operational adaptability. The synergistic effect between Mn species and the zeolite support significantly improved ozone utilization efficiency. The O₃ + Mn/ZSM-5 system required less ozone while maintaining high oxidation efficiency, demonstrating better cost-effectiveness. Mechanism studies revealed that the conversion pathway of $\mathrm{N}{\mathrm{O}}_2^{-}$ followed a dual-path catalytic mechanism combining direct ozonation and free radical chain reactions. Practical spray tests confirmed that coupling the Mn/ZSM-5 system with ozone oxidation flue gas denitrification achieved over 95% removal of liquid-phase $\mathrm{N}{\mathrm{O}}_2^{-}$ byproducts without compromising the synergistic removal efficiency of NO_x/SO₂. This study provided an efficient catalytic solution for industrial wastewater treatment and the resource utilization of flue gas denitrification byproducts. Full article

To improve the accuracy of heating load forecasting and effectively address the energy waste caused by supply–demand imbalances and uneven thermal distribution, this study innovatively proposes a hybrid prediction model incorporating seasonal adjustment strategies. The model establishes a dynamically adaptive forecasting framework through synergistic integration of the Sparrow Search Algorithm (SSA), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) network. Specifically, VMD is first employed to decompose the historical heating load data from Arizona State University’s Tempe campus into multiple stationary modal components, aiming to reduce data complexity and suppress noise interference. Subsequently, the SSA is utilized to optimize the hyperparameters of the LSTM network, with targeted adjustments made according to the seasonal characteristics of the heating load, enabling the identification of optimal configurations for each season. Comprehensive experimental evaluations demonstrate that the proposed model achieves the lowest values across three key performance metrics—Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE)—under various seasonal conditions. Notably, the MAPE values are reduced to 1.3824%, 0.9549%, 6.4018%, and 1.3272%, with average error reductions of 9.4873%, 3.8451%, 6.6545%, and 6.5712% compared to alternative models. These results strongly confirm the superior predictive accuracy and fitting capability of the proposed model, highlighting its potential to support energy allocation optimization in district heating systems. Full article

Background: The study evaluates the immunomodulatory potential of secukinumab (SECU) and honokiol (HONK) in a murine model of allergic asthma complicated by acute lung injury (ALI), with an emphasis on modulating key inflammatory pathways. The rationale is driven by the necessity to attenuate Th17-mediated cytokine cascades, wherein IL-17 plays a critical role, as well as to explore the adjunctive anti-inflammatory effects of HONK on Th1 cytokine production, including IL-6, TNF-α, and Th2 cytokines. Methods: Mice were sensitized and challenged with ovalbumin (OVA) and lipopolysaccharide (LPS) was administrated to exacerbate pulmonary pathology, followed by administration of SECU, HONK (98% purity, C₁₈H₁₈O₂), or their combination. Quantitative analyses incorporated OVA-specific IgE measurements, differential cell counts in bronchoalveolar lavage fluid (BALF), and extensive cytokine profiling in both BALF and lung tissue homogenates, utilizing precise immunoassays and histopathological scoring systems. Results: Both SECU and HONK, when used alone or in combination, display significant immunomodulatory effects in a murine model of allergic asthma concomitant with ALI. The combined therapy synergistically reduced pro-inflammatory mediators, notably Th1 cytokines, such as TNF-α and IL-6, as measured in both BALF and lung tissue homogenates. Conclusions: The combined therapy showed a synergistic attenuation of pro-inflammatory mediators, a reduction in goblet cell hyperplasia, and an overall improvement in lung histoarchitecture. While the data robustly support the merit of a combinatorial approach targeting multiple inflammatory mediators, the study acknowledges limitations in cytokine diffusion and the murine model’s translational fidelity, thereby underscoring the need for further research to optimize clinical protocols for severe respiratory inflammatory disorders. Full article

The evolution of Industry 4.0 and the emerging paradigm of Industry 5.0 have introduced disruptive technologies that are reshaping modern manufacturing environments. Among these, Digital Twin (DT) and Industrial Metaverse (IM) technologies are increasingly recognized for their potential to enhance Occupational Health and Safety (OHS). However, a comprehensive understanding of how these technologies integrate to support OHS in manufacturing remains limited. This study systematically explores the transformative role of DT and IM in creating immersive, intelligent, and human-centric safety ecosystems. Following the PRISMA guidelines, a Systematic Literature Review (SLR) of 75 peer-reviewed studies from the SCOPUS and Web of Science databases was conducted. The review identifies key enabling technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Internet of Things (IoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and Collaborative Robots (COBOTS), and highlights their applications in real-time monitoring, immersive safety training, and predictive hazard mitigation. A conceptual framework is proposed, illustrating a synergistic digital ecosystem that integrates predictive analytics, real-time monitoring, and immersive training to enhance the OHS. The findings highlight both the transformative benefits and the key adoption challenges of these technologies, including technical complexities, data security, privacy, ethical concerns, and organizational resistance. This study provides a foundational framework for future research and practical implementation in Industry 5.0. Full article

Research on ecosystem carbon sequestration services and ecological compensation is crucial for advancing carbon neutrality. As a public good, ecosystem carbon sequestration services inherently lead to externalities. Therefore, it is essential to consider externalities in the flow of sequestration services. However, few studies have examined intra- and inter-regional ecosystem carbon sequestration flows, making regional ecosystem carbon sequestration flows less comprehensive. Against this background, the research objectives of this paper are as follows. The flow of carbon sequestration services between Henan Province and out-of-province regions is studied. In addition, this study clarifies the beneficiary and supply areas of carbon sink services in Henan Province and the neighboring regions at the prefecture-level city scale to obtain a more systematic, comprehensive, and actual flow of carbon sequestration services for scientific and effective eco-compensation and to promote regional synergistic emission reductions. The research methodologies used in this paper are as follows. First, this study adopts a meta-coupling framework, designating Henan Province as the focal system, the Central Urban Agglomeration as the adjacent system, and eight surrounding provinces as remote systems. Regional carbon sequestration was assessed using net primary productivity (NEP), while carbon emissions were evaluated based on per capita carbon emissions and population density. A carbon balance analysis integrated carbon sequestration and emissions. Hotspot analysis identified areas of carbon sequestration service supply and associated benefits. Ecological radiation force formulas were used to quantify service flows, and compensation values were estimated considering the government’s payment capacity and willingness. A three-dimensional evaluation system—incorporating technology, talent, and fiscal capacity—was developed to propose a diversified ecological compensation scheme by comparing supply and beneficiary areas. By modeling the ecosystem carbon sequestration service flow, the main results of this paper are as follows: (1) Within Henan Province, Luoyang and Nanyang provided 521,300 tons and 515,600 tons of carbon sinks to eight cities (e.g., Jiaozuo, Zhengzhou, and Kaifeng), warranting an ecological compensation of CNY 262.817 million and CNY 263.259 million, respectively. (2) Henan exported 3.0739 million tons of carbon sinks to external provinces, corresponding to a compensation value of CNY 1756.079 million. Conversely, regions such as Changzhi, Xiangyang, and Jinzhong contributed 657,200 tons of carbon sinks to Henan, requiring a compensation of CNY 189.921 million. (3) Henan thus achieved a net ecological compensation of CNY 1566.158 million through carbon sink flows. (4) In addition to monetary compensation, beneficiary areas may also contribute through technology transfer, financial investment, and talent support. The findings support the following conclusions: (1) it is necessary to consider the externalities of ecosystem services, and (2) the meta-coupling framework enables a comprehensive assessment of carbon sequestration service flows, providing actionable insights for improving ecosystem governance in Henan Province and comparable regions. Full article

Addressing the stability control challenges of roadways with composite roofs in the No. 34 coal seam of Donghai Mine under high-strength mining conditions, this study employed integrated methodologies including laboratory experiments, numerical modeling, and field trials. It investigated the mechanical response characteristics of the composite roof and developed a synergistic control system, validated through industrial application. Key findings indicate significant differences in mechanical behavior and failure mechanisms between individual rock specimens and composite rock masses. A theoretical “elastic-plastic-fractured” zoning model for the composite roof was established based on the theory of surrounding rock deterioration, elucidating the mechanical mechanism where the cohesive strength of hard rock governs the load-bearing capacity of the outer shell, while the cohesive strength of soft rock controls plastic flow. The influence of in situ stress and support resistance on the evolution of the surrounding rock zone radii was quantitatively determined. The FLAC^3D strain-softening model accurately simulated the post-peak behavior of the surrounding rock. Analysis demonstrated specific inherent patterns in the magnitude, ratio, and orientation of principal stresses within the composite roof under mining influence. A high differential stress zone (σ₁/σ₃ = 6–7) formed within 20 m of the working face, accompanied by a deflection of the maximum principal stress direction by 53, triggering the expansion of a butterfly-shaped plastic zone. Based on these insights, we proposed and implemented a synergistic control system integrating high-pressure grouting, pre-stressed cables, and energy-absorbing bolts. Field tests demonstrated significant improvements: roof-to-floor convergence reduced by 48.4%, rib-to-rib convergence decreased by 39.3%, microseismic events declined by 61%, and the self-stabilization period of the surrounding rock shortened by 11%. Consequently, this research establishes a holistic “theoretical modeling-evolution diagnosis-synergistic control” solution chain, providing a validated theoretical foundation and engineering paradigm for composite roof support design. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article