Search Results (287)

The organic Rankine cycle (ORC) is a key technology for the recovery of low-grade waste heat, but its efficient and stable operation is challenged by complex kinetic coupling. This paper proposes a model partitioning strategy based on gap measurement to construct a high-fidelity ORC system model and combines the setting of observer decoupling and multi-model switching strategies to reduce the coupling impact and enhance adaptability. For control optimization, the reinforcement learning method of deep deterministic Policy Gradient (DDPG) is adopted to break through the limitations of the traditional discrete action space and achieve precise optimization in the continuous space. The proposed DDPG-MGPC (Hybrid Model Predictive Control) framework significantly enhances robustness and adaptability through the synergy of reinforcement learning and model prediction. Simulation shows that, compared with the existing hybrid reinforcement learning and MPC methods, DDPG-MGPC has better tracking performance and anti-interference ability under dynamic working conditions, providing a more efficient solution for the practical application of ORC. Full article

In the stage of high-quality development, the functional coordination between transportation systems and territorial space is a key issue for improving urban spatial efficiency. This paper breaks through the traditional volume-to-capacity ratio analysis paradigm and innovatively integrates the “production-living-ecological space” theory. By introducing an improved accessibility evaluation model and developing a coordination measurement algorithm, a three-dimensional evaluation mechanism covering development potential assessment, service efficiency diagnosis, and resource allocation optimization is established. Empirical research indicates that the improved accessibility indicators can precisely identify the transportation location value of regional functional cores, while the composite coordination indicators can deconstruct the spatiotemporal matching characteristics of “transportation facilities—spatial functions,” providing a dual decision-making basis for the redevelopment of existing space. This measurement system innovatively realizes the integration of planning transmission mechanisms with multi-scale application scenarios, guiding both overall spatial planning and urban renewal area re-optimization. The methodology, applied to the urban villages of Guangzhou, can significantly increase land utilization intensity and value. The research results offer a technical tool for cross-scale collaboration in land space planning reforms and provide theoretical innovations and practical guidance for the value reconstruction of existing spaces under the context of new urbanization. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

Against the backdrop of the profound restructuring in global climate governance, China’s energy management system is undergoing a comprehensive transition from dual energy consumption control to dual carbon emissions control. This policy shift fundamentally alters the underlying logic of energy-focused regulation and inevitably impacts the economy–energy–environment (3E) system. This study innovatively constructs a “Triangular Trinity” theoretical framework integrating internal, intermediate, and external triangular couplings, as well as providing a granular analysis of their transmission relationships and feedback mechanisms. Using Guangdong Province as a case study, this study takes the dual control emissions policy within the external triangle as an entry point to research the restructuring logic of dual carbon emissions control for the coupling coordination mechanisms of the 3E system. The key findings are as follows: (1) Policy efficacy evolution: During 2005–2016, dual energy consumption control significantly improved energy conservation and emissions reduction, elevating Guangdong’s 3E coupling coordination. Post 2017, however, its singular focus on total energy consumption revealed limitations, causing a decline in 3E coordination. Dual carbon emissions control demonstrably enhances 3E systemic synergy. (2) Decoupling dynamics: Dual carbon emissions control accelerates economic–carbon emission decoupling, while slowing economic–energy consumption decoupling. This created an elasticity space of 5.092 million tons of standard coal equivalent (sce) and reduced carbon emissions by 26.43 million tons, enabling high-quality economic development. (3) Mechanism reconstruction: By leveraging external triangular elements (energy-saving technologies and market mechanisms) to act on the energy subsystem, dual carbon emissions control leads to optimal solutions to the “Energy Trilemma”. This drives the systematic restructuring of the sustainability triangle, achieving high-order 3E coupling coordination. The Triangular Trinity framework constructed by us in the paper is an innovative attempt in relation to the theory of energy transition, providing a referenceable methodology for resolving the contradictions of the 3E system. The research results can provide theoretical support and practical reference for the low-carbon energy transition of provinces and cities with similar energy structures. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article

Predicting water inrush in coal mines faces significant challenges due to limited data, model generalization, and a lack of interpretability. Current approaches often neglect the inherent geometrical symmetries and structured patterns within the complex hydrological parameter space, rely on local parameter optimization, and struggle with interpretability, leading to insufficient predictive accuracy and engineering applicability under complex geological conditions. This study addresses these limitations by integrating Gaussian mixture modeling (GMM), manifold learning, and data augmentation to effectively capture multimodal hydrological data distributions and reveal their intrinsic symmetrical configurations and manifold structures, thereby reducing feature dimensionality. We then apply a whale optimization algorithm (WOA)-enhanced XGBoost model to forecast water inrush probabilities. Our model achieved an R² of 0.92, demonstrating a greater than 60% error reduction across various metrics. Validation at the Yangcheng Coal Mine confirmed that this balanced approach significantly enhances predictive accuracy, interpretability, and cross-scenario applicability. The synergy between high accuracy and transparency provides decision makers with reliable risk insights, enabling bidirectional validation with geological mechanisms and supporting the implementation of targeted, proactive safety measures. Full article

Amid global efforts to balance sustainable development and food security, ecosystem service value (ESV), a critical bridge between natural systems and human well-being, has gained increasing importance. This study explores the spatiotemporal dynamics and driving factors of land use changes and ESV from a food security perspective, aiming to inform synergies between ecological protection and food production for regional sustainability. Using Guangdong Province as a case study, we analyze ESV patterns and spatial correlations from 2005 to 2023 based on three-phase land use and socioeconomic datasets. Key findings: I. Forestland and cropland dominate Guangdong’s land use, which is marked by the expansion of construction land and the shrinking of agricultural and forest areas. II. Overall ESV declined slightly: northern ecological zones remained stable, while eastern/western regions saw mild decreases, with cropland loss threatening grain self-sufficiency. III. Irrigation scale, forestry output, and fertilizer use exhibited strong interactive effects on ESV, whereas urban hierarchy influenced ESV independently. IV. ESV showed significant positive spatial autocorrelation, with stable agglomeration patterns across the province. The research provides policy insights for optimizing cropland protection and enhancing coordination between food production spaces and ecosystem services, while offering theoretical support for land use regulation and agricultural resilience in addressing regional food security challenges. Full article

This study addresses the efficiency and cost challenges of hydrogen evolution reaction (HER) catalysts in the context of carbon neutrality strategies by employing a synergistic approach that combines cation vacancy anchoring and transition metal doping on two-dimensional (2D) MXenes. Using an in situ LiF/HCl etching process, the aluminum layers in Ti₃AlC₂ were precisely removed, resulting in a few-layer Ti₃C₂T_x MXene with an increased interlayer spacing of 12.3 Å. Doping with the transition metals Fe, Co, Ni, and Cu demonstrated that Fe@Ti₃C₂ provided the optimal HER performance, characterized by an overpotential (η₁₀) of 81 mV at 10 mA cm⁻², a low Tafel slope of 33.03 mV dec⁻¹, and the lowest charge transfer resistance (R_ct = 5.6 Ω cm²). Mechanistic investigations revealed that Fe’s 3d⁶ electrons induce an upward shift in the d-band center of MXene, improving hydrogen adsorption free energy and reducing lattice distortion. This research lays a solid foundation for the design of non-precious metal catalysts using MXenes and highlights future avenues in bimetallic synergy and scalability. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

How video-based methodologies and advanced computer vision algorithms can facilitate the development of antimicrobial peptide (AMP) generation models should be further reviewed, structural and functional patterns should be elucidated, and the generative power of in silico pipelines should be enhanced. AMPs have drawn significant interest as promising therapeutic agents because of their broad-spectrum efficacy, low resistance profile, and membrane-disrupting mechanisms. However, traditional discovery methods are hindered by high costs, lengthy synthesis processes, and difficulty in accessing the extensive chemical space involved in AMP research. Recent advances in artificial intelligence—especially machine learning (ML), deep learning (DL), and pattern recognition—offer game-changing opportunities to accelerate AMP design and validation. By integrating video analysis with computational modelling, researchers can visualise and quantify AMP–microbe interactions at unprecedented levels of detail, thereby informing both experimental design and the refinement of predictive algorithms. This review provides a comprehensive overview of these emerging techniques, highlights major breakthroughs, addresses critical challenges, and ultimately emphasises the powerful synergy between video-driven pattern recognition, AI-based modelling, and experimental validation in the pursuit of next-generation antimicrobial strategies. Full article

Effective soil salinity monitoring is crucial for sustainable land management in arid regions. Most current studies face limitations by relying solely on single-source data. This study presents a novel three-dimensional (3D) optical-radar feature space model combining Gaofen-3 polarimetric synthetic aperture radar (SAR) and Sentinel-2 multispectral data for China’s Yutian Oasis. The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). Based on the interactions of these three optimal features within the 3D feature space, we constructed the Optical-Radar Salinity Inversion Model (ORSIM). Subsequent validation using measured soil electrical conductivity (EC) data (May–June 2023) demonstrated strong model performance, with ORSIM achieving R² = 0.75 and RMSE = 7.57 dS/m. Spatial analysis revealed distinct salinity distribution patterns: (1) Mildly salinized areas clustered in the central oasis region, and (2) severely salinized zones predominated in northern low-lying margins. This spatial heterogeneity strongly correlated with local topography-higher elevation (south) to desert depression (north) gradient. The 3D feature space approach advances soil salinity monitoring by overcoming traditional 2D limitations while providing an accurate, transferable framework for arid ecosystem management. Furthermore, this study significantly expands the application potential of SAR data in soil salinization research. Full article

Urban mobility structure detection and functional distribution learning are significant for urban planning and management. However, existing methods have limitations in handling complex urban data and capturing global spatial structure features. To deal with these challenges, we proposed a multi-scale feature-aware urban mobility structure embedding method based on contrastive learning. First, we designed a multi-scale contrastive learning strategy to effectively learn local human activity features and global spatial structure features, determine the community affiliation of regions, and generate regional embedding vectors. Next, we introduced a correlation matrix to encode the functional synergy and competition of Point of Interests (POIs) and construct the complex correlation between urban mobility structure and urban functional distribution to evaluate the quality of regional embedding vectors. Experiments in Haikou City show that the proposed method can accurately detect the urban mobility structure and functional distribution. The analysis reveals that the central urban area of Haikou exhibits concentrated functions and significant traffic tidal effects, while the suburban areas have relatively weaker functions, with residents displaying a high level of dependence on the central area. Therefore, urban planning needs to optimize the functional layout, improve the functions of the suburbs, and promote the balance of urban space. Full article

Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly regarding interactions with meteorological, topographical, and socio-economic factors. According to the urban-suburban air temperature difference, this study classified the city into non-, weak, and strong CUHI regions. We integrated causal inference, machine learning and a geographical detector (Geodetector) to model and interpret PCI dynamics across CUHI gradients. The results reveal that surrounding impervious surface coverage is a universal driver of PCI by enhancing thermal contrast at park boundaries. However, the dominant drivers of PCI varied significantly across CUHI gradients. In non-CUHI regions, surrounding imperviousness dominated PCI and exhibited bilaterally enhanced interaction with intra-park patch density. Weak CUHI regions relied on intra-park green coverage with nonlinear synergies between water body proportion and park area. Strong CUHI regions involved systemic urban fabric influences mediated by surrounding imperviousness, evidenced by a validated causal network. Crucially, causal inference reduces model complexity by decreasing predictor counts by 79%, 25% and 71% in non-, weak and strong CUHI regions, respectively, while maintaining comparable accuracy to full-factor models. This outcome demonstrates the efficacy of causal inference in eliminating collinear metrics and spurious correlations from traditional feature selection, ensuring retained predictors reside within causal pathways and support process-based interpretability. Our study highlights the need for context-adaptive cooling strategies and underscores the value of integrating causal–statistical approaches. This framework provides actionable insights for designing climate-resilient blue–green spaces, advancing urban sustainability goals. Future research should prioritize translating causal diagnostics into scalable strategies for sustainable urban planning. Full article

The room for reducing carbon emissions and improving low-carbon practices in the building industry is significant. In this study, a bibliometric analysis shows that technology is the primary mechanism adopted for driving carbon reduction in the existing practices building industry, which is conducted by using the CiteSpace 6.2.R4 tool. It is considered that there is a limitation in promoting further low-carbon practices by technological dominance without a proper management paradigm. This paper, therefore, aims to search for a new management paradigm in order to help further reduce emissions in the building industry. This study adopts an innovative synergy theory to explain the mechanism by which the efforts of all management dimensions can be synergized to promote low-carbon practices in the building sector. Consequently, the outcome of this paper is the introduction of a total low-carbon management (TLCM) paradigm. Synergy theory supports our assertion that a joint force can be formed within the building industry system to drive TLCM practice, as all building-related elements (government departments, building organizations, building personnel, building activities, and building processes) in the system will have to follow the government’s actions towards low-carbon practices. The TLCM paradigm is integrated by five components: whole regulation, whole industry, whole enterprise, whole staff, and whole process. The new paradigm should be promoted to replace the existing technology-dominated paradigm in order to achieve low-carbon practices in the building industry. The TLCM paradigm will guide low-carbon management decisions and practices across all phases of the building project’s lifecycle, together with integrating quality and risk management. Full article

Underground spaces offer significant potential for sustainable urban development, particularly in cold climate regions where surface thermal fluctuations are extreme. However, optimizing the wind–heat environmental performance of such spaces remains insufficiently explored, especially in relation to spatial morphology. This study addresses this gap by investigating how underground spatial configurations influence thermal comfort and ventilation efficiency. Six representative spatial prototypes—fully enclosed, single-side open, double-side open, central atrium, wind tower, and earth kiln—were constructed based on common underground design typologies. Computational fluid dynamics (CFD) simulations were conducted to evaluate airflow patterns and thermal responses under winter and summer conditions, incorporating relevant geotechnical properties into the boundary setup. The results indicate that deeper burial depths enhance thermal stability, while central atrium and wind tower prototypes offer the most balanced performance in both ventilation and heat regulation. These findings provide valuable design guidance for climate-responsive underground developments and contribute to the interdisciplinary integration of building physics, spatial design, and geotechnical engineering. Full article