Search Results (1,437)

Warm-mixed asphalt technology can significantly reduce the heating temperatures required for asphalt pavement construction, which makes it one of the crucial technical approaches in road engineering for achieving energy conservation and emission reduction, and carbon neutrality. Existing research often focuses on designing asphalt materials to ensure optimal service performance, but insufficient attention has been paid to the specific extent of reduction in asphalt fume emissions. However, the latter is a critical factor that cannot be neglected when constructing asphalt pavements in environmentally sensitive regions. Considering the environmental factor, this study systematically explores the comprehensive influence of different warm-mixed additive dosages on the viscoelastic properties and VOC emissions of warm-mixed SBS asphalt binder using rotational viscosity, bending beam rheometer (BBR), dynamic shear rheometer (DSR), and gas chromatography–mass spectrometry (GC-MS) test methods. The findings show that the application of warm-mixed additive does not compromise the comprehensive properties of SBS asphalt binder, but partially enhances its service performance instead. Due to the significant reduction in heating temperature, asphalt VOC emissions are indirectly reduced. Although the warm-mixed additive possesses a certain degree of volatility, its application still shows a significant trend toward emission reduction. Despite 0.4% being a relatively economical dosage of warm-mixed additive, a slight increase to 0.5% can achieve more pronounced environmental benefits in VOC emission reduction while maintaining comprehensive service performance that meets specification requirements. The findings can provide new insights for the application and decision-making of warm-mixed asphalt technology in environmentally sensitive regions. Full article

Under the “Dual Carbon” strategy, high-penetration integration of distributed generators (DG) into distribution networks has triggered bidirectional power flow and reactive power-voltage violations. This phenomenon undermines the accuracy guarantee of conventional relaxation models (represented by second-order cone programming, SOCP), causing solutions to deviate from the AC power flow feasible region. Notably, ensuring solution feasibility becomes particularly crucial in engineering practice. To address this problem, this paper proposes a collaborative optimization framework integrating convex inner approximation (CIA) theory and a solution recovery algorithm. First, a system relaxation model is constructed using CIA, which strictly enforces ACPF constraints while preserving the computational efficiency of convex optimization. Second, aiming at the conservatism drawback introduced by the CIA method, an admissible region correction strategy based on Stochastic Gradient Descent is designed to narrow the dual gap of the solution. Furthermore, a multi-objective optimization framework is established, incorporating voltage security, operational economy, and renewable energy accommodation rate. Finally, simulations on the IEEE 33/69/118-bus systems demonstrate that the proposed method outperforms the traditional SOCP approach in the 24 h sequential optimization, reducing voltage deviation by 22.6%, power loss by 24.7%, and solution time by 45.4%. Compared with the CIA method, it improves the DG utilization rate by 30.5%. The proposed method exhibits superior generality compared to conventional approaches. Within the upper limit range of network penetration (approximately 60%), it addresses the issue of conservative power output of DG, thereby effectively promoting the utilization of renewable energy. Full article

Aquaponic systems are the integration of aquaculture and hydroponic systems to enhance productivity, reduce land use, and improve sustainability. This review focused on commonly used life cycle assessment (LCA) methodologies, system boundaries, and functional units used in aquaponics, standard impact categories, and identified hotspots. The scope is worldwide and encompasses a variety of aquaponic designs, fish species, and crops, illustrating the diversity of the systems examined. The analysis indicates that aquaponics provides the considerable environmental advantages of decreased fertilizer consumption and water conservation in comparison with aquaculture and hydroponic system. However, aquaponics systems are characterized by high energy consumption and may produce greater greenhouse gas (GHG) emissions compared to traditional farming methods when reliant on fossil fuel energy sources. Studies show that fish feed production, system infrastructure, and electricity usage for pumps, lights, heating, and other controls are hotspots. Harmonized comparisons of previous studies show methodological differences, especially in fish–plant co-production. Despite these variations, most believe that energy efficiency, renewable energy, feed optimization, and waste reuse may make aquaponics more sustainable. The study recommends the inclusion of broader environmental and social impacts. Also, future focus might be on making a standard functional unit or specifying system boundaries which might provide different accurate outcomes. Full article

During the trial mining process, ground horizontal pipes are prone to generating hydrates due to pressure and temperature changes, leading to ice blockage. Hydrate inhibitors are usually added on-site to prevent freezing blockage. However, existing addition methods have limitations, including poor real-time performance, insufficient accuracy in the addition amount, and dependence on manual adjustment. In view of this, this paper aims to develop models to predict the throttling pressure and temperature for horizontal ground pipes, and to indicate the amount of ethylene glycol needed to prevent freezing blockage, thereby laying the foundation for accurate, real-time prediction of fluid pressure and temperature and for controlling the addition amount. By integrating data-driven technologies and mechanism models, this study developed intelligent prediction systems for ground horizontal pipe throttling pressure and temperature, and for suppression of freeze-blocking ethylene glycol addition. First, a three-phase throttling mechanism model for oil, gas, and water is established using the energy conservation equation to accurately predict the pressure and temperature at the throttling points along the process. At the same time, HYSYS software is used to simulate various operating conditions and to fit the ethylene glycol addition amount prediction model. Finally, edge computing equipment is integrated to enable real-time data collection, prediction, and dynamic adjustment and optimization. The field measurement data of Well A showed that the model’s prediction error of pressure and temperature before and after throttling is less than 6%, and the prediction error of the ethylene glycol addition amount is less than 5%, which provides key technical support for safe and efficient operation of the trial mining process as well as for cost reduction and efficiency improvement. Full article

With China’s accelerated urbanization, road and tunnel lighting demand and its electricity consumption have grown significantly, making energy conservation, and carbon reduction urgent. GB 37478, the core standard for road and tunnel LED luminaires, is crucial for promoting high-efficiency products and the lighting industry’s energy efficiency transformation. This study focuses on its 2019 and 2025 editions, using a bottom-up model, product Stock model, and carbon reduction potential method to analyze the standard’s energy conservation and carbon reduction potential during 2021–2030, alongside international energy efficiency comparisons. The results show that by 2030, GB 37478 will achieve 162 TWh cumulative electricity savings, over 90 million tons of CO₂ reduction. The standard has optimized the market structure: Grade 1 energy efficiency products rose from 5% (2019) to over 60% (2025). China’s energy efficiency requirements for such LED luminaires are internationally advanced. Replacing high-pressure sodium lamps with LEDs (50–60% savings) outperforms LED upgrades (10–20%). Future standards should extend from product to system level, integrating safety, health, and intelligence. This study provides a scientific basis for quantifying the standard’s dual-carbon contribution and references for industry policies. Full article

With the rapid advancement of artificial intelligence (AI) technology, training deep neural networks has become a core computational task that consumes significant energy in data centers. Researchers often employ various methods to estimate the energy usage of data center clusters or servers to enhance energy management and conservation efforts. However, accurately predicting the energy consumption and carbon footprint of a specific AI task throughout its entire lifecycle before execution remains challenging. In this paper, we explore the energy consumption characteristics of AI model training tasks and propose a simple yet effective method for predicting neural network training energy consumption. This approach leverages training task metadata and applies genetic programming-based symbolic regression to forecast energy consumption prior to executing training tasks, distinguishing it from time series forecasting of data center energy consumption. We have developed an AI training energy consumption environment using the A800 GPU and models from the ResNet{18, 34, 50, 101}, VGG16, MobileNet, ViT, and BERT families to collect data for experimentation and analysis. The experimental analysis of energy consumption reveals that the consumption curve exhibits waveform characteristics resembling square waves, with distinct peaks and valleys. The prediction experiments demonstrate that the proposed method performs well, achieving mean relative errors (MRE) of 2.67% for valley energy, 8.42% for valley duration, 5.16% for peak power, and 3.64% for peak duration. Our findings indicate that, within a specific data center, the energy consumption of AI training tasks follows a predictable pattern. Furthermore, our proposed method enables accurate prediction and calculation of power load before model training begins, without requiring extensive historical energy consumption data. This capability facilitates optimized energy-saving scheduling in data centers in advance, thereby advancing the vision of green AI. Full article

In response to the “carbon peak and carbon neutrality” strategy, industrial energy conservation has become increasingly important. Interior Permanent Magnet Synchronous Motors (IPMSMs) exhibit significant potential for efficient flux-weakening control due to their asymmetric rotor reluctance. However, conventional control strategies often cause instability during transitions across speed zones. This paper proposes a novel adaptive fuzzy-based smooth transition strategy to address this issue. First, a composite control framework integrating Maximum Torque per Ampere (MTPA) and leading-angle control is established to enhance flux-weakening capability. Then, within this framework, adaptive fuzzy controllers are designed for different weakening zones, incorporating a Lyapunov-based parameter adaptation mechanism for real-time compensation. Simulation results demonstrate that the proposed strategy achieves smooth switching across the entire speed range of IPMSMs. Quantitatively, it reduces speed overshoot by 5–15%, suppresses torque ripple by over 10%, and virtually eliminates switching current pikes compared to conventional methods, thereby significantly improving system dynamic performance and operational reliability. Full article

We present a linearly implicit and structure-preserving scheme to solve the space-fractional Ginzburg–Landau–Schrödinger equation. The fully discrete scheme is obtained by combining the modified leap-frog method in the temporal direction and the finite difference methods in the spatial direction. It is shown that the scheme can be unconditionally energy-stable. In particular, the equation becomes the space-fractional Schrödinger equation. Then, the scheme can keep both the discrete mass and energy conserved. Moreover, convergence of the scheme is obtained. Numerical experiments are performed to confirm the theoretical results. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

This study addresses the challenge of rapid identification and assessment of localized damage to building envelopes under resource-constrained conditions—specifically, the absence of specialized inspection equipment—with a particular focus on the detrimental effects of such damage on thermal performance and energy efficiency. An efficient detection methodology tailored to small-scale maintenance scenarios is proposed, leveraging the YOLOv11 object detection architecture to develop an intelligent system capable of recognizing common envelope defects in contemporary residential buildings, including cracks, spalling, and sealant failure. The system prioritizes the detection of anomalies that may induce thermal bridging, reduced airtightness, or insulation degradation. Defects are classified according to severity and their potential impact on thermal behavior, enabling a graded, integrated repair strategy that holistically balances structural safety, thermal restoration, and façade aesthetics. By explicitly incorporating energy performance recovery as a core objective, the proposed approach not only enhances the automation of spatial data processing but also actively supports the green operation and low-carbon retrofitting of existing urban building stock. Characterized by low cost, high efficiency, and ease of deployment, this method offers a practical and scalable technical pathway for the intelligent diagnosis of thermal anomalies and the enhancement of building energy performance. It aligns with the principles of high-quality architectural development and sustainable building governance, while concretely advancing operational energy reduction in the built environment and contributing meaningfully to energy conservation goals. Full article

Urban rivers are essential resources for human societies; however, their degradation poses serious public health, economic, and environmental risks. Conventional physical remediation methods can partially mitigate pollution by targeting specific contaminants, but they are often limited in scope, lack long-term sustainability, and fail to restore ecological functions. In contrast, biotechnological approaches integrated with ecological engineering offer sustainable and nature-based solutions for river depollution, conservation, and revitalization. Although these strategies are supported by a solid theoretical framework and successful applications in other aquatic systems, their large-scale implementation in urban rivers has only recently begun to gain momentum. This review critically examines strategies for the revitalization of polluted urban rivers, progressing from conventional remediation techniques to advanced biotechnological interventions. It highlights real-world applications, evaluates their advantages and limitations, and discusses policy frameworks and management strategies required to promote the broader adoption of biotechnological solutions for sustainable urban river restoration. The goal is to demonstrate the transformative potential of integrated biotechnological, eco-engineering, and data-driven approaches—particularly microbial, phytoplankton-based, and biofilm systems—to reduce energy demand and carbon emissions in urban river restoration while highlighting the need for scalable designs, adaptive management, and supportive regulatory frameworks to enable their large-scale implementation. Full article

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and supply capacity under coupled meteorological and air pressure effects, limiting its practical promotion. This study focuses on a solar-coupled compressed-air energy storage regulated sprinkler irrigation system (CAES-SPSI). Integrating experimental and theoretical methods, it establishes dynamic flow models for three DC diaphragm pumps considering combined PV output and outlet back pressure, introduces pressure loss and drop coefficients to construct a nozzle pressure dynamic model via calibration and iteration, and conducts a 1-hectare corn field case study. The results indicate the following: pump flow increases with PV power and decreases with outlet pressure (model deviation < 9.24%); nozzle pressure in pulse spraying shows logarithmic decline; CAES-SPSI operates 10 h/d, with hourly water-lifting capacity of 0.317–1.01 m³/h and daily cumulation of 6.71 m³; and the low-intensity and long-duration mode extends irrigation time, maintaining total volume and optimal soil moisture. This study innovatively incorporates dynamic air pressure potential energy into meteorological-PV coupling analysis, providing a universal method for quantifying pump flow changes, clarifying CAES-SPSI’s water–energy coupling mechanism, and offering a design basis for its agricultural application feasibility. Full article

The separate-layer gas injection technology is a key means to improve the effect of refined gas injection development. Currently, the measurement and adjustment of separate injection wells primarily rely on manual experience and automatic measurement via instrument traversal, resulting in a long duration, low efficiency, and low qualification rate for injection allocation across multi-layer intervals. Given the different CO₂-containing natural gas injection rates across different intervals, this paper establishes a coupled flow model of a separate-layer gas injection wellbore–gas distributor–formation based on the energy and mass conservation equations for wellbore pipe flow, and develops a solution method for determining gas nozzle sizes across multi-layer intervals. Based on the maximum allowable gas nozzle size, an optimization method for multi-layer collaborative allocation of separate injection wells is established, with minimum wellhead injection pressure and layered injection allocation as the optimization objectives, and the opening of gas distributors for each layer as the optimization variable. Taking Well XXX as an example, the optimization process of allocation schemes under different gas allocation requirements is simulated. The research shows that the model and method proposed in this paper have high calculation accuracy, and the formulated allocation schemes have strong adaptability and minor injection allocation errors, providing a scientific decision-making method for formulating refined allocation schemes for separate-layer gas injection wells, with significant theoretical and practical value for promoting the refined development of oilfields. Full article

Under the “dual carbon” goals, enhancing the energy supply for communication base stations is crucial for energy conservation and emission reduction. An individual base station with wind/photovoltaic (PV)/storage system exhibits limited scalability, resulting in poor economy and reliability. To address this, a collaborative power supply scheme for communication base station group is proposed. This paper establishes a capacity optimization configuration model for such integrated system and introduces a hybrid solution methodology combining random scenario analysis, Nondominated Sorting Genetic Algorithm II (NSGA-II), and Generalized Power Mean (GPM). Typical scenarios are solved using NSGA-II to generate a candidate solution set, which is then refined under operational constraints. The GPM method is applied to determine the final configuration by accounting for attribute correlations. A case study on a Chinese base station group, considering uncertainties in renewable generation, demonstrates the feasibility and effectiveness of the proposed approach. Full article

Background/Objectives. Antibiotic resistance is an escalating global health concern, highlighting the need for innovative antibacterial strategies beyond traditional drugs. GroEL, a highly conserved bacterial chaperonin essential for protein folding and stress tolerance, represents a promising but underexplored therapeutic target. This study aimed to identify short peptides capable of binding GroEL monomers and potentially altering their function, with the long-term goal of disrupting bacterial survival mechanisms. Methods. A phage display screening of a 12-mer peptide library was performed against purified GroEL monomers, yielding five candidate peptides (G1–G5). Their interactions with GroEL were analyzed through molecular docking and molecular dynamics simulations using three-dimensional GroEL structures (1MNF, 1XCK, 8S32). Stability of binding and interaction profiles were assessed through molecular dynamics-based analyses and MM/GBSA free energy calculations. Results. Peptides G4 and G5 displayed the most stable and energetically favorable interactions, with G4–8S32 showing the strongest binding (−116.68 kcal/mol). These peptides localized near inter-subunit interfaces, suggesting potential interference with GroEL oligomerization or allosteric transitions, which are critical for its biological function. Conclusions. Our findings demonstrate that short peptides can stably bind GroEL and potentially modulate its activity. Peptides G4 and G5 represent at our knowledge the first promising scaffolds for developing a novel class of peptide-based antibacterial agents targeting conserved chaperonin systems. This work introduces a new avenue that warrants further experimental validation. Full article