Search Results (9,512)

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R² index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R² index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R² index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions. Full article

The construction industry is a major contributor to global carbon emissions, driving the need for sustainable solutions. Ultra-lightweight structures have emerged as an effective approach to reducing material usage and energy consumption. This study explores the potential of ultra-lightweight architectural systems through a learning-by-doing methodology, integrating innovative composite materials, PolRe, and knitting techniques to enhance tensegrity structures for sustainable, deployable, and efficient structural designs. Combining physical modeling, inspired by Frei Otto and Heinz Isler, with digital simulations using Kangaroo 2 and Python, this research employs form-finding and finite element analysis to validate structural performance. A 1:5 scale prototype was constructed using a manual knitting machine adapted from traditional knitting techniques. The integration of elastic meshes and rigid joints produced modular tensegrity systems that balance tension and compression, creating reversible, deployable, and material-efficient solutions. This study bridges conceptual aesthetics with structural efficiency, providing a template for sustainable, ultra-lightweight, textile-based structures. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

This study addresses the issue of inadequate remote sensing monitoring accuracy for photovoltaic (PV) arrays in complex geographical environments against the backdrop of rapid global expansion in PV power generation. Particularly concerning the complex spatial distribution characteristics formed by multiple types of PV power stations within China, this study overcomes traditional technical limitations that rely on very high-resolution (0.3–0.8 m) aerial imagery and manual annotation templates. Instead, it proposes an intelligent recognition method for PV arrays based on satellite remote sensing imagery. By enhancing the C3 feature extraction module of the YOLOv5 object detection model and innovatively introducing a weight-adaptive adjustment mechanism, the model’s ability to represent features of PV components across multiple scenarios is significantly improved. Experimental results demonstrate that the improved model achieves enhancements of 6.13% in recall, 3.06% in precision, 5% in F1 score, and 4.6% in mean Average Precision (mAP), respectively. Notably, the false detection rate in low-resolution (<5 m) panchromatic imagery is significantly reduced. Comparative analysis reveals that the optimized model reduces the error rate for small object detection in black-and-white imagery and complex scenarios by 19.8% compared to the baseline model. The technical solution proposed in this study provides a feasible technical pathway for constructing a dynamic monitoring system for large-scale PV facilities. Full article

Under intensifying global warming and extreme climate events, the climate adaptability of folk houses in Xinjiang’s arid regions faces critical challenges. However, existing studies predominantly focus on traditional folk houses under current climate conditions, neglecting modern material hybrids and long-term performance under future warming scenarios. This study develops a data-driven framework to assess and enhance building envelope performance across historical-to-future climate conditions (2007–2021 TMY data, 2024 observations, and 2050/2080 SSP3–7.0 projections) using the entropy-weighted TOPSIS method and NSGA-II algorithm. Analyzing rammed earth, brick–wood, and brick–concrete folk houses in Kashgar, Hotan, Kuqa, and Turpan, the optimization targets thermal discomfort hours (TDHs), heating energy consumption (HEC), and net present value (NPV). The results demonstrate optimized solutions achieve 30–60 year climate resilience, reducing HEC by 51.54–84.76% (43.02–125.78 kW·h/m²·a) compared to baseline buildings, TDH by 15–52.93% (301–1236 h) in arid Zone A and by 5.54–10.8% (208–352 h) in the extreme hot-arid Zone B (Turpan), and NPV values by CNY 31,000–85,000. Rammed earth constructions demonstrate superior performance in Zone A, while brick–concrete exhibits optimal extreme hot-arid adaptability, and brick–wood requires prioritized retrofitting. The findings advocate revising China’s design standards to address concurrent winter overcooling and summer overheating risks under future warming. This work establishes a climate-resilient optimization paradigm for arid-region folk houses, advancing energy efficiency and thermal comfort. Full article

A critical natural solution to combat global warming and reduce carbon emission is the forest carbon sink (FCS). Owing to variations in geographic location, policy formulation, and economic development, Chinese provinces exhibit significant disparities in forest carbon sink efficiency (FCSE). Therefore, evaluating and enhancing FCSE and optimizing resource allocation have emerged as pressing issues. This study develops a pioneering analytical framework for the systematic estimation and optimization of FCS resources. It measures FCSE, considering both dynamic and static aspects and adopting a spatial–temporal perspective, utilizing the Malmquist Index and Super Efficiency Slacks-Based Measure to analyze the primary factors influencing FCSE. The Autoregressive Integrated Moving Average method forecasts carbon sink goals for typical regions for the years 2030, 2045, and 2060. To effectively enhance FCSE and rationally optimize FCS resource allocation, this study constructs the Inverse Data Envelopment Analysis. The study’s findings indicate significant disparities in the extremes of the average FCSE across Chinese regions, with a mean value difference of 2.2188. Technological change is the primary driver of advancements in FCSE. To achieve the 2060 carbon sink goal, each input indicator requires a substantial increase. Drawing on insights into the FCS landscape, the study delineates regional disparities and offers a scientific foundation for policymakers to devise strategies and address sustainability concerns regarding FCS. Full article

Realizing the SDGs is a core issue of global development. In this regard, China has put forward a new quality productive forces development path with innovative thinking, providing systematic solutions for sustainable transformation through factor allocation optimization and whole-chain innovation drive. In the agricultural sector, industrial agglomeration is one of the factors affecting the development of new quality productive forces, with a spatial layout that can improve the efficiency of agricultural production and the effective utilization of resources. This paper investigates the impact of agricultural industry agglomeration on new quality productive forces by using the spatial Durbin model (SDM) to measure the relevant data of 16 prefecture-level cities in Shandong, China, from 2010 to 2022. The results show the following: (1) The spatial patterns of agricultural industry agglomeration and new quality productive forces in Shandong Province have been evolving, showing an obvious spatial correlation and “high in the south and low in the north” and “high in the north and low in the south” spatial patterns, respectively. (2) From a global perspective, industrial agglomeration has significant negative direct and indirect effects on the development of agricultural new quality productive forces, and this conclusion still holds after robustness testing. (3) From a local perspective, the impact of agricultural industry agglomeration on new quality productive forces is regionally heterogeneous. In the central economic zone, the impact is positive, while in the western and eastern economic zones, it is negative. This research provides a theoretical basis for optimizing the spatial layout of the agricultural industry and constructing a sustainable productivity system. Full article

Food safety is a significant global and local concern due to the threat of foodborne pathogens to public health and food security. Bacterial biofilms are communities of bacteria adhered to surfaces and represent a persistent contamination source in food environments. Their resistance to conventional antimicrobials exacerbates the challenge of eradication, driving the search for alternative strategies to control biofilms. Unconventional or “green” antimicrobial agents have emerged as promising solutions due to their sustainability and effectiveness. These agents include bacteriophages, phage-derived enzymes, plant extracts, and combinations of natural antimicrobials, which offer novel mechanisms for targeting biofilms. This approach aligns with the “One Health” concept, which underscores the interconnectedness of human, animal, and environmental health and advocates for integrated strategies to address public health challenges. Employing unconventional antimicrobial agents to manage bacterial biofilms can enhance food safety, protect public health, and reduce environmental impacts by decreasing reliance on conventional antimicrobials and mitigating antimicrobial resistance. This review explores the use of unconventional antimicrobials to combat foodborne pathogen biofilms, highlighting their mechanisms of action, antibiofilm activities, and the challenges associated with their application in food safety. By addressing these issues from a “One Health” perspective, we aim to demonstrate how such strategies can promote sustainable food safety, improve public health outcomes, and support environmental health, ultimately fostering a more integrated approach to combating foodborne pathogen biofilms. Full article

Silver nanoparticles (AgNPs) are used in a variety of different fields due to their excellent antimicrobial potential. Despite clear advantages, concerns about their toxicity have arisen, also concerning biogenic nanoparticles. Simultaneously, global healthcare is facing a problem of spreading antimicrobial resistance towards existing antibiotics. Using combined therapies involving AgNPs and antibiotics seems to be a promising solution to the above problems. The aim of this study was to evaluate the enhancement of the effectiveness of AgNPs, ciprofloxacin, and gentamicin against Staphylococcus aureus and Pseudomonas aeruginosa. The research involved the assessment of antimicrobial and antibiofilm-forming activities and the analysis of phospholipid and fatty acid profiles. Our results showed that combining the tested antimicrobials can enhance their activity against the tested bacterial strains. However, no effect was observed while mixing AgNPs with ciprofloxacin against P. aeruginosa. The most significant effect was obtained by combining 3.125 µg/mL of AgNPs with 0.125 µg/mL of gentamicin against S. aureus. It was also shown that the tested antimicrobials applied in combination exhibited an increased inhibitory activity towards bacterial biofilm formation by S. aureus. Lipidomic analysis revealed that under the influence of the tested antimicrobials, the properties of the cell membrane were altered in different ways depending on the bacterial strain. Full article

As global initiatives to reduce greenhouse gas emissions and combat climate change expand, electric vehicles (EVs) powered by fuel cells and lithium-ion batteries are gaining global recognition as solutions for sustainable transportation due to their high energy conversion efficiency. Considering the driving range limitations of battery electric vehicles (BEVs) and the low efficiency of internal combustion engines (ICEs), fuel cell hybrid vehicles offer a compelling alternative for long-distance, low-emission driving with less refuelling time. To facilitate their wider scale adoption, it is essential to understand their energy performance through models that consider external weather effects, driving styles, road gradients, and their simultaneous interaction. This paper presents a microlevel, multicriteria assessment framework to investigate the performance of BEVs, fuel cell electric vehicles (FCEVs), and hybrid electric vehicles (HEVs), with a focus on energy consumption, drive systems, and emissions. Simulation models were developed using MATLAB 2021a Simulink environment, thus enabling the integration of standardized driving cycles with real-world wind and terrain variations. The results are presented for various trip scenarios, employing quantitative and qualitative analysis methods to identify the most efficient vehicle configuration, also validated through the simulation of three commercial EVs. Predictive modelling approaches are utilized to estimate a vehicle’s performance under unexplored conditions. Results indicate that trip conditions have a significant impact on the performance of all three vehicles, with HEVs emerging as the most efficient and balanced option, followed by FCEVs, making them strong candidates compared with BEVs for broader adoption in the transition toward sustainable transportation. Full article

As the global demand for sustainable energy grows, hydrogen fuel has become a promising alternative to fossil fuels, particularly in the drone industry. Drones, known for their high mobility and low operational costs, are increasingly utilized in sectors like defense, agriculture, and logistics. However, traditional battery-powered drones are limited by flight duration and recharging times. Hydrogen fuel cells present a viable solution, with effective hydrogen pressure regulation being the key to ensuring their stable operation. This paper presents an innovative valve design for drones, developed to regulate the pressure reduction of high-pressure hydrogen gas from the storage tank to the fuel cell system. The valve incorporates a multi-stage pressure reduction mechanism, optimized to minimize the adverse effects of gas flow. Using a combination of experimental tests and numerical simulations, the study examines hydrogen flow characteristics at various valve openings, focusing on pressure, velocity distribution, and energy consumption. The results demonstrate that narrowing the valve opening improves pressure reduction, effectively controlling hydrogen flow and stabilizing pressure, thereby ensuring proper fuel cell operation. Further analysis reveals that smaller valve openings help reduce turbulence and energy loss, improving flow stability and system efficiency. This research provides valuable insights into hydrogen pressure regulation in drone fuel delivery systems, especially under extreme conditions such as high pressures and large pressure ratios. The findings offer both theoretical and practical guidance for optimizing hydrogen fuel delivery systems in fuel cell-powered drones, contributing to improve energy management and enhance performance in future drone applications. Full article

Near-global Digital Elevation Model (DEM) products generated through space-based radar techniques have become a basic data source for a variety range of applications. However, these DEM products often contain typical errors such as vegetation bias and topography-related errors, which impede their practical utility. Despite the development of numerous correction methods based on mathematical fitting and artificial neural networks over recent decades, reliably correcting large-scale spaceborne radar-derived DEMs remains an open challenge due to issues like underfitting or overfitting. This paper introduces a novel framework called Feature-Reinforced Ensemble Learning (FREEL) designed specifically for correcting space-based radar-derived DEMs. Within this FREEL framework, a feature derivation module and a feature reinforcement module are integrated to enhance the original input features. Subsequently, an adaptive weighting variant of the DeepForest algorithm is proposed to emphasize critical features and improve training robustness, even with limited training data. The Shuttle Radar Topographic Mission (SRTM) DEMs of Hunan Province, China, characterized by diverse surface terrain and vegetation coverage, were selected to evaluate the FREEL framework. The results indicate that the accuracy of the SRTM DEM corrected using the FREEL framework improved by 40%, surpassing several mathematical fitting and machine learning baseline algorithms by an average of 45% and 23%, respectively. This method provides a more robust solution for correcting near-global space-based radar-derived DEM products. Full article

Ensuring secure communication for mobile patients in e-healthcare requires an efficient and robust key distribution mechanism. This study introduces a novel hierarchical key distribution architecture inspired by federated learning (FL), enabling seamless authentication for patients moving across different healthcare centers. Unlike existing approaches, the proposed system allows a central healthcare authority to share global security parameters with subordinate units, which then combine these with their own local parameters to generate and distribute symmetric keys to mobile patients. This FL-inspired method ensures that patients only need to store a single key, significantly reducing storage overhead while maintaining security. The architecture was rigorously evaluated using SPAN-AVISPA for formal security verification and BAN logic for authentication protocol analysis. Performance metrics—including storage, computation, and communication costs—were assessed, demonstrating that the system minimizes the computational load and reduces the number of exchanged messages during authentication compared to traditional methods. By leveraging FL principles, the solution enhances scalability and efficiency, particularly in dynamic healthcare environments where patients frequently switch between facilities. This work bridges a critical gap in e-healthcare security, offering a lightweight, scalable, and secure key distribution framework tailored for mobile patient authentication. Full article

The decline in the young population in rural areas has led to a shortage of skilled labor in agriculture. While the use of technology and capital is often suggested as a solution, it may not be sufficient, especially with the aging rural population. The goal of this study was to examine the factors influencing young people’s decisions to stay in agriculture, and propose solutions. On the other hand, this study presents policy recommendations aimed at strengthening implementation tools for sustainable development and revitalizing global partnerships under SDG 17. Data were collected through surveys with 2398 young individuals aged 15–29 across 27 rural settlements in Turkey. A binary logit regression model was used to analyze the probability of young people remaining in agriculture. The results show that, similar to studies in developing economies, young men were more likely to stay in agriculture than young women. Additionally, having personal income or assets, as well as larger land and livestock holdings in the household, increased the likelihood of staying in agriculture. Conversely, migration from households and higher education levels decreased the probability. The study emphasizes the need for projects that improve the welfare of rural youth. Economic development alone is insufficient; policies integrating agricultural and social factors, including family dynamics, could be more effective in ensuring youth retention in agriculture and supporting sustainable agricultural production. Full article

Plastic recycling holds significant potential to reduce global carbon emissions. Despite advances in recycling technologies, challenges such as limited data availability, contamination in sorted materials, and the complexity of real-world material flows continue to hinder progress. This study addresses these issues by introducing a novel approach to plastic classification, leveraging simulated spectral data to reduce reliance on large datasets and improve classification accuracy. Using near-infrared spectroscopy and deep learning models, the framework integrates data augmentation techniques and spectral simulation to augment datasets with synthetic spectra based on a data sample of 25 plastic granules. The proposed classification framework achieves excellent recall and robust balanced accuracy for both binary and multi-target polymer classification with minimal data input (only 50 spectra per class). Thus, the measurement effort is drastically reduced while maintaining an equally high model accuracy. The model significantly outperforms conventional unsupervised approaches. By overcoming the limitations of supervised learning models, the proposed framework provides a scalable and efficient solution for plastics recycling. Full article