Search Results (53)

Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade. Full article

To address the high cost of cobalt in rare-earth hydrogen storage alloys, this study developed cost-effective low-cobalt and cobalt-free AB₅-type alloys. The results demonstrate that all synthesized alloys displayed a single-phase LaNi₅ structure possessing a homogeneous elemental distribution. Low-cobalt (La, Ce) (Ni, Co, Mn, Al)₅ alloy 4SC and cobalt-free (La, Ce) (Ni, Mn, Al)₅ alloy 7D exhibited similarly excellent electrochemical performance, including high discharge capacity, long cycle life, and superior high-rate discharge (HRD) capability. In addition, the kinetic test results show that the exchange current densities of these two alloys were quite similar, measuring 302.97 mA g⁻¹ and 317.70 mA g⁻¹, respectively. However, the hydrogen diffusion coefficient of 7D was significantly higher than that of 4SC, reaching 9.45 × 10⁻¹⁰ cm² s⁻¹, while that of 4SC was only 5.88 × 10⁻¹⁰ cm²/s. This work establishes a theoretical foundation for industrial-scale and cost-effective AB₅-type hydrogen storage alloys, offering significant commercial potential. Full article

The automotive industry is a key pillar of many national economies, and automotive body manufacturing is among the most complex production processes. In the automotive body manufacturing process, quality control and safety assurance are of paramount importance, directly influencing the overall safety performance, structural reliability, and comfort of vehicles. Therefore, it is crucial to analyze the primary factors that influence quality and safety during the car body manufacturing process. The study first focuses on four key processes of car body manufacturing—stamping, welding, painting, and assembly—using the man, machine, material, method, environment (4M1E) framework to analyze the factors affecting quality and safety. Subsequently, a quality and safety early-warning indicator system is established for the automotive body manufacturing process, followed by a comprehensive analysis of the constructed system. To address the issue of subjectivity in traditional technique for order of preference by similarity to an ideal solution (TOPSIS) evaluation methods, this paper employs the coefficient of variation method for objective analysis of criterion-level indicators, the trapezoidal fuzzy number method for subjective analysis of criterion-level indicators, and establishes a model for optimizing target weight that balances subjective and objective approaches. Furthermore, a relative entropy-based method is applied to comprehensively evaluate criterion-level indicators. This approach reduces the information loss associated with separate weighting schemes and overcomes a known limitation of traditional TOPSIS—its inability to distinguish alternatives that lie equidistant from ideal solutions. Finally, an evaluation model for quality and safety influencing factors in body manufacturing is developed and validated through a case study, demonstrating its feasibility. The results show that the proposed model can effectively identify the key quality and safety influencing factors in the automobile body manufacturing process, guarantee quality control and safety assurance in the body manufacturing process, and thus ensure that the automobile production process meets the quality and safety requirements. Full article

Polyurethane foams are frequently used to provide thermal insulation. Thanks to the blowing agents used during their synthesis, pores are created in the structure and thermal insulation is achieved through these pores. In this study, five different insulating polyurethane foam samples containing water and cyclohexane blowing agents were synthesized. Pore stabilities and their effects on pore neighboring were analyzed computationally (MP2/aug-cc-pVDZ). A digital image processing- and machine learning-based algorithm was developed to predict the mean neighboring effect distances of the produced foams. It was created using the Voronoi tessellation method used for the identification problems in industrial applications. This method showed that there was a close relationship between the calculated Voronoi neighboring effect distances of the samples and their thermal conductivity coefficients. Considering the Voronoi neighboring effect distances proposed in this study, the thermal conductivity coefficient of similar polyurethane foams could be predicted. This method required only a standard mobile phone to capture images of the samples and the algorithm developed using Python (version 3.13.2) programming language. In addition, when compared to the local surface imaging device SEM, it allowed the entire surface to be analyzed faster and at once, without any surface deterioration. Full article

The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a constrained muon flux, leading to prolonged acquisition times, noisy reconstructions, and challenges in image interpretation. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images were able to match the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated a noise improvement to that of 31 days worth of sampling. A second cWGAN-GP model, trained for semantic segmentation, was developed to quantitatively assess the upsampling model’s impact on each of the features within the concrete samples. This model was able to achieve segmentation of rebar grids and tendon ducts embedded in the concrete, with respective Dice–Sørensen accuracy coefficients of 0.8174 and 0.8663. This model also revealed an unexpected capability to mitigate—and in some cases entirely remove—z-plane smearing artifacts caused by the muography’s inherent inverse imaging problem. Both models were trained on a comprehensive dataset generated through Geant4 Monte Carlo simulations designed to reflect realistic civil infrastructure scenarios. Our results demonstrate significant improvements in both acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications. Full article

The purpose of this study is to examine how tax incentives resulting from the so-called Amazon Law (Law No. 27037) affect small- and medium-sized agro-industrial producers (SMEAPs) in the Junín and Huánuco regions in Peru. This research fills a void that relates to the exclusion of these producers regarding the Law’s incentives that aim to encourage investment in the Amazon. In this study, the research design was non-experimental, and since the data were descriptive–correlational in nature, a structured questionnaire with a Likert scale was used to gauge participants’ opinions about economic progress and tax benefits. The survey participants included 72 co-operatives drawn from a population of 88, and their awareness and use of tax incentives were targeted. SPSS and similar statistical analysis tools were used and showed that there was a positive correlation between tax benefits and economic development, with a correlation coefficient of 0.873, indicating a strong relationship. However, most co-operatives ranked the benefits only as average or poor, with 34.72% rating them as regular and 31.94% as poor. This study indicates that the present laws do not provide these producers with sufficient opportunities for development. The authors suggest that changes to the Law are required to improve the inclusion of small- and medium-sized agricultural producers so that proposals for improvements in their economic development and management of the agricultural lands in the Amazon region can be promoted. Full article

As the integration of the digital economy into the real economy accelerates and the goals of green and low-carbon development become increasingly clear, promoting green innovation (GI) through digital–real economy integration (DRI) is of great practical significance for achieving green, high-quality urban development. This study, based on a sample of 284 prefecture-level cities and above in China, used the entropy method to measure the development levels of the digital economy and real economy in each city and employed a coupling coordination model to calculate the degree of DRI. By constructing a bidirectional fixed effects model, a mediation effect model, a threshold regression model, and a spatial Durbin model, this study explored the impact of DRI on GI in cities. The research found that DRI has a non-linear promoting effect on GI. When a city’s DRI level surpasses the second threshold, each 1% increase in DRI leads to a 1.439% rise in GI. This effect also shows heterogeneity based on city location and resource endowment. In this process, transaction costs and the upgrading of the industry structure serve as mediating factors, with each 1% increase in DRI reducing transaction costs by 0.163% and enhancing industrial upgrading with a coefficient of 0.176. Additionally, DRI in one city can significantly enhance the level of GI in neighboring cities through spatial spillover effects. For instance, under the geographic distance weighting matrix, the indirect effect of DRI reaches 4.693, and similar significant spillover effects are observed under the economic distance and economic geography weighting matrices. Full article

The rapid growth of the global population, the acceleration of the urbanization process, and the demands of economic development, place enormous pressure on scarce land resources. Cultivated land use presents a series of problems, hindering its socioeconomic and ecological sustainability. The sustainable intensification of cultivated land use (SICLU) is a development model designed to maximize land use efficiency, while minimizing environmental pollution. It is considered to be an efficient method to achieve three aspects of sustainable goals, namely in regard to society, the economy, and ecology, simultaneously. This approach has significant theoretical and practical implications for China’s food security and ecological safety. This study incorporates the “agricultural carbon emissions” indicator into the indicator evaluation system. Using the super-efficiency SBM model, we estimate the SICLU levels in China from 2001 to 2020. ArcGIS and the Dagum Gini coefficient decomposition model are employed to explore the temporal and spatial evolution characteristics and non-equilibrium spatial dynamics of SICLU in China. Finally, the Tobit regression model is used to reveal the driving factors. The results show the following: (1) Since 2003, China’s SICLU levels demonstrate an overall ascent amid fluctuations, sustaining a relatively high average annual level of 0.945. (2) In terms of spatial evolution patterns, China’s SICLU levels demonstrate significant spatial disparities, with distinct differences among the four major regions. Regions with similar SICLU levels show a certain degree of spatial adjacency. (3) There are significant regional disparities in China’s SICLU levels, which overall exhibit a declining trend. The differences between regions are the primary source of spatial variation, followed by hypervariable density and intra-regional disparities. (4) The regional industrial structure, the level of agricultural modernization, the agricultural cropping structure, and the per capita sown area, positively influence the enhancement of SICLU levels in China. Throughout the study period, the SICLU levels in China continuously improved and the overall regional disparities diminished. However, significant inter-regional imbalances persist, necessitating tailored optimization measures, based on local conditions. Establishing a coordinated mechanism for orderly and synergistic regional development is crucial, in order to provide references to decision-makers to promote the rational use of arable land in China. Full article

In the marine industry, the search for sustainable methods, materials, and processes, from the product’s design to its end-of-life stages, is a necessity for combating the negative consequences of climate change. In this context, the lightening of products is essential in reducing their environmental impact throughout their life. In addition to lightening through design, lightweight materials, especially plastic-based composites, will need to be used in new and creative ways. The material extrusion technique, one of the additive manufacturing methods, is becoming more widespread day by day, especially in the production of objects with complex forms. This prevalence has not yet been reflected in the marine industry. In this study, the performances of plastic composite propellers produced by the material extrusion technique is investigated and discussed comparatively with the help of both hydrodynamic and structural tests carried out in a cavitation tunnel and mechanical laboratory. The cavitation tunnel test and numerical simulations were conducted at a range of advance coefficients (J) from 0.3 to 0.9. The shaft rate was kept at 16 rps. The thrust and torque data were obtained using the tunnel dynamometer. Digital pictures were taken to obtain structural deformation and cavitation dynamics. The structural performance of the propellers shows that an aluminum propeller is the most rigid, while a short carbon fiber composite propeller is the most flexible. Continuous carbon fiber composite has high strength and stiffness, while continuous glass fiber composite is more cost-effective. In terms of the hydrodynamic performance of the propellers, flexibility reduces the loading on the blade, which can result in thrust and torque reduction. Overall, the efficiency of the composite propellers was similar and less than that of the rigid aluminum propeller. In terms of weight, the composite carbon propeller containing continuous fiber, which is half the weight of the metal propeller, is considered as an alternative to metal in production. These propellers were produced from a unique composite consisting of polyamide, one of the thermoplastics that is a sustainable composite material, and glass and carbon fiber as reinforcements. The findings showed that the manufacturing method and the new composites can be highly successful for producing ship components. Full article

Southeast Asia is suffering from extreme weather, and its carbon emissions are increasing rapidly. For efficient emission reduction, clarifying the complex sources behind is necessary. This study takes a new perspective of incorporating multiple GHGs (greenhouse gases) into the same analysis framework, employing the STIRPAT model to dissect the contributions of various socio-economic factors to the emissions of CO₂, CH₄, and F-gases based on panel data. The analysis reveals that the driving coefficients of total population and urbanization rate are several to 10 times higher than those of other factors and can reach up to 2.98 and 4.715 and are the most significant drivers of GHG emissions in the region. Quadratic per capita GDP shows a significant positive driving effect, indicating that most Southeast Asian countries are unlikely to reach the Kuznets point in current development trajectories. The driving coefficients of F-gases in industrialized countries are significantly higher than those of other GHGs, indicating that their growth rate of F-gases will outpace that of CO₂ and CH₄. In countries with a similar industrial structure, the driving coefficient of CO₂ from the secondary industry is up to 0.183 and down to 0.057, shows the influence of specific sector composition in the secondary industry on emissions. These findings provide critical insights for Southeast Asian policymakers aiming to develop effective climate policies. Full article

Recently, groove gap waveguides (GGWs) have shown significant potential in power handling and bandwidth enhancement compared to conventional waveguides. In this research work, we designed and developed an innovative mushroom-unit-cell-based groove gap waveguide (MGGW) that has shown improved bandwidth compared to conventional GGW structures. The dispersion characteristics of the MGGW were analyzed through the eigenmode solver feature of Microwave Studio CST, which showed that the bandwidth was improved by 8% compared to conventional unit cells in the microwave spectrum. To validate our proposed method for the physical dimensions of unit cell structures, we developed an MGGW structure for the S band, which shows similar trends aligning with the simulation results. The measurement results are promising as a reflection coefficient of less than −20 dB was achieved over the entire band for the WR284 Electronic Industries Alliance (EIA) standard waveguide adapter. The proposed MGGW structure with improved bandwidth will open new doors for researchers to develop ultra-wide bandwidth microwave applications, i.e., filters, transmission lines, resonators, attenuators, etc. Full article

Industrial structure upgrading (ISU) plays a critical role in reducing carbon emissions (CO₂ emissions); however, the existing literature lacks dynamic research on the relationship between the two. Based on provincial panel data from China between 2002 and 2021, this paper establishes a time-varying coefficient two-way fixed-effects model to empirically explore the dynamic effects of ISU on CO₂ emissions. The findings indicate that the overall impact of China’s ISU on CO₂ emissions demonstrates a dynamic tendency of initially promoting and subsequently inhibiting such emissions and, since 2016, ISU has had the ability to significantly reduce CO₂ emissions. This time-varying trend is highly related to the evolving direction and stage of the ISU. During the initial stage of ISU, dominated by industrialization, the promotional effect is dominant in terms of CO₂ emissions, but with the development of tertiary and emerging industries, its inhibitory effect is continuously enhanced and, eventually, ISU can significantly suppress CO₂ emissions. Further, regional heterogeneity analysis shows that in the eastern and western regions of China, ISU has always inhibited CO₂ emissions, while in the central and northeastern regions, ISU first promotes and then inhibits CO₂ emissions, which is similar to the overall pattern in China. Based on these findings, relevant policy suggestions are provided to promote sustainable economic and environmental development. Full article

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts. Full article

The detection of abrasive grain images in diamond tools serves as the foundation for assessing the overall condition of the tools, encompassing crucial aspects of diamond abrasive grains like the quantity, size, morphology, and distribution. Given the intricate background textures and reflective characteristics exhibited by diamond images, diamond detection and segmentation pose a significant challenge. Recently, numerous defect detection methods based on machine learning and deep learning have emerged. However, several issues persist, such as detection accuracy and the interference caused by intricate background textures. The present work demonstrates an efficient classification and segmentation network algorithm that combines Swin-Unet with SAM (Segment Anything Model) to alleviate the existing problems. Specifically, four embedding structures were devised to bridge the two models for iterative training. The transformer blocks within the Swin-Unet model were enhanced to facilitate classification and coarse segmentation, and the mask structure in SAM was refined to enable fine segmentation. The experimental results show that under a small sample dataset with complex background textures, the average index values of ACC (accuracy), SE (Sensitivity), and DSC (Dice Similarity Coefficient) for the classification and segmentation of diamond abrasive grains reached 98.7%, 92.5%, and 85.9%, respectively. Compared with the model before improvement, its ACC, SE and DSC increased by 1.2%, 15.9%, and 7.6%, respectively. The test results, based on four different datasets, consistently indicated that this model has excellent segmentation performance and robustness and has great application potential in the industrial field. Full article

Activated carbon (AC) and biochar (BC) are porous materials with large surface areas and widely used in environmental and industrial applications. In this study, different types of AC and BC samples were produced from Sargassum sp. by a chemical activation and pyrolysis process and compared to commercial activated carbon samples. All samples were characterized using various techniques to understand their structure and functionalities. The metal content of the samples was characterized by using an inductively coupled optical emission spectrometer (ICP-OES). A toxicity test was applied to investigate the effect of AC/BC on organisms, where Sinapis alba seed and Escherichia coli bacteria-based toxicity tests were used. The results revealed that the samples did not negatively affect these two organisms. Thus, it is safe to use them in various applications. Therefore, the samples were tested as fillers in polyurethane composites and, thus, polyurethane-AC/BC samples were prepared. The amounts of AC/BC mixed into the polyurethane formulation were 1%, 2%, and 3%. Mechanical and acoustic properties of these composites were analyzed, showing that by adding the AC/BC to the system an increase in the compression strength for all the samples was achieved. A similar effect of the AC/BC was noticed in the acoustic measurements, where adding AC/BC enhanced the sound adsorption coefficient (α) for all composite materials. Full article