Search Results (30)

With the spread of electric vehicles (EVs), the EV load will have a significant impact on the planning and operation of the grid and the operation of the electricity market. Due to the charging and discharging characteristics of EVs, as well as their randomness and dispersion, it is feasible and challenging to introduce EV loads into the grid as a means of frequency regulation and peak shaving of the power system. In this paper, considering multi-subject distribution networks and the interaction of source–grid–load–storage with Intelligent Transportation Systems (ITS), a density peak clustering (DPC) algorithm based on principal component analysis is employed to analyze the spatial and temporal characteristics of EV loads and identify the access status of EV charging stations and EV load status in each region in real time, as well as analyze the adjustable capacity and adjustable range of EV loads. Based on the adjustable capacity of the EV load, the optimization objectives include the maximum regulation of the EV load and the most economical operation cost. An accurate load regulation strategy based on automatic active control (APC) is proposed to reduce the maximum frequency deviation by 25% by integrating the load regulation of electric vehicles into the original AGC frequency regulation. At the same time, the feasibility of electric vehicles in peaking and standby scenarios is studied and verified through simulation cases, which can reduce the peak value of thermal power generation by 15% and 10% in the morning and evening. Full article

As the basic unit of nature, basins concentrate most of the vegetation cover of terrestrial ecosystems and play an important role in forest carbon fixation and regulation of local climates. However, there are obvious differences between different basins in terms of topography, climate, population, economy, and other factors, so it is important to conduct a comparative study on the spatiotemporal patterns of factors affecting forest carbon storage in different basins. The province of Zhejiang is rich in vegetation resources, and there are obvious differences in the natural and economic factors within the province; GDP is higher in the eastern and northern regions, and natural resources are more abundant in the western and southern regions. Therefore, we used the BIOME-BGC model and the Optimal Parameters-based Geographical Detector (OPGD) model to simulate and analyze the spatiotemporal evolution and driving mechanism of forest aboveground carbon (AGC) storage in eight basins of Zhejiang Province over the past 30 years (1984–2014). The results showed that (1) the overall simulation accuracy of AGC in different basins based on the BIOME-BGC model is high, with the overall simulation accuracy ranging from 0.67 to 0.77. (2) The forest AGC of the eight basins showed an increasing trend over the past 30 years, with a growth rate ranging from 0.07 Tg C/10 yr to 3.45 Tg C/10 yr. (3) Climatic conditions (temperature and precipitation) play a dominant role in the variation in AGC, with an explanatory power above 16% in the southern and northern basins, and the explanatory power of human activities on the AGC is secondary, with more than 9% in the central basins. (4) The interaction between natural factors and socio-economic factors (especially the population density factor) has a more obvious effect on the changes in AGC in each basin, and the explanatory power of the interaction is much larger than that of the single factor. (5) The results of the risk detection showed that human activities were negatively correlated with AGC in all basins. Full article

Background: The diagnostic challenges presented by hyperchromatic crowded cell groups (HCGs) in cervical cytology often result in either overdiagnosis or underdiagnosis due to their densely packed, three-dimensional structures. The objective of this study is to characterize the structural differences among HSIL-HCGs, AGC-HCGs, and NILM-HCGs using quantitative texture analysis metrics, with the aim of facilitating the differentiation of benign from malignant cases. Methods: A total of 585 HCGs images were analyzed, with assessments conducted on 8-bit gray-scale value, thickness, skewness, and kurtosis across various groups. Results: HSIL-HCGs are distinctly classified based on 8-bit gray-scale value. Significant statistical differences were observed in all groups, with HSIL-HCGs exhibiting higher cellular density and cluster thickness compared to NILM and AGC groups. In the AGC group, HCGs shows statistically significant differences in 8-bit gray-scale value compared to NILM-HCGs, but the classification performance by 8-bit gray-scale value is not high because the cell density and thickness are almost similar. These variations reflect the characteristic cellular structures unique to each group and substantiate the potential of 8-bit gray-scale value as an objective diagnostic indicator, especially for HSIL-HCGs. Conclusion: Our findings indicate that the integration of gray-scale-based texture analysis has the potential to improve diagnostic accuracy in cervical cytology and break through current diagnostic limitations in the identification of high-risk lesions. Full article

Over the past half-century, environmental degradation and human disturbances have threatened the aboveground biomass carbon (AGC) in China’s karst ecosystems. However, recent ecological programs have led to environmental improvements, leaving it unclear whether China’s karst ecosystems act as an AGC sink or AGC source. In this study, we utilized L-band vegetation optical depth to quantify the dynamics of AGC across the karst regions of China from 2015 to 2021. We observed an increase in AGC density of 0.73 Mg C ha⁻¹ yr⁻¹, suggesting that karst ecosystems in China functioned as an AGC sink throughout the research period. The largest increase in AGC density, 1.29 Mg C ha⁻¹ yr⁻¹, was observed in Central China, indicating an AGC sink capacity stronger than that of other regions. Among the different land-use types, forests played a dominant role, exhibiting the largest net change in AGC density at 1.03 Mg C ha⁻¹ yr⁻¹. Furthermore, using the random forest model, temperature, soil clay content, and altitude were identified as the primary factors driving AGC changes. Our results enhance the understanding of the role of China’s karst terrestrial ecosystem in the global carbon cycle, emphasizing its contribution to the global carbon sink. Full article

This work aims at the effects of anion-exchange membranes (AEMs) and ionomer binders on the catalyst electrodes for anion-exchange membrane fuel cells (AEMFCs). In the experiments, four metal catalysts (nano-grade Pt, PtRu, PdNi and Ag), four AEMs (aQAPS-S8, AT-1, X37-50T and X37-50RT) and two alkaline ionomers (aQAPS-S14 and XB-7) were used. They were verified through several technical parameters examination and cell performance comparison for the optimal selection of AMEs. The bimetallic PdNi nanoparticles (PdNi/C) loaded with Vulcan XC-72R carbon black were used as anode electrodes by using the wet impregnation method, and Ag nanoparticles (Ag/C) were used as the catalyst cathode. It was found that the power density and current density of the X37-50RT are higher than the other three membranes. Also, alkaline ionomers of XB-7 had better performance than aQAPS-S14. The efficiency was improved by 32%, 155% and 27%, respectively, when compared to other membranes by using the same catalyst of PdNi/C, Ag/C and Pt/C. The results are consistent with the membrane ion conductivity measurements, which showed that the conductivity of the X37-50RT membrane is the highest among them. The conductivity values for hydroxide ions (OH⁻) and bromide ions (Br⁻) are 131 mS/cm and 91 mS/cm, respectively. These findings suggest that the properties (water uptake, swelling rate and mechanical) of the anion-exchange membrane (AEM) can serve as a key reference for AEM fuel cell applications. Full article

This paper presents a novel optoelectronic transimpedance amplifier (OTA) for short-range LiDAR sensors used in 180 nm CMOS technology, which consists of a main transimpedance amplifier (m-TIA) with an on-chip P⁺/N-well/Deep N-well avalanche photodiode (P⁺/NW/DNW APD) and a replica TIA with another on-chip APD, not only to acquire circuit symmetry but to also obtain concurrent automatic gain control (AGC) function within a narrow single pulse-width duration. In particular, for concurrent AGC operations, 3-bit PMOS switches with series resistors are added in parallel with the passive feedback resistor in the m-TIA. Then, the PMOS switches can be turned on or off in accordance with the DC output voltage amplitudes of the replica TIA. The post-layout simulations reveal that the OTA extends the dynamic range up to 74.8 dB (i.e., 1 µA_pp~5.5 mA_pp) and achieves a 67 dBΩ transimpedance gain, an 830 MHz bandwidth, a 16 pA/$\surd \mathrm{Hz}$ noise current spectral density, a −31 dBm optical sensitivity for a 10⁻¹² bit error rate, and a 6 mW power dissipation from a single 1.8 V supply. The chip occupies a core area of 200 × 120 µm². Full article

The rapid urbanization process in recent decades has altered the carbon cycle and exacerbated the impact of climate change, prompting many cities to develop tree planting and green area preservation as mitigation and adaptation measures. While numerous studies have estimated the carbon stocks of urban trees in temperate and subtropical cities, data from tropical regions, including tropical botanic gardens, are scarce. This study aimed to quantify the aboveground biomass and carbon (AGB and AGC, respectively) stocks in trees at the Rio de Janeiro Botanical Garden arboretum, Rio de Janeiro, Brazil. Our survey included 6793 stems with a diameter at breast height (DBH) ≥ 10 cm. The total AGB was 8047 ± 402 Mg, representing 4024 ± 201 Mg of AGC. The AGB density was 207 ± 10 Mg·ha⁻¹ (AGC = 104 ± 5 Mg·ha⁻¹), which is slightly lower than the density stored in Brazil’s main forest complexes, the Atlantic and Amazon forests, but much higher than in many cities worldwide. Our results suggest that, in addition to their global importance for plant conservation, tropical botanic gardens could function as significant carbon sinks within the urban matrix. Full article

The fast-growing tree, eastern cottonwood (Populus deltoides), currently is being planted to catalyze native forest restoration on degraded agricultural sites in the southeastern United States. Many of these restoration sites are appropriate for short rotation woody crop (SRWC) culture that addresses climate mitigation objectives, but information needed to optimize climate mitigation objectives through such plantings is limited. Therefore, we established a 10-year experiment on degraded agricultural land located in the Mississippi Alluvial Valley, USA, aiming to quantify the dynamics of aboveground carbon (AGC) accumulation in a cottonwood planting of four replicated spacing levels (3.7 × 3.7 m, 2.7 × 1.8 m, 2.1 × 0.8 m, and (0.8 + 1.8) × 0.8 m) aligned with SRWC systems targeting various ecosystem services. Annual sampling revealed a substantial range in increments of AGC and year 10 carbon stocks among stands of different densities. Mean annual increments for AGC (MAI_AGC) were similar for the two tightest spacing levels, peaking higher than for the other two spacings at about 7.5 Mg ha⁻¹ y⁻¹ in year 7. Year 10 AGC ranged between 22.3 Mg ha⁻¹ for stands spaced 3.7 × 3.7 m and 70.1 Mg ha⁻¹ for stands of the two tightest spacings, leading us to conclude that a spacing between 2.1 × 0.8 m and 2.7 × 1.8 m would maximize aboveground carbon stocks through year 10 on sites of similar agricultural degradation. Increments and accumulation of AGC on the degraded site trended lower than values reported from more productive sites but illustrate that quick and substantial transformation of the carbon stock status of degraded agricultural sites can be achieved with the application of SRWCs to restore forests for climate mitigation and other compatible ecosystem services. Full article

Radiofrequency Interference (RFI) is a growing concern for many navigation-reliant applications. The dual benefits of RFI localization are considered: first, it can help with situational awareness by estimating the location of the interference source, and secondly, the results can be used to verify the detection of significant interference. The paper exploits the latter by proposing detection techniques making use of the localization results. The performance of the algorithms is evaluated using an experiment in a controlled lab environment where a wideband interference source is emulated in a UAV-based scenario. The detection results are validated using a reference detector operating in a non-position domain. Full article

(1) Background: Tropical Mountain forests (TMF) constitute a threatened major carbon sink due to deforestation. Carbon compensation projects could significantly aid in preserving these ecosystems. Consequently, we need a better understanding of the above-ground carbon (AGC) spatial distribution in TMFs to provide project developers with accurate estimations of their mitigation potential; (2) Methods: integrating field measurements and remote sensing data into a random forest (RF) modelling framework, we present the first high-resolution estimates of AGC density (Mg C ha⁻¹) over the western Ecuadorian Andes to inform an ongoing carbon compensation mechanism; (3) Results: In 2021, the total landscape carbon storage was 13.65 Tg in 194,795 ha. We found a broad regional partitioning of AGC density mediated primarily by elevation. We report RF-estimated AGC density errors of 15% (RMSE = 23.8 Mg C ha⁻¹) on any 10 m pixel along 3000 m of elevation gradient covering a wide range of ecological conditions; (4) Conclusions: Our approach showed that AGC high-resolution maps displaying carbon stocks on a per-pixel level with high accuracy (85%) could be obtained with a minimum of 14 ground-truth plots enriched with AGC density data from published regional studies. Likewise, our maps increased precision and reduced uncertainty concerning current methodologies used by international standards in the Voluntary Carbon Market. Full article

The utilization of anion exchange membranes (AEMs) has revolutionized the field of electrochemical applications, particularly in water electrolysis and fuel cells. This review paper provides a comprehensive analysis of recent studies conducted on various commercial AEMs, including FAA3-50, Sustainion, Aemion™, XION Composite, and PiperION™ membranes, with a focus on their performance and durability in AEM water electrolysis (AEMWE) and AEM fuel cells (AEMFCs). The discussed studies highlight the exceptional potential of these membranes in achieving high current densities, stable operation, and extended durability. Furthermore, the integration of innovative catalysts, such as nitrogen-doped graphene and Raney nickel, has demonstrated significant improvements in performance. Additionally, the exploration of PGM-free catalysts, such as Ag/C, for AEMFC cathodes has unveiled promising prospects for cost-effective and sustainable fuel cell systems. Future research directions are identified, encompassing the optimization of membrane properties, investigation of alternative catalyst materials, and assessment of performance under diverse operating conditions. The findings underscore the versatility and suitability of these commercial AEMs in water electrolysis and fuel cell applications, paving the way for the advancement of efficient and environmentally benign energy technologies. This review paper serves as a valuable resource for researchers, engineers, and industry professionals seeking to enhance the performance and durability of AEMs in various electrochemical applications. Full article

In this work, the synthesis of bimetallic Ag and Cu particles on carbon vulcan (AgCu/C) is reported, synthesized by a simple galvanic displacement method using citrate tribasic hydrate as a co-reducing agent and a commercial material based on Cu/C as a template. The materials were characterized by several physicochemical techniques, including TGA, ICP-OES, XRD, SEM, and BET. The catalysts were evaluated as cathodic catalysts for the oxygen reduction reaction (ORR) and were used for the preparation of membrane electrode assemblies for evaluation in an Anion Exchange Membrane Fuel Cell (AEMFC). The results were compared with the commercial Ag/C and Cu/C catalysts; the bimetallic catalyst obtained a higher power density, which was attributed to a synergistic effect between Ag and Cu particles. Full article

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R² = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R² = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs. Full article

Morphologically similar to Synechococcus, a large number of Parasynechococcus strains were misclassified, resulting in extreme underestimation of their genetic diversity. In this study, 80 Synechococcus-like strains were reevaluated using a combination of 16S rRNA phylogeny and genomic approach, identifying 54 strains as Parasynechococcus-like strains and showing considerably intragenus genetic divergence among the subclades identified. Further, bioinformatics analysis disclosed diversified patterns of distribution, abundance, density, and diversity of microsatellites (SSRs) and compound microsatellites (CSSRs) in genomes of these Parasynechococcus-like strains. Variations of SSRs and CSSRs were observed amongst phylotypes and subclades. Both SSRs and CSSRs were in particular unequally distributed among genomes. Dinucleotide SSRs were the most widespread, while the genomes showed two patterns in the second most abundant repeat type (mononucleotide or trinucleotide SSRs). Both SSRs and CSSRs were predominantly observed in coding regions. These two types of microsatellites showed positive correlation with genome size (p < 0.01) but negative correlation with GC content (p < 0.05). Additionally, the motif (A)_n, (AG)_n and (AGC)_n was a major one in the corresponding category. Meanwhile, distinctive motifs of CSSRs were found in 39 genomes. This study characterizes SSRs and CSSRs in genomes of Parasynechococcus-like strains and will be useful as a prerequisite for future studies regarding their distribution, function, and evolution. Moreover, the identified SSRs may facilitate fast acclimation of Parasynechococcus-like strains to fluctuating environments and contribute to the extensive distribution of Parasynechococcus species in global marine environments. Full article

Timely, accurate estimates of forest aboveground carbon density (AGC) are essential for understanding the global carbon cycle and providing crucial reference information for climate-change-related policies. To date, airborne LiDAR has been considered as the most precise remote-sensing-based technology for forest AGC estimation, but it suffers great challenges from various uncertainty sources. Stratified estimation has the potential to reduce the uncertainty and improve the forest AGC estimation. However, the impact of stratification and how to effectively combine stratification and modeling algorithms have not been fully investigated in forest AGC estimation. In this study, we performed a comparative analysis of different stratification approaches (non-stratification, forest type stratification (FTS) and dominant species stratification (DSS)) and different modeling algorithms (stepwise regression, random forest (RF), Cubist, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost)) to identify the optimal stratification approach and modeling algorithm for forest AGC estimation, using airborne LiDAR data. The analysis of variance (ANOVA) was used to quantify and determine the factors that had a significant effect on the estimation accuracy. The results revealed the superiority of stratified estimation models over the unstratified ones, with higher estimation accuracy achieved by the DSS models. Moreover, this improvement was more significant in coniferous species than broadleaf species. The ML algorithms outperformed stepwise regression and the CatBoost models based on DSS provided the highest estimation accuracy (R² = 0.8232, RMSE = 5.2421, RRMSE = 20.5680, MAE = 4.0169 and Bias = 0.4493). The ANOVA of the prediction error indicated that the stratification method was a more important factor than the regression algorithm in forest AGC estimation. This study demonstrated the positive effect of stratification and how the combination of DSS and the CatBoost algorithm can effectively improve the estimation accuracy of forest AGC. Integrating this strategy with national forest inventory could help improve the monitoring of forest carbon stock over large areas. Full article