Search Results (6,925)

Understanding the relationship between gel formation and the hierarchical structure of mung bean starch fermented by Lactobacillus plantarum has potential value for its green modification and quality improvement. The variations in characteristics, including gelation characteristics, starch chain, and the molecular order degree of mung bean starch fermented by different L. plantarum, were compared. The results show that in the gelation process, starch began to disintegrate at 65 °C, indicating a critical temperature for structural changes. Compared with the control group, although the effects of different L. plantarum sources on mung bean starch varied, notable improvements were observed in water absorption across all groups of fermented starch, along with reduced free water-soluble substances and enhanced anti-expansion ability. This led to the easier formation of gels with higher viscosity, primarily attributed to decreased crystallinity, increased short-chain amylopectin tendency, an elevated amylose content, and enhanced short-range order when microorganisms acted on the crystallization zone. In conclusion, although L. plantarum came from different sources, its action mode on mung bean starch was similar, which could enhance the gel structure. Full article

The interrelation between Reynolds stresses and their dissipation rate tensors for different Karlovitz number values was analysed using a direct numerical simulation (DNS) database of turbulent statistically planar premixed H₂-air flames with an equivalence ratio of 0.7. It was found that a significant enhancement of Reynolds stresses and dissipation rates takes place as a result of turbulence generation due to thermal expansion for small and moderate Karlovitz number values. However, both Reynolds stresses and dissipation rates decrease monotonically within the flame brush for large Karlovitz number values, as the flame-generated turbulence becomes overridden by the strong isotropic turbulence. Although there are similarities between the anisotropies of Reynolds stress and its dissipation rate tensors within the flame brush, the anisotropy tensors of these quantities are found to be non-linearly related. The predictions of three different models for the dissipation rate tensor were compared to the results computed from DNS data. It was found that the model relying upon isotropy and a linear dependence between the Reynolds stress and its dissipation rates does not correctly capture the turbulence characteristics within the flame brush for small and moderate Karlovitz number values. In contrast, the models that incorporate the dependence of the invariants of the anisotropy tensor of Reynolds stresses were found to capture the components of dissipation rate tensor for all Karlovitz number conditions. Full article

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified. Full article

Bivalve mollusks, comprising animals enclosed in two shell valves, are well-adapted to benthic life in many intertidal zones. Clams have evolved the buried lifestyle, which depends on their unique soft tissue structure and their wedge-shaped muscular foot and long extendible siphons. However, molecular mechanisms of adaptative phenotype evolution remain largely unknown. In the present study, we obtain the high-quality chromosome-level genome of Manila clam R. philippinarum, an economically important marine bivalve in many coastal areas. The genome is constructed by the Hi-C assisted assembly, which yields 19 chromosomes with a total of 1.17 Gb and BUSCO integrity of 92.23%. The de novo assembled genome has a contig N50 length of 307.7 kb and scaffold N50 of 59.5 Mb. Gene family expansion analysis reveals that a total of 24 single-copy gene families have undergone the significant expansion or contraction, including E3 ubiquitin ligase and dynein heavy chain. The significant expansion of transposable elements has been also identified, including long terminal repeats (LTR) and non-LTR retrotransposons. The comparative transcriptomics among different clam tissues reveals that extracellular matrix (ECM) receptors and neuroactive ligand receptors may play the important roles in tissue structural support and neurotransmission during their infaunal life. These findings of gene family expansion and tissue-specific expression may reflect the unique soft tissue structure of clams, suggesting the evolution of lineage-specific morphological novelties. The high-quality genome and transcriptome data of R. philippinarum will not only facilitate the genetic studies on clams but will also provide valuable information on morphological novelties in mollusks. Full article

Ambipolar plasma thrusters are an appealing technology due to multiple system-related advantages, including propellant flexibility and the absence of electrodes or neutralizer. Understanding the plasma generation and acceleration mechanisms is key to improving the performance and capabilities of these thrusters. However, the source and plume regions inside are often simulated separately, and no self-consistent strategy exists which can couple these different simulations together. This paper introduces the MUlti-regime Plasma Equilibrium Transport Solver (MUPETS), a self-consistent coupled model integrating a fluid solver for the plasma dynamics in the source, which are collision-driven, with a kinetic Particle-In-Cell (PIC) code for the plasma dynamics in the magnetic nozzle, which involve expansion across a diverging magnetic field. The methodology begins by solving the plasma source with the classical Bohm condition at the thruster’s throat. The resulting plasma profiles (density, temperature, speed) are input into the PIC code for the magnetic nozzle. The PIC code calculates the plasma plume expansion and determines the electric field at the thruster’s throat. This electric field is then used as a boundary condition in the fluid code, where it replaces the Bohm assumption, and the fluid simulation is repeated. This iterative process continues until convergence. In comparing the MUPETS results with those for an experimental thruster, the plasma densities at the thruster’s throat differed by less than 2–5% between the fluid and PIC regions. The thrust predictions agreed with the experimental trend, and were kept well within the measurement’s uncertainty band. These results validate the effectiveness of the coupling strategy for enhancing plasma thruster simulation accuracy. Full article

Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies. Full article

Expansins are known as cell wall loosening proteins and are involved in cell expansion and varieties of plant developmental progresses. However, little is known about their biological functions in alfalfa (Medicago sativa L.). In the present study, 30 MsEXP genes were identified in the alfalfa (cultivar “zhongmu-1”) genome. Phylogenetic analysis revealed that these MsEXP proteins were divided into four subfamilies, including twenty-one MsEXPAs, six MsEXPBs, one MsEXL1 and two MsEXLBs. MsEXP genes were unevenly distributed on eight chromosomes. The gene structures of the MsEXP genes and the motif composition of the MsEXP proteins were inconsistent with the phylogenetic relationship of MsEXPs. Cis-acting elements analysis indicated that MsEXP genes may respond to diverse hormonal signals involved in the developmental progress of plants. Furthermore, expression analysis suggested that MsEXP genes exhibited distinct expression patterns among different tissues of alfalfa. Overexpression of MsEXPA3 or MsEXPA4 promoted the growth of leaves, stems and roots of Arabidopsis, thereby increasing the biomass of plants. Subsequent cell morphological analysis uncovered that overexpression of MsEXPA3 or MsEXPA4 promoted the expansion of cells. Taken together, these findings illustrate the functions of MsEXP proteins in regulating the development of plants. Our results may provide a strong basis for further elucidating the roles of these EXP genes in alfalfa development and valuable genetic resources for future crop improvement. Full article

Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water body with the external boundary. Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. Then, an information interaction module (IIM) is designed to exchange information between two spatial paths with two fixed resolution intervals and the two paths through. During decoding, a feature decoupling module (FDM) is developed to utilize internal flow prediction to acquire the main body features, and erasing techniques are employed to obtain boundary features. Therefore, the deep features of the water body and the detailed boundary information are supplemented, strengthening the decoupled body and boundary features. Furthermore, the integrated expansion recoupling module (IERM) module is designed for the recoupling stage. The IERM expands the water body and boundary features using expansion and adaptively compensates the transition region between the water body and boundary through information guidance. Finally, multi-level constraints are combined to realize the supervision of the decoupled features. Thus, the water body and boundaries can be extracted more accurately. A comparative validation analysis is conducted on the public datasets, including the gaofen image dataset (GID) and the gaofen2020 challenge dataset (GF2020). By comparing with seven SOTAs, the results show that the proposed method achieves the best results, with IOUs of 91.22 and 78.93, especially in the localization of water bodies and boundaries. By applying the proposed method in different scenarios, the results show the stable capability of the proposed method for extracting water with various shapes and areas. Full article

Straw mulching cultivation technology can improve the soil environment of farmland, and it is applied in the dry farming area of Northwestern China. There are few studies on the effect of corn straw mulching on the soil temperature and yield of potato fields in dry land in Northwestern China. In this paper, three treatments, black film ridge (HM), corn straw mulching mechanized no-tillage planting (JG) and no-covering open field flat planting (CK), were set up in the period of 2022 to 2023. A field plot experiment was carried out to determine the soil temperature, growth index, and yield data during the key growth period. The statistical analysis results show that JG and HM significantly increased the potato yield, by 12.27~18.30% and 13.09~18.10%, compared with CK, but there was no significant difference between JG and HM. The yield was significantly positively correlated with tuber weight per plant at the tuber expansion stage, starch accumulation stage, and harvest stage (0.47 *~0.60 **), and significantly negatively correlated with the number of tubers at the harvest stage (−0.54 *). Compared with CK, HM increased the average soil temperature over the whole growth period by 0.27~0.92 °C. In 2022, the increase in the 5 cm soil layer in the tuber expansion period was the largest, reaching 0.83 °C. In 2023, the increase in the 5 cm soil layer in the starch accumulation period was the largest, reaching 3.08 °C. JG reduced the soil temperature over the whole growth period by 0.52 °C, and the 20 cm soil layer in the tuber formation period decreased the most, reaching 1.45 °C, which aggravated the soil temperature change over the whole growth period (the amplitude was 4.13~4.53 °C). The temperature difference between day and night in different growth periods in 2022 was 2.14~5.41 °C, and the soil temperature in some growth periods in 2022 even exceeded that with HM. The results showed that JG could regulate soil temperature and optimize the relationship between tuber weight per plant, tuber number per plant, and biomass allocation during tuber formation, which are beneficial for the improvement of the potato yield in the dry farming area of Northwestern China. Full article

Heterobasidion, a significant forest pathogen affecting coniferous forests in the Northern Hemisphere, can infect 27 species of coniferous trees, leading to widespread forest mortality. It has already caused considerable damage to both natural and plantation forests in Europe. As essential components of the global ecosystem, forests are increasingly affected by ongoing environmental changes. The ability to accurately predict and effectively respond to pathogen outbreaks across different habitats is becoming increasingly critical. This study employs an optimized MaxEnt model in conjunction with six Global Climate Models (GCMs) to simulate and predict the potentially suitable distributions and changes of three Heterobasidion species in Europe (Heterobasidion abietinum, Heterobasidion annosum sensu stricto, Heterobasidion parviporum) under current conditions and four future climate scenarios (SSP126, SSP245, SSP370, and SSP585) for the period 2081–2100. The objective of this analysis is to assess the potential effects of climate change on the distribution of Heterobasidion species. The results indicate that the distributions of the three Heterobasidion species are influenced by factors such as temperature and precipitation. By 2081–2100, under most climate scenarios, except for the SSP585 scenario, the distribution areas of the three Heterobasidion species show an expansion trend. Notably, Heterobasidion abietinum showed the most significant expansion of its suitable habitat, while the expansion of low-suitability areas for Heterobasidion parviporum and Heterobasidion annosum sensu stricto was more pronounced. Heterobasidion abietinum is projected to shift southward due to factors such as precipitation, while Heterobasidion parviporum and Heterobasidion annosum sensu stricto are expected to migrate northward, influenced by factors such as temperature and host tree species. Full article

Background: The significant expansion of nanobiotechnology and nanomedicine has led to the development of innovative and effective techniques to combat various pathogens, demonstrating promising results with fewer adverse effects. Metal peroxide nanoparticles stand out among the crucial yet often overlooked types of nanomaterials, including metals. These nanoparticles are key in producing oxygen (O₂) and hydrogen peroxide (H₂O₂) through simple chemical reactions, which are vital in treating various diseases. These compounds play a crucial role in boosting the effectiveness of different treatment methods and also possess unique properties due to the addition of metal ions. Methods: This review discusses and analyzes some of the most common metal peroxide nanoparticles, including copper peroxide (CuO₂), calcium peroxide (CaO₂), magnesium peroxide (MgO₂), zinc peroxide (ZnO₂), barium peroxide (BaO₂), and titanium peroxide (TiOx) nanosystems. These nanosystems, characterized by their greater potential and treatment efficiency, are primarily needed in nanomedicine to combat various harmful pathogens. Researchers have extensively studied the effects of these peroxides in various treatments, such as catalytic nanotherapeutics, photodynamic therapy, radiation therapy, and some combination therapies. The tumor microenvironment (TME) is particularly unique, making the impact of nanomedicine less effective or even null. The presence of high levels of reactive oxygen species (ROS), hypoxia, low pH, and high glutathione levels makes them competitive against nanomedicine. Controlling the TME is a promising approach to combating cancer. Results: Metal peroxides with low biodegradability, toxicity, and side effects could reduce their effectiveness in treating the TME. It is important to consider the distribution of metal peroxides to effectively target cancer cells while avoiding harm to nearby normal cells. As a result, modifying the surface of metal peroxides is a key strategy to enhance their delivery to the TME, thereby improving their therapeutic benefits. Conclusions: This review discussed the various aspects of the TME and the importance of modifying the surface of metal peroxides to enhance their therapeutic advantages against cancer, as well as address safety concerns. Additionally, this review covered the current challenges in translating basic research findings into clinical applications of therapies based on metal peroxide nanoparticles. Full article

The rapid expansion of digital commerce, particularly same-day-delivery and next-day-delivery online shopping, is transforming daily life and community dynamics in urban settings. This study explores how these shopping behaviors impact the sense of community by mediating various social activities at both individual and community levels. Using an online survey design, this study analyzes the mediating roles of different types of social interactions, including informal social gatherings and organized community events, in shaping a sense of community. The findings reveal that while both unplanned, low-threshold interactions and more structured, organized activities contribute to a sense of community, their effects vary in strength. Stronger, more deliberate social interactions, such as resident-led events, show a full mediation effect, whereas casual, spontaneous encounters provide partial mediation. Similarly, organized community events demonstrate different levels of influence, with community-organized activities fully mediating certain sense of community dimensions and resident-engaged activities providing more comprehensive impacts. These findings highlight that same-day-delivery and next-day-delivery online shopping behaviors significantly shape the sense of community by facilitating both informal and organized social interactions. Full article

Multi-material can have functional properties, which are not typical for the materials of which they are composed (for instance, shape-changing effect). This can be used in robotics, micromachines, aerospace, and other fields. In this work, the 316L/FeNi36 multi-material produced by selective laser melting was investigated. The results show that the interfacial zone of the multi-material exhibits mixing regions of the two alloys but no defects. The microstructure is constituted by large grains with epitaxial growth, which propagate in a directional manner from the 316L alloy through the interfacial zone to the FeNi36 region. The multi-material sample displays three different zones of chemical composition: the FeNi36 composition zone; the interfacial zone; and the 316L zone. The size of the interfacial zone is approximately 50 µm. The multi-material sample exhibits the presence of three distinct phases: γ-Fe; γ-Fe64Ni36; and α-Fe. The hardness of the FeNi36 zone is approximately 163 HV, followed by an interfacial zone with a hardness of approximately 200 HV and then, the 316L zone with a hardness of approximately 214 HV. Functional tests demonstrate that the shape-changing effect is directly correlated with the variation in the FeNi36 thermal expansion coefficient with temperature. For achieving the most pronounced shape-changing effect, the temperature range of 25–215 °C is more suitable. Full article

Forest ecosystem services are critical for maintaining ecological balance and supporting human well-being from different perspectives. However, rapid land use changes driven by agricultural expansion, urbanization, and industrial activities have significantly altered forest ecosystems, degrading the services they provide. We here conduct an ecosystem service assessment through biophysical and economic estimates for a multipurpose Andean water sub-basin in western Colombia. We compare a business as usual (BAU) with a forest nature-based solution (NbS) scenario focused on forest landscape restoration. The research employed participatory methods for the NbS selection and economic valuation techniques to evaluate water flow regulation, water provisioning, water purification, and food provisioning services. Results show that the NbS scenario yielded a net positive economic impact across most evaluated ecosystem services, with notable trade-offs. Specifically, the NbS scenario increased water retention by 2.9% compared to BAU. Water flow regulation demonstrated the most substantial economic benefit, increasing by EUR 11.39 million/year in the NbS scenario. On the other hand, the food provisioning service presented a reduction of EUR 3.2 million/year in the NbS scenario. These findings highlight the potential of forest-based NbS to address the Water–Energy–Food–Ecosystem (WEFE) nexus challenges. The study’s outcomes provide valuable insights for policymakers and practitioners, supporting the development of Payment for Ecosystem Services schemes and integrating ecosystem service valuation into land use planning and decision-making processes. Full article

Understanding and recognizing urban morphology evolution is a crucial issue in urban planning, with extensive research dedicated to detecting the extent of urban expansion. However, as urban development patterns shift from incremental expansion to stock optimization, related studies on meso- and microscale urban morphology evolution face limitations such as insufficient spatiotemporal data granularity, poor generalizability, and inability to extract internal evolution patterns. This study employs deep learning and meso-/microscopic urban form indicators to develop a generic framework for extracting and describing the evolution of meso-/microscale urban morphology. The framework includes three steps: constructing specific urban morphology datasets, semantic segmentation to extract urban form, and mapping urban form evolution using the Tile-based Urban Change (TUC) classification system. We applied this framework to conduct a combined quantitative and qualitative analysis of the internal urban morphology evolution of Binhai New Area from 2009 to 2022, with detailed visualizations of morphology evolution at each time point. The study identified that different locations in the area exhibited seven distinct evolution patterns: edge areal expansion, preservation of developmental potential, industrial land development pattern, rapid comprehensive demolition and construction pattern, linear development pattern, mixed evolution, and stable evolution. The results indicate that in the stock development phase, high-density urban areas exhibit multidimensional development characteristics by region, period, and function. Our work demonstrates the potential of using deep learning and grid classification indicators to study meso-/microscale urban morphology evolution, providing a scalable, cost-effective, quantitative, and portable approach for historical urban morphology understanding. Full article