Distribution Area Research Articles

Shamanism with a drum among the Khanty of the Surgut Ob River region in the beginning of the 21st century

The area of this research is confined to the Surgut Ob River region and includes the western and central areas of residence of the Eastern Khanty. The purpose of this work is to summarize and analyse information about the current state of shamanism with a tambourine among the Surgut Khanty. The main sources for the study were the author's field materials collected in 2002–2017 in the regions along the rivers of Lyamin, Pim, Tromyegan, Agan, Bolshoy and Maly Yugan. The uniqueness of the situation among the Surgut Khanty lies in the fact that the tradi-tional rituals, which received a second wind in the 1990s in the wake of the actualization of ethnicity, in fact, were not interrupted. Among the traditional rites of the region, shamanism with a tambourine is one of the little-studied aspects of their spiritual culture. The article characterizes the current state of shamanic rituals with a tambourine, indicates the area of distribution of these practices, considers examples of making a tambourine and the formation of a sha-manic gift, makes a description of the formal side and indicates the presently recorded occasions for the ritual. The differences between shamanism with a tambourine among the Khanty of the right bank and the left bank of the Ob River are revealed, and some aspects of shamanism are compared with those of the neighbouring ethnic groups.

Soil Nutrient Changes in Black Soil of Northeastern China Under Long-Term Cultivation with Different Fertilizer Applications and Tillage Patterns: A Meta-Analysis

ABSTRACT As one of the four major black soil distribution areas in the world, the black soil region in northeastern China has shown a trend of nutrient degradation under excessive development. Understanding its nutrient changes and the factors affecting them is of great significance to the sustainable development of black soil. Soil nutrient profiles are influenced by several anthropogenic management patterns, including fertilizer application methods and tillage practices. A comprehensive analysis of the influencing factors can help to understand the mechanisms of nutrient changes in black soil cropland. Therefore, using soil organic carbon (SOC) as a proxy for nutrients, this study applied a meta-analysis based on 1138 observations from 60 papers to reveal the effects of anthropogenic management practices (application of organic fertilizers (OF), straw back (SB), application of chemical fertilizers (CF) and conservational tillage (CT)) on soil nutrients. The results showed that the most of soil nutrients increased significantly under management model. The application of chemical fertilizers decreased soil pH by 4.55% and increased soil available phosphorus (AP) content by 112.14%. Compared to the conventional model, application of organic fertilizers significantly increased SOC content, total nitrogen (TN) content and microbial biomass carbon (MBC) content by, 16.75%, 20.10% and 31.15% (p < .05), respectively. The effect values of SOC content were positively correlated with those of most soil nutrient contents under different anthropogenic management. This study helps to understand the response of nutrient content of northeastern black soils to different anthropogenic management models and provides reasonable references for sustainable farming patterns.

Perfectly hexagonal sponge‐like NiO‐NiCo2O4 with rich electromicrostructural physiognomies for high‐efficiency electrocatalytic urea oxidation

In order to design high‐efficiency electrocatalysts for the development of urea oxidation reaction (UOR)‐based energy conversion and storage systems, herein, a very facile and kinetically controlled material growth strategy has been strategized to prepare extremely uniform and perfectly hexagonal sponge‐like NiO‐NiCo2O4 with high BET surface area (126 m2 g−1) and monomodal distribution of mesopores (~3.9 nm). The in‐depth electrochemical analyses demonstrate rich redox reversibility, high UOR current density, very‐low charge transfer and series resistance, and typical Warburg response indicative of facilitated diffusion of electrolyte ions during electrocatalytic UOR. The NiO‐NiCo2O4 requires lower overpotential for effective UOR and exhibits minimal current loss for prolonged duration. Proposedly, the multiple oxidation states of Ni and Co in NiO‐NiCo2O4, combined with its rich physicoelectrochemical physiognomies, offer lowly‐impeded electrolyte ion intercalation‐deintercalation, good electronic conductivity, higher number of accessible redox active sites, facile adsorption of urea on the electrocatalytic sites and inhibition in the blockage of active sites by side products to augment the overall UOR kinetics. The optimized approach presented in this study is poised to advance the catalyst systems for UOR, which will lead to the development of high‐efficiency urea‐based energy conversion and storage systems for prospective integration in contemporary electronic architectures.

Modification of Uniaxial Stress–Strain Model of Concrete Confined by Pitting Corroded Stirrups

To investigate the impact of stirrup pitting corrosion on the stress–strain model of core concrete under compression, this study, based on existing corroded steel specimens, establishes a probabilistic model of the residual cross-sectional area distribution of steel bars to reasonably evaluate the effect of pitting on the mechanical performance of stirrups. Considering the tension stiffening effect in reinforced concrete, a time-dependent damage model of corroded steel bars in concrete was determined, and the existing stress–strain model of concrete confined by stirrups was ultimately modified, establishing a time-dependent constitutive model that incorporates the effects of stirrup pitting corrosion. A comparison with previous experimental results indicates that the revised model presented in this paper can appropriately reflect the changes in the mechanical performance of concrete confined by corroded stirrups. The results of this study can provide theoretical support for the refined numerical analysis of reinforced concrete structures under the erosion of chloride ions.

Enrichment Characteristics, Risk Evaluation, and Source Apportionment of Heavy Metals in Wabu Lake of Yangtze River to Huaihe River Water Diversion Project

Wabu Lake serves as a crucial junction within the Yangtze River to Huai River Water Diversion Project （Yangtze-Chao Lake-Wabu Lake-Huai River）, where clarifying the status and risk levels of heavy metal contamination in the lake is crucial for the aquatic ecological safety along the route of the project. Surface water and sediment samples from 24 locations in Wabu Lake were collected and analyzed for the contents of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd）. Risk evaluation and source apportionment of heavy metals in the lake were conducted using the human risk assessment （HRA） model, sediment quality guidelines （SQGs）, and positive matrix factorization （PMF） method. The results showed that the average concentrations of heavy metals （except for Cd and Pb） in the surface water were lower than the class III limits of the surface water quality standard （GB 3838-2002）. The average concentration of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd） in interstitial water were lower than the Criteria Continuous Concentration （CCC） of the National Recommended Water Quality Criteria. The average contents of As, Zn, Pb, Ni, Cu, Cr, and Cd in the sediment exceeded the background values by factors of 1.78, 1.85, 1.42, 1.77, 1.41, 1.38, and 3.46, respectively. In addition, the heavy metals Pb, Cd, and Cr in the Wabu Lake water posed severe risks to human health, with the cancer risk （CR） value for Cr reaching 6.14×10-5. The sediment quality guidelines （SQGs） indicated that the probability of adverse effects on lakes by As, Ni, and Cr was relatively high. The spatial distribution of risk areas showed that the risk control zones for As, Cr, and Ni in Wabu Lake covered almost the entire lake area. The PMF analysis indicated that the primary sources of heavy metals in Wabu Lake were exogenous inputs from industrial, agricultural, and residential activities in surrounding areas, followed by endogenous pollution caused by enclosed aquaculture. The subsequent focus should be on the pollution of heavy metals Cr, Cd, and Pb in the overlying water of Wabu Lake as well as the sediment ecological safety of As, Cr, and Ni in the sediment.

Geoinformation monitoring of the condition of rice fields in Giang Province (Vietnam) according to multispectral ERS data and field spectroradiometering

The spatial distribution of areas of territory used for agricultural work is of great importance for the development of measures for managing territories and planning the rational use of land and water resources. As a result of the high development of land and its use for agricultural production, timely assessment of both the condition of soils and the growing season of crops in the fields plays an important role. Since in the conditions of the study area it is possible to choose the timing of sowing, growing and harvesting, spatial data on the location of fields for growing rice can be used to estimate the volume of water consumed for its cultivation and develop a crop rotation model for different volumes of available water based on the level of water reserves of the main crop. source. Geoinformation classification of Earth remote sensing data and the use of spectral indices can be used to monitor the dynamics of rice crop formation under existing conditions. Refinement of the results of geoinformation processing of satellite images is carried out using field standardization methods, including photo standardization, showing the real value of reflected energy during large-scale photography, and spectroradiometry, which makes it possible to determine the characteristics of the reflected energy by these objects in various spectral ranges. The results obtained made it possible to obtain statistical data on the values of the area of plots based on 4844 measurements; the average values of the area of plots were established – 0.447, standard deviation – 0.309, maximum area – 5.84 hectares, minimum 0.02 hectares. The obtained statistical results make it possible to determine on average the estimated number of plots in the territory of An Giang province, which is 442 thousand fields. Thus, when deciphering images of rice fields on satellite images, local geoinformation cartographic layers of such fields are developed, taking into account the actual characteristics and stage of plant vegetation, as well as the stage of harvesting and the stage of tillage.

The impact of artificial intelligence on economic development: ethical aspects of technology implementation and development

Relevance. Digitalization is one of the main priorities of the economy. Machine learning and artificial intelligence technologies are introduced by advanced companies into production and management processes, and change the nature of their activities and the way they do business. New issues related to the legislative regulation of technologies, the distribution of areas of responsibility between humans and robotic systems arise, and legal relations that are not provided for by the regulatory framework appear. Technological development also leads to a profound transformation of society. New mechanisms of interaction raise a number of ethical issues. In this regard, the topic of research on the introduction of artificial intelligence is becoming increasingly popular in the scientific community.The purpose of the article is to study the features of the introduction of digital systems, including those which are based on artificial intelligence, as well as to systematize the ethical problems of using such technologies.Objectives: analyze the regulatory framework for the regulation of artificial intelligence systems, determine the basic principles of its development and implementation, consider the main problems arising in connection with the expansion of the scope of artificial intelligence, as well as options for their solutions.Methodology. To achieve the goal in the research are used methods of system analysis and synthesis, bibliographic analysis. The works of domestic and foreign scientists devoted to AI, the regulatory framework of the federal and regional levels, materials of scientific journals and conferences have become the information base of the study.Results. A literary review of modern scientific research on ethical issues of implementation is presented in the article, the main normative acts used both in international practice and developed in Russia are considered.Conclusions. The systematization of the principles and ethical problems of the introduction of artificial intelligence systems based on the analysis are presented. The use of new technology has advantages, but it is necessary to create a legislative and institutional framework that will minimize risks and achieve sustainable development goals.

The Mycobiota Diversity Associated with Mistletoe (Viscum album L.) in Poland

The spread of mistletoe, which mainly affects Scots pine in Europe’s managed forests, has become a serious ecological and economic problem, exacerbated by climate change and drought stress. Research into mistletoe-associated mycobiota provides crucial insights into the biology, ecology and distribution of these fungi and forms the basis for sustainable plant protection and effective management strategies. The aim of this study was to identify the fungal species associated with mistletoe (Viscum album L.) at 75 sites across Poland, with a focus on the northeastern regions of the country. The field work included a systematic and targeted search using the transect method, followed by laboratory analysis, during which fungal samples were prepared and examined under both light and scanning electron microscopes. The genetic analysis was carried out using ITS sequencing to confirm the presence of pathogen-specific fungi in the majority of V. album samples examined. The results showed four fungal species associated with the mycobiota of mistletoe: Sphaeropsis visci, Plenodomus visci, Cylindrodendrum hubeiense and Septoria krystynae. It is noteworthy that two of these species (P. visci and C. hubeiense) have not yet been detected in Poland. Based on these findings, the distribution areas of the identified fungal species were mapped accordingly.

National Park Double Boundary Delimitation: A Synergy-Based Approach Integrating Biodiversity and Ecosystem Services—An Example of Proposed Ailaoshan–Wuliangshan National Parks in China

The demarcation of national park boundaries is crucial for comprehensive planning, effective management, and maintaining the integrity of ecosystems and biodiversity. This research uses the proposed ‘Ailaoshan–Wuliangshan’ National Park (AWNP) in Yunnan Province, China, as the study area and adheres to the principles of systematic conservation planning (SCP). It employs the Marxan 2.43, MaxEnt 3.4.4, and InVEST 3.14.2 models to predict suitable distribution areas for key endangered species within the AWNP, identifies core ecological source areas, priority conservation areas, and conservation gaps, and constructs a double boundary protection framework. The study’s findings indicate that the potentially suitable habitats for the major rare and endangered species, as predicted by the MaxEnt model, are predominantly located in the Ailaoshan and Wuliangshan areas, with a smaller portion distributed in the Konglonghe area. The InVEST model assessment of habitat quality revealed that the total area of the core ecological source areas is 4775.26 km2, accounting for 35.34% of the total study area. The Marxan model identified a total area of 1064.22 km2 as priority conservation areas, constituting 7.90% of the total study area. Additionally, it revealed conservation gaps of 302.1 km2, which represent 2.20% of the total area. Ultimately, by integrating biodiversity conservation and ecosystem services, the boundaries of the AWNP were optimized into a double boundary delineation model: the inner boundary, characterized by rigid control, spans an area of 1076.20 km2, while the outer boundary, characterized by elastic management, covers an area of 3056.92 km2. Corresponding management recommendations are proposed for the different areas. The double boundary delineation method proposed in this study can, to a certain extent, reconcile the conflict between biodiversity conservation and resource utilization, providing an appropriate reference for the demarcation and dynamic management of national park boundaries in China.

Applying Multi-Objective Evolutionary Computation for Optimal Feng Shui Layout in Interior Design

Feng Shui principles have a profound impact in Asia, studies have shown that consumers often consider Feng Shui when purchasing property to arrange interior layouts. Balancing design requirements and cultural beliefs in the design process requires significant communication and calculation efforts, However, aside from repeated communication among Feng Shui experts, homeowners, and designers, there is currently a lack of efficient methods to incorporate Feng Shui into design. Therefore, this study establishes a decision model to provide layout recommendations for purchase property, design, and for existing property renovation planning. By references Feng Shui Compass School principles to assess the Feng Shui quality of dwelling interiors and considers spatial layout and area distribution rules to evaluate the feasibility of the solution. Multi-Objective Evolutionary Algorithm (MOEA) is then applied to optimize Feng Shui and design conditions in real-world case studies. The results show that the application can effectively optimize and balance Feng Shui and design conditions in a short period of time, also provides homeowners and designers with clear strategies during purchase, design, and renovation to meet the needs related to cultural beliefs.

Properties optimisation of nanostructures via machine learning: Progress and perspective

Nanostructures play a vast role in the current Internet of NanoThings (IoNT) era due to remarkable properties and features that precisely impart their desired application functions in catalysis, energy and other fields. The exploration in understanding their minute features caused by the flexibility of compositional and complex atomic arrangement from the synthesis reaction widely opens for the in-depth discovery of their search space such as particle size, morphology and structures that controlled the characteristics. A wide range of possible compositions and various lattice atomic arrangements combined with small particle size distribution and large surface area create grand challenges to copy/differentiate their corresponding specific properties. Thus, the employment of machine learning (ML)-based strategies using the closed-loop experimental data from the nanostructure synthesis to help navigate and optimise for the large classes of data attributes related to the size, morphology and other properties from the trained model are reviewed. The data attributes are assisted by discussions of the selected case studies from the recent literature that highlight different condition nanostructures. The review concludes with a discussion of perspectives on the major challenges in the implementation of ML data-driven design in the field of nanostructure synthesis.

The Vendian and Early Cambrian of the Southwest, West and Northwest of the Eastern European Platform: categories of rivers that formed sedimentary

Based on data on the geochemical characteristics ((La/Yb)N, Eu/Eu* and content of Th) of clay rocks of Podolian Transnistria, South-Eastern Poland, Belarus and Volyn, Lithuania, the vicinity of St. Petersburg, South-Eastern White Sea region and Arctic Norway, categories of rivers that transported fine-grained aluminosiliconclastics to sinks that existed during the Vendian and Early Cambrian were reconstructed in the west of the East European Platform. The distribution of data points of clay rocks of the Volynian time on the (La/Yb)N–Eu/Eu* diagram with the fields of the composition of pelitic fraction of bottom sediments in the estuary of modern rivers of various categories allows us to assume that for Lithuania and Podolian Transnistria the feeding provinces were, among other things, paleowatersheds composed of volcanic formations. For Eastern Belarus and the South-Eastern White Sea region, the influence of erosion products supplied by rivers flowing through igneous/metamorphic terranes (crystalline basement) is noticeable. A significant part of the clastic material was carried by rivers that drained sedimentary formations, as well as large rivers, i.e. rivers whose length exceeded 1000 km and whose drainage area was more than 100,000 km2. The existence of the latter is confirmed by the presence of detrital zircon in the rocks of the Zhukov Formation of South-Eastern Poland, borrowed possibly from the rocks of Fennoscandia. In the Redkinian time, along with large rivers (rivers category 1) and rivers fed by the products of erosion of sedimentary formations (rivers category 2), transport of fine-grained clastics was also carried out by rivers that drained rocks of the crystalline basement ((rivers category 3; this is typical of Belarus and Volyn, the South-Eastern White Sea and Arctic Norway) and by rivers flowing through areas of distribution of volcanic associations (rivers category 4). The distribution of data points of the clay rocks of the Kotlin stage on the (La/Yb)N–Eu/Eu* diagram suggests that the main agents of transport of fine-grained aluminosiliconclastics to sinks at that time were large rivers and rivers fed by fine particulate matter due to the erosion of predominantly sedimentary rocks. The Early Cambrian paleowatersheds were apparently composed not only of crystalline rocks, but also of sedimentary formations. All of the above is in fairly good agreement with the previously established fact of a gradual increase in the contribution of erosion products of sedimentary rocks to the formation of Vendian–Early Cambrian sedimentary sequences from Podolian Transnistria to Arctic Norway.

Advanced montane bird monitoring using self-supervised learning and transformer on passive acoustic data

Passive acoustic monitoring combined with deep learning-based bird sound classifiers is an effective tool, particularly in remote areas. While self-supervised learning has recently excelled in natural language processing and image recognition, its application to bird sound recognition remains limited. This study proposes an innovative self-supervised learning approach, which leverages vast amounts of passive acoustic recordings for pre-training, followed by fine-tuning of target species. Compared to the three state-of-the-art models based on transfer learning from ImageNet, the proposed method demonstrated improvements across all species, with even more significant gains for tail-end species. These results confirm that domain-specific pre-training in self-supervised learning enhances downstream recognition tasks and provides greater robustness, benefiting tail-end species in imbalanced ecological datasets. Our experiments further demonstrate that integrating open-source datasets and data augmentation techniques is the most effective strategy for mitigating data imbalances and cross-domain issues. In addition, introducing a ‘catch-all’ category into training datasets has been shown to improve model robustness in open set recognition scenarios. We also identified the minimum viable sample size requirements for our proposed model and explored the impact of overlapping bird vocalizations during dawn choruses on model performance. Targeting 31 bird species in the montane regions of subtropical Taiwan, the model achieved a class-wise mean average precision of 0.782 and an overall precision of 85.6 % at the F0.5 threshold in dawn chorus soundscape recordings. This study confirms the effectiveness and advantages of self-supervised learning in bird sound recognition, supporting long-term monitoring of bird distribution and vocal activity in remote montane areas.

Assessing the impact of land cover on air quality parameters in Jordan: A spatiotemporal study using remote sensing and cloud computing (2019–2022)

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively. The Google Earth Engine (GEE) platform and JavaScript were used to produce monthly short-term average concentration maps and time series for the three pollutants. Pearson correlation analysis was performed to evaluate the performance of Sentinel-5P data against ground-based monitoring stations in estimating NO2, SO2, and CO concentration at a regional scale. Results revealed a moderate correlation, with r-values of 0.42, 0.43, and 0.40, for NO2, SO2, and CO, respectively. The spatiotemporal analysis showed a higher concentration of SO2 and NO2 in the northern and middle regions of the country, coinciding with the spatial distribution of built-up areas and the main urban centers. On a temporal scale, the highest concentration of the three pollutants was observed in the winter months for all governorates of Jordan. For instance, it was found that the highest value of NO2 was in Balqa Governorate in December 2022, 1.57 * 10^4 mol/m2. The highest average monthly SO2 values were observed in Jerash Governorate in December 2019, 7.36 * 10^4 mol/m2. CO concentrations were mainly concentrated in the western parts of the Jordan rift valley.

The contributions of hazard and exposure to flood risk in Shenzhen City

Flood risk strongly depends on the hazard and the exposure of people and assets. With the continuous growth of the urban population, exposure is contributing more and more to urban flood risk. Meanwhile, climate warming has led to frequent extreme precipitation events, resulting in severe flood hazards in cities. However, the individual contributions of hazard and exposure to flood risk remain unclear, hindering effective risk reduction strategies in metropolitan areas. This study examines the contributions of pluvial flood hazard and population exposure to the population mortality risk, as well as the contributions of pluvial flood hazard and urban built-up area exposure to the economic loss risk in the Shenzhen metropolitan area. The results indicate that the concentration of population and assets in central Shenzhen increases the pluvial flood risk. With almost the same water depth, the Longgang, Longhua, and Luohu districts exhibit higher death tolls and asset losses compared to the Guangming district. Additionally, changes in pluvial flood hazard also significantly contribute to the variation in economic loss risk, which could explain up to 84% of the observed fluctuations. With the existing distribution of population and urban built-up areas, pluvial flood risk in Shenzhen is expected to increase in the future as extreme precipitation intensifies.

Vegetation Changes in the Arctic: A Review of Earth Observation Applications

The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing by up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for the global climate and energy balance. Satellite remote sensing is a valuable tool for monitoring Arctic vegetation dynamics, particularly in regions with limited ground observations. To investigate the ongoing impact of climate change on Arctic and sub-Arctic vegetation dynamics, a review of 162 studies published between 2000 and November 2024 was conducted. This review analyses the research objectives, spatial distribution of study areas, methods, and the temporal and spatial resolution of utilised satellite data. The key findings reveal circumpolar tendencies, including Arctic greening, lichen decline, shrub increase, and positive primary productivity trends. These changes impact the carbon balance in the tundra and affect specialised fauna and local communities. A large majority of studies conducted their analysis based on multispectral data, primarily using AVHRR, MODIS, and Landsat sensors. Although the warming of the Arctic is linked to greening trends, increased productivity, and shrub expansion, the diverse and localised ecological shifts are influenced by a multitude of complex factors. Furthermore, these changes can be challenging to observe due to difficult cloud cover and illumination conditions when acquiring optical satellite data. Additionally, the difficulty in validating these changes is compounded by the scarcity of in situ data. The fusion of satellite data with different spatial–temporal characteristics and sensor types, combined with methodological advancements, may help mitigate data gaps. This may be particularly crucial when assessing the Arctic’s potential role as a future carbon source or sink.

High-performance computing in urban flood modeling: A study on spatial partitioning techniques and parallel performance

China has been frequently afflicted by urban flooding incidents in recent years. Using hydrodynamic models to simulating urban flooding process is an effective measure in flood control. However, many existing researches of hydrodynamic models confine its scope to relatively small areas or low-resolution limited by computing resources and model performance. This paper introduces a parallel model based on hydrodynamic principles and MPI, designed for simulating urban flooding runoff processes on the powerful computing resources of a national supercomputing center in China. Detailed simulations of the runoff process and the distribution of flood-prone areas within the campus of Tsinghua University under varying storm conditions are studied. Six distinct 2D parallel partitioning arrangement schemes are tested and timed for performance assessment. A comprehensive analysis is conducted on each partitioning scheme’s parallel performance. The findings indicate that the model demonstrates near-linear speed-up with remarkable efficiency. This model holds the potential for practical application in large-scale parallel computing, offering significant advancements in flood prediction.

An improved body force model based on a data-driven region-segmentation combinational loss model for a transonic fan rotor under uniform and non-uniform inflow

Boundary layer ingestion (BLI) fans are required to continuously operate under distorted inflow conditions, which severely reduces the fan's efficiency. Therefore, it is necessary to focus efforts on designing a high-performance distortion-tolerant fan. During the preliminary design stage of the fan, it is inevitable to repeatedly evaluate the aerodynamic performances of different design cases through low-fidelity computational approaches. However, the predictive accuracy of these low-fidelity computational methods generally depends on the precision of the loss models integrated into them. These loss models are built using simplified physics or human experience, making it difficult to depict the complex non-linear mapping relationships between losses and their influencing factors under BLI inflow distortion. In this work, a high-accuracy data-driven based region-segmentation combinational loss model is used for the first time to attempt to improve the overall prediction accuracy of the body force model. As a low-fidelity computational approach, the body force model has the ability to predict the spatial distribution of three-dimensional, non-uniform flow under inflow distortion conditions. Various operating conditions, including different rotational speeds, radial inflow, and BLI-distorted inflow, are selected to test the predictive accuracy of this improved body force model based on a region-segmentation combinational loss model. The results show that, compared with the one based on traditional loss prediction approach, the improved body force model in this work has shown higher prediction accuracy for the fan adiabatic efficiency characteristic curve. In addition, further evaluations of the flow loss spatial distributions under BLI inflow distortion conditions indicate that the body force model based on region-segmentation combinational loss model can more accurately capture the radial loss distributions at different circumferential locations. Specifically, for the loss distribution in the tip area, it can capture the sharp increase in variation trend near the end wall, and the average prediction errors can be reduced by more than 10% at different annulus locations under various BLI inflow distortions. Meanwhile, this improved body force model also enhances the accuracy of predicting the loss distribution in the circumferential direction at different span positions, including more accurately capturing the circumferential positions corresponding to the maximum and minimum flow losses.

Novel application of Ru-based catalysts on MgAl oxides alkaline adsorbents for cyclic CO2 methanation

This study explores the performances of Ru-based catalysts with a low metal content (2 wt%) supported on MgO and Mg-Al Oxides (MgAl) for cyclic CO2 adsorption and methanation at atmospheric pressure. Adsorption and desorption tests demonstrated that MgAl-based catalysts are more promising for CO2 capture due to their larger surface area and better distribution of active sites (Mg2+–O2-). Moreover, doping the MgAl support with K2CO3 further improves surface alkalinity and, consequently, capture performance. During cyclic operations, all the catalysts proved effective and selective for methane production. To simulate realistic conditions, both dry and wet CO2 adsorptions were conducted before the methanation stage. The presence of moisture positively influenced gas carbonation for all catalysts, increasing the overall amount of CO2 captured. Specifically, Ru/MgAl exhibited the best performance in terms of adsorption and conversion to methane (approximately 85 % after dry adsorption and 79 % after wet adsorption), with a maximum methane production of 183 and 220 μmolCH4 g−1, respectively. The reaction yield was further enhanced with Ru-K/MgAl, achieving 327 μmolCH4 g−1 after dry adsorption and 333 μmolCH4 g−1 after wet adsorption. However, this catalyst displays different conversion kinetics, attributed to slowed carbonate migration, low Ru dispersion, limited specific surface area, and excessive carbonation strength. Operando FTIR tests revealed differences in reaction intermediates between Ru/MgAl and Ru-K/MgAl, by going deeper into the kinetic differences observed. The study concludes that Ru/MgAl materials are highly promising catalysts for CO2 adsorption and methane production, supporting the development of technologies for CO2 abatement and renewable energy utilisation.

Impacts of Climate Change Conditions on the Potential Distribution of Anoplophora glabripennis and Its Host Plants, Salix babylonica and Salix matsudana, in China.

The Anoplophora glabripennis (Motschulsky) is a phytophagous pest that is seriously endangering Salix babylonica Linn. and S. matsudana Koidz. Poor control can damage local ecosystems, resulting in economic losses and management risks. In the context of climate change, the climatic ecological niche of organisms is no longer compatible with the surrounding environment. To mitigate the effects of climate change, some organisms respond adaptively to climate change through different mechanisms and in different ways. In this study, an optimized MaxEnt model was used to explore the potential distribution areas of A. glabripennis and its host plants, S. babylonica and S. matsudana, in response to current and future climate and to determine their movement routes and relative dynamics. The results show that the optimized model exhibits the lowest complexity and excellent prediction accuracy. It is important to note that both temperature and precipitation are the main environmental factors affecting the distribution of suitable habitats for A. glabripennis and its host plants. This is evidenced by the mean temperature of the warmest quarter and precipitation of the wettest month being the main environmental factors affecting the distribution of suitable habitats for the host plants. Similarly, the minimum temperature of the coldest month and precipitation seasonality are the primary bioclimatic variables constraining the dispersal of A. glabripennis. Under climate change, the suitable areas of both S. babylonica and S. matsudana are declining, while the suitable areas of A. glabripennis are expanding in future climates. Furthermore, three species exhibited a proclivity for migration to higher latitudes in response to climate change. In conclusion, this study contributes to our understanding of the biogeographic characteristics of these A. glabripennis, S. babylonica, and S. matsudana and provides a basis for the formulation of timely conservation strategies to reduce the potential impacts of climate change. This is of great significance for the rational management, utilization, and protection of forest ecosystems in China.