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The technique of refracturing is an effective method to solve the rapid decline in oil well production caused by factors such as severe reservoir energy loss and fracture failure after the initial hydraulic fracturing of low-permeability reservoirs. The key to designing refracturing lies in establishing a model for evaluating the potential fracturing layers. Based on the geological characteristics of the low-permeability conglomerate reservoir in the Lower Wuerhe area of the Eig District of the Xinjiang Oilfield, this paper studies the influence of different development approaches on the distribution pattern of remaining oil in the reservoir. A coupled model of remaining oil distribution and the in situ stress field is established and discusses the characteristics of the four-dimensional in situ stress field under different development modes. This paper analyzes the influence of geological factors and well network factors on the distribution of residual oil, and analyzes the influence of various factors, such as reservoir properties and injection and extraction parameters, on ground stress. Based on the residual oil distribution and ground stress changes, an evaluation method for screening potential fractured layers in reservoirs with different development modes (water injection development and depletion development) is developed. [ABSTRACT FROM AUTHOR]

The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2. [ABSTRACT FROM AUTHOR]

The Awulale Iron Metallogenic Belt (AIMB) has developed many medium–large iron deposits, of which the Zhibo iron deposit is selected as the research object in this paper. The Zhibo deposit's ore primarily consists of magnetite as the main mineral, accompanied by extensive epidotization. The mineral assemblage includes diopside, albite, actinolite, epidote, chlorite, K-feldspar, quartz, calcite, chalcopyrite, and pyrite. Magnetite is classified into two groups based on sulfide content and mineral assemblage (MagI for sulfide-free and MagII for sulfide-rich ores). Two-stage mineralization of magnetite has been identified based on mineral assemblages and paragenesis, including the magmatic stage MagI and hydrothermal stage MagII. Mag I shows inhomogeneous backscattered electron (BSE) textures and consists of BSE-light and -dark domains (Mag I-L and MagI-D). Seven subtypes of magnetite have been recognized in this deposit. MagI-L and MagI-D have formed in the magmatic stage and show BSE images in light and dark colors, respectively. MagI-L is anhedral to subhedral and is inclusion-free. MagI-D has mainly replaced MagI-L along fractures and contains inclusions and pores. MagII has formed in the hydrothermal stage and is characterized by coupled dissolution–reprecipitation (DRP) textures. It can be divided into five sub-generations, that is, MagII-1, MagII-2, MagII-3, MagII-L, and MagII-D. MagII-1, MagII-2, and MagII-3 comprise the core–mantle–rim texture, while MagII-L and MagII-D comprise the core–rim texture. MagII-1 is BSE-light and is enriched with inclusions and pores. MagII-2 has partly replaced MagII-1 and exhibits oscillatory zoning under BSE imaging. It also contains inclusions. BSE-light MagII-3 occurs as overgrowth along MagII-2 margins and is inclusion-free. MagI magnetite is enriched with V, Cr, and Ni, whereas MagII is enriched with W, Ta, Nb, Sr, Sb, Sn, Y, Zr, Mg, Al, and Ti, indicating a decreased temperature of magnetite formation. MagI-L crystallizes from the original magma, while MagI-D is formed from the residual magma enriched with incompatible elements. MagII crystallizes from later multiple hydrothermal activities through the dissolution of early magnetite and the re-precipitation of later magnetite or from MagI-D which has later undergone a hydrothermal overprinting process. According to the texture and chemical composition of magnetite from the Zhibo deposit, we suggest that the Zhibo iron deposit was formed from the initial magmatic origin and then underwent a hydrothermal overprinting process. [ABSTRACT FROM AUTHOR]

This paper presents a study on the mechanical properties of cement-stabilized steel-slag-based materials under freeze–thaw cycles for a highway project in Xinjiang. Using 3D scanning technology the specimen model conforming to the real steel slag shape was established. The objectives of the study are as follows: to explore the sensitivity between the macro- and micro-parameters of the specimen and to establish a non-linear regression equation; and to study the changes in mechanical properties of materials under freeze–thaw cycles, fatigue loading, and coupled freeze–thaw cycle–fatigue loading. The results show that there are three stages of compression damage of the specimen, namely, linear elasticity, peak plasticity, and post-peak decline. Maximum contact forces between cracks and particles occur mainly in the shear zone region within the specimen. The compression damage of the specimen is a mixed tensile–shear damage dominated by shear damage. When freeze–thaw cycles or fatigue loads are applied alone, the flexural strength and fatigue life of the specimens show a linear relationship of decline. The decrease in flexural modulus at low stress is divided into the following: a period of rapid decline, a relatively smooth period, and a period of fracture, with a tendency to change towards linear decay with increasing stress. In the case of freeze–thaw–fatigue coupling, the flexural modulus of the specimen decreases drastically by about 50% in the first 2 years, and then enters a period of steady decrease in flexural modulus in the 3rd–5th years. [ABSTRACT FROM AUTHOR]

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately. [ABSTRACT FROM AUTHOR]

Habitat selection has been a central focus of animal ecology, with research primarily concentrating on habitat choice, utilization, and evaluation. However, studies confined to a single scale often fail to reveal the habitat selection needs of animals fully and accurately. This paper investigates the wintering whooper swan (Cygnus cygnus) in Manas National Wetland Park, Xinjiang, using satellite tracking to determine their locations. The Maximum Entropy model (MaxEnt) was applied to explore the multi-scales habitat selection needs of Manas National Wetland Park's wintering whooper swans across nighttime, daytime, and landscape scales. This study showed that the habitat selection of the wintering whooper swans varied in different scales. At the landscape scale, wintering whooper swans prefer habitats with average winter precipitations of 6.9 mm and average temperatures of −6 °C, including water bodies and wetlands, indicating that climate (precipitation and temperature) and land type (wetlands and water bodies) influence their winter habitat selection. During daytime, whooper swans prefer areas close to wetlands, water bodies, and bare land, with a more dispersed distribution of water bodies. For nighttime, they tend to choose areas within the wetland park where human disturbance is minimal and safety is higher. This study can provide scientific basis and data support for habitat conservation and management of wintering waterbirds like whooper swans, recommending targeted conservation measures to effectively manage and protect the wintering grounds of whooper swans. [ABSTRACT FROM AUTHOR]

Current scholarship on the Uyghurs eclipses the fact that before the present moment of profound crisis, a robust polite society of the indigenous ethnonational communities existed in Xinjiang in the 1980s. This paper, by uncovering the little-known history of the Urumqi-based Tianshan Film Studio and its cinematic production of the non-Han population as the cultural majority of the region in the 1980s, adds to the academic literature that has firmly pushed against the state tropes of Xinjiang as a restive Muslim backwater with little skill in negotiating modernity. By intersecting institutional history, close reading of concrete filmic texts, and the larger historical context of what I argue as a decade of cultural rule in China's governance of its ethnic frontiers, this paper presents a fresh look at Uyghurs and their ethnic allies in the polite bargaining with the Chinese state for their cultural majority status in the region. [ABSTRACT FROM AUTHOR]

Oil shale, as a vast potential resource, is considered an important alternative energy source, and its effective development and economic utilization are of significant importance in alleviating the contradiction between energy supply and demand. Presently, the in situ conversion technology for oil shale has gained significant global attention, with numerous extraction methods undergoing active research and development. One of these methods is the in situ conversion of oil shale based on the hydration reaction of calcium-oxide-based composite material (CaO-CM). This approach harnesses the heat produced by the reaction between CaO-CM and water as a heat source for the pyrolysis of oil shale. This paper conducted experiments to assess the feasibility of temperature associated with this method. The feasibility study mainly includes two aspects: First, it is necessary to investigate whether the temperature generated by the hydration reaction of CaO-CM can meet the temperature requirements for the pyrolysis of oil shale. Through pyrolysis experiments of Xinjiang oil shale, the minimum temperature required for oil shale pyrolysis was determined to be 330 °C. High-temperature and high-pressure reaction vessels were employed to explore the temperature generated by the hydration reaction of CaO-CM. The results show that with the increase in environment pressure, environment temperature, and reaction mass, the maximum temperature generated by the hydration reaction of CaO-CM also increases (reach 455.5 °C), meeting the temperature requirements for the pyrolysis of oil shale. Second, the study evaluates whether the hydration reaction of CaO-CM can induce pyrolysis hydrocarbons of the oil shale. Through the pyrolysis experiments of oil shale based on the hydration reaction of CaO-CM, the changes in the content of pyrolysis hydrocarbons (S2) in oil shale before and after pyrolysis are measured. The results show that under 10 MPa pressure, the content of pyrolysis hydrocarbons in the oil shale decreased from 40.96 mg/g to 0.08 mg/g after pyrolysis. This confirms the feasibility of the temperature conditions for the in situ conversion of oil shale based on the hydration reaction of CaO-CM. [ABSTRACT FROM AUTHOR]

Since the issuance of the "Guiding Opinions on Vigorously Developing Sports Tourism" in 2016, the integration of sports and tourism has become a strategy in regional economic development. It creates new economic growth points, enhances local images, and promotes cultural communication. In the context of the "Tourism Makes Xinjiang Thrive" strategy, quantitatively investigating the integration of the sports and tourism industries helps people to better understand their interaction which can serve as the valuable input in policy-making for the comprehensive development of a region. This paper uses entropy weight method, stochastic frontier analysis and coupling coordination model to quantitatively analyze the effect of sports tourism industry integration in Xinjiang from the perspective of integration path. Meanwhile, the Dagum Gini coefficient and nuclear density estimation were used to analyze the regional differences and dynamic evolution of industrial integration quality. The result shows that (1) The sports and tourism integration quality in Xinjiang has not reached the optimal goal of complete integration. In the process of mutual industrial promotion, tourism promotes a higher degree of integration with the sports industry. (2) The industrial integration quality shows a phenomenon of "imbalance and inadequacy" among the regions. The regions with high quality of industrial integration were Urumqi, Ili, Kashgar, Altay and Changji, which have rich sports tourism resources. (3) The overall spatial difference in the quality of industrial integration presented a fluctuation downtrend. The difference between the tourism industrial belts was very significant. [ABSTRACT FROM AUTHOR]

Gossypol is a yellow polyphenol naturally produced by Malvaceae plants such as cotton, which mainly exists in roots, stems, leaves, and seeds of cotton. Gossypol has animal toxicity, and long-term intake of gossypol will accumulate in livestock and cause poisoning, resulting in decreased fecundity, organ lesions, and even death of animals. The cotton output in Xinjiang is huge, accounting for more than 80% of the whole country. After cotton harvest, most of the waste cotton straws are directly crushed and returned to the field, resulting in great waste of resources. Cotton straw is rich in crude protein, cellulose, hemicellulose, and lignin, which is equivalent to common crop straw in nutritional components. However, its gossypol content exceeded the standard, which limited the application of cotton straw feed. How to achieve efficient and low-cost detoxification of gossypol has become the key factor to determining the fodder of cotton straw. The structure, properties, toxicity, detoxification technology, and detoxification mechanism of gossypol were summarized in this paper, which provided a reference for feed utilization of cotton straw. [ABSTRACT FROM AUTHOR]

With the development of detrital zircon U–Pb ages and Hf isotope analysis as conventional sedimentological research methods, a large number of studies have been conducted in the Tarim, West Kunlun, Tajik, Pamir and Tianshuihai regions and abundant data have been accumulated. Summarizing these data to characterize sedimentary units and source regions is more important than local studies of specific geological epochs; therefore, we compiled a database of detrital zircon U–Pb ages and Hf isotope analyses from the Tarim, West Kunlun, Pamir, Tajik and Tianshuihai terranes. The database contains data from 90 papers, including 35,281 individual U–Pb ages and 4,181 Hf isotope analyses. The database records the literature source, petrologic sample information, regional geological information, geographic coordinates and analytical parameters for each data point in as much detail as possible to allow readers to review, process, analyse and use the data. The database relies on the Deep‐time Digital Earth (DDE) platform and uses an open database update system to enable sharing and collaborative building. We encourage other researchers to contribute relevant published data to facilitate wider use. [ABSTRACT FROM AUTHOR]

As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is crucial for maintaining vegetation growth and development, especially the improvement in the terrestrial ecological environment in arid areas under the background of climate change. However, there are few studies on the effect of spatial differences in detailed geomorphic types on vegetation distribution patterns. Therefore, this paper divides the Xinjiang hilly oasis zone into six geomorphologic level zones and innovatively investigates the influence of detailed geomorphologic types on the spatial distribution of vegetation and vegetation cover. Further, the area proportion of detailed landform types corresponding to different vegetation coverage in each geomorphic area was quantitatively calculated. Finally, the Geodetector method was used to detect the drivers of interactions between vegetation and the environment. The findings are shown as follows: (1) In the same climate zone, the spatial differentiation of landforms has a great influence on the vegetation distribution, manifesting as the significantly different vegetation distribution in different landform types. Grassland is the main vegetation type in the erosion and denudation of Nakayama; cultivated vegetation and meadows have a larger coverage in the alluvial flood plain and alluvial plain; and the distribution of vegetation in the Tianshan economic zone is characterized by obvious vertical zoning with the geomorphology. (2) The landform type and morphological types are the strongest driving factors for vegetation coverage with q values of 0.433 and 0.295, respectively, which effectually fill the gap caused by only using two terrain indicators, slope and elevation, to study the relationship between landforms and vegetation. (3) In addition, the improved nonlinear interaction resulting from the double factor of landform type and slope is 0.486, which has a stronger control on vegetation coverage than the single factor of landform type. These findings are conducive to enhancing the supply services of vegetation to the ecosystem in arid areas as well as providing important scientific guidance for the construction of ecological civilization and sustainable development in Xinjiang. [ABSTRACT FROM AUTHOR]

To realize the high‐efficiency utilization of iron‐rich coal, it is necessary to conduct in‐depth research on the ash fusion characteristics and regulation mechanism. In this paper, the ash fusion temperature test of iron‐rich coal in Xinjiang (ZC) under different atmospheres was first carried out. Then, red mud (HJ) from Huajin Aluminum Industry and black water pipe scale (GDG) were selected as the additives. After the same ashing procedure, the two additives were mixed with iron‐rich coal ash in different proportions to realize the regulation of the chemical composition of the coal ash to improve the ash fusion characteristic. The ash fusion temperature (AFT) experiment was carried out in the automatic ash melting point analyzer, and high‐temperature ash melting slags at different temperatures were prepared. The mineral phases of coal ash and slag were characterized by XRD and compared with the Factsage simulation calculation results to reveal the influence mechanism of coal ash fusion temperature. The result shows that the fusion temperature of ZC iron‐rich coal ash is significantly different between oxidizing atmosphere and weak reducing atmosphere. Compared with the oxidizing atmosphere, the four characteristic temperatures of ZC iron‐rich coal ash are significantly lower under weak reducing conditions, and the difference is about 150°C. The addition of red mud and pipeline scale increased the fusion temperature of ZC coal ash to varying degrees. The addition of HJ ash has little effect on the deformation temperature of ZC coal ash, while the addition of different proportions of HJ ash significantly increases the flow temperature of ZC coal ash. When the addition ratio of HJ ash is 40%, the flow temperature FT of ZC coal ash increases most obviously, which is about 50°C. The addition of HJ ash and GDG ash will lead to an increase in the content of iron‐containing spinel, thereby increasing the ash flow temperature. [ABSTRACT FROM AUTHOR]

Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of photovoltaic (PV) power prediction and the stability of power system, a short-term PV power prediction model based on hierarchical clustering of K-means++ algorithm and deep learning hybrid model is proposed in this paper. First, hierarchical clustering of the K-means++ algorithm is used to cluster historical data into different weather scenes according to different seasons. Second, a hybrid model combining convolutional neural network (CNN), squeeze-and-excitation attention mechanism (SEAM), and bidirectional long short-term memory (BILSTM) neural network is constructed to capture long-term dependencies in time series, and the improved pelican optimization algorithm (IPOA) is used to optimize the hyperparameters in the prediction model. Finally, an example for modeling analysis is conducted by using the actual output and meteorological data of a PV power station in the Ili region of Xinjiang, China. The effectiveness and accuracy of the proposed model are verified by comparing with LSTM, BILSTM, CNN-BILSTM, and POA-CNN-SEAM-BILSTM models, and the superiority of IPOA is verified by comparing with particle swarm optimization and whale optimization algorithm. The results show that the proposed model can obtain better results under different weather scenes in different seasons, and the prediction accuracy of the model optimized by IPOA is further improved. [ABSTRACT FROM AUTHOR]

Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth. [ABSTRACT FROM AUTHOR]

Satellite laser altimetry technology, a novel space remote sensing technique, actively acquires high-precision elevation information about the Earth's surface. However, the accuracy of laser altimetry can be compromised by alterations in the satellite-ground environment, thermal dynamics, and cosmic radiation. These factors may induce subtle variations in the installation and internal structure of the spaceborne laser altimeter on the satellite platform, diminishing measurement precision. In-orbit calibration is thus essential to enhancing the precision of laser altimetry. Through collaborative calculations between satellite and ground stations, we can derive correction parameters for laser pointing and ranging, substantially improving the accuracy of satellite laser altimetry. This paper introduces a sophisticated calibration method for laser altimeter pointing and ranging that utilizes dense control points. The approach interpolates discrete ground control point data into continuous simulated terrain using empirical Bayesian kriging, subsequently categorizing the data for either pointing or ranging calibration according to their respective functions. Following this, a series of calibration experiments are conducted, prioritizing "pointing" followed by "ranging" and continuing until the variation in the ranging calibration results falls below a predefined threshold. We employed experimental data from ground control points (GCPs) in Xinjiang and Inner Mongolia, China, to calibrate the GaoFen-7 (GF-7) satellite Beam 2 laser altimeter as per the outlined method. The calibration outcomes were then benchmarked against those gleaned from infrared laser detector calibration, revealing disparities of 1.12 s in the pointing angle and 2 cm in the ranging correction value. Post validation with ground control points, the measurement accuracy was refined to 0.15 m. The experiments confirm that the proposed calibration method offers accuracy comparable to that of infrared laser detector calibration and can facilitate the updating of 1:10,000 topographic maps utilizing stereo optical imagery. Furthermore, this method is more cost-effective and demands fewer personnel for ground control point collection, enhancing resource efficiency compared to traditional infrared laser detector calibration. The proposed approach surpasses terrain-matching limitations when calibrating laser ranging parameters and presents a viable solution for achieving frequent and high-precision in-orbit calibration of laser altimetry satellites. [ABSTRACT FROM AUTHOR]

To realize the high‐efficiency utilization of iron‐rich coal (ZC), it is necessary to conduct in‐depth research on its ash fusion characteristics and regulation mechanism. In this paper, the fusion temperature test of ZC coal ash was first carried out in a weak reducing atmosphere, and diatomite (GZT) and Naomaohu high‐calcium coal (NMH) were selected as additives. The results show that the four characteristic temperatures of ZC coal ash are all low under weak reducing conditions. The addition of NMH ash lowered the fusion temperature of ZC coal ash, while the GZT ash increased the fusion temperature to varying degrees. When the addition ratio of NMH ash is less than 40 %, the effect of increasing the difference between the deformation temperature and flow temperature of ZC coal ash is still positive. The addition of NMH ash made the slag system generate a high content of calcium‐magnesium feldspar and calcium‐iron feldspar, decreasing the fusion temperature of the blended coal ash. The addition of GZT ash increased the content of SiO2 in the slag system and promoted the increase of the iron‐pyroxene content, and the formation of the iron‐aluminum pyroxene and cordierite mineral phases, thereby increasing the coal ash fusion temperature. [ABSTRACT FROM AUTHOR]

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China's largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people's daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase. [ABSTRACT FROM AUTHOR]

Reservoirs are essential hydraulic facilities for water resource allocation in Xinjiang. Since the 1950s, many reservoirs have been constructed for oasis water resource utilization in Xinjiang, enhancing the storage capacity of water resources. There are a few intact and open reservoir archives containing both geolocations and hydraulic attributes, such as the reservoir completion year, which can facilitate our understanding of the correlation between hydraulic engineering and oasis expansion. This paper mapped all the reservoirs of Xinjiang using Sentinel-2 MSI images from 2022. It associated their attributes with the reservoir's extent, such as the capacity, area, complete year, altitude, etc., by consulting historical almanac data to establish a full elemental dataset with both geographic and attribute information. Furthermore, the spatial variability and historical process of the reservoirs were discussed against geomorphic information and oasis evolution. The results showed that 804 reservoirs were mapped cumulatively in Xinjiang up to 2022, and 1960–1980 and 2005–2010 are the rapidly developed periods. The construction history of the reservoirs indicates that reservoirs' locations have the spatial tendency to shift from being in oasis plain areas to mountainous areas, and the newly built reservoirs showed a trend of miniaturization in area and maximization in volume. [ABSTRACT FROM AUTHOR]

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation. [ABSTRACT FROM AUTHOR]

High-energy laser beams have a powerful ability to induce rock fragmentation, but there is still a lack of suitable calculation methods to quantitatively predict the laser-induced rock cutting slit depth. This work aims to provide a new predictive model for the quantitative determination of the laser rock cutting slit depth. In this paper, based on the energy consumption model of laser-induced rock breaking, the prediction model of laser cutting slit depth is creatively established by comprehensively considering the factors affecting laser cutting slit depth, such as laser power, moving speed, and spot radius. Then, relying on the three kinds of rocks exposed in the second phase of the Xinjiang water supply project, which is the longest water conservancy tunnel in the world, the prediction model is verified by laboratory tests, and the variation rules of slit depth, width and specific energy with laser parameters are explored. The results show that the slit depth of laser rock cutting is directly proportional to the laser power and inversely proportional to the moving speed and the defocusing amount. The prediction error for cutting slit depth of the model is less than 10% for palimpsest alkali feldspar granite, and less than 15% for granitic shallow granulite and siliceous schist. Under the same laser condition, the cutting slit depth of siliceous schist is about 25% larger than that of the other two kinds of rocks for its high content of plagioclase. The prediction model and experimental rules proposed in this paper are expected to further promote the application of laser cutting technology in the field of auxiliary TBM rock breaking. [ABSTRACT FROM AUTHOR]

Power stations operating on PhotoVoltaic (PV) power generation are challenged by demand forecasting as PV power generation is random and intermittent. This paper presents an Efficient Shrinkage Temporal Convolutional Network (ESTCN) model, which combines the Temporal Convolutional Network (TCN) and an improved Deep Residual Shrinkage Network (DRSN) to forecast PV power output. First, the attention sub-network of the DRSN is improved, the fully connected layer of the sub-network is canceled, and feature extraction is done directly on the model features following global average pooling using a one-dimensional convolution to obtain cross-channel interaction and increased model efficiency. Next, an improved attention mechanism and adaptive soft thresholding are introduced into TCN to automatically determine the noise threshold to address the issue of information weight dispersion caused by redundant information in the input samples. By incorporating dilated causal convolution, attention module, soft thresholding, and residual connection, the ESTCN model is formed and is shown to enhance PV power output prediction with only a minimal increase in the number of parameters. The power station in Ningxia, China is adopted as a validation example with data taken from the year 2020. The fitting and prediction results of the ESTCN model are compared against the Convolutional Neural Network, Long Short-Term Memory, and TCN models. The ESTCN model yielded the following values of the evaluation metrics: Root Mean Square Error (RMSE) of 1.47 kW, Mean Absolute Error (MAE) of 0.79 kW, and coefficient of determination of 0.999. Compared to the TCN model, the prediction errors based on RMSE and MAE improved by 0.39 kW and 0.18 kW, respectively. Applying the ESTCN model to predict PV power generation of power stations in two regions in China, Ningxia and Xinjiang, shows the ESTCN model to be superior, scalable, and universal over other deep learning models. • Study the problem of photovoltaic power using a deep learning method. • A new Efficient Shrinkage Temporal Convolutional Network model is presented. • Attention sub-network of DRSN is improved by cancelling the fully connected layer. • Feature extraction is done on the model features using a one-dimensional convolution. • Improved attention mechanism and adaptive soft thresholding are introduced into TCN. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium hirsutum L.) is one of the most salt-tolerant cash crops in the arid zone of Central Asia, especially in Xinjiang (XJ), China. Phosphorus (P) deficiency is one of the major yield-limiting factors for cotton production in this region. Although a high-chemical-P fertiliser input ensures high yields, only 18% of P can be taken up by crops in the application season, leading to large economic losses for farmers and accelerating the depletion of non-renewable P resources. It is a great challenge to optimise P inputs, increase P use efficiency (PUE) and maintain high yields under mulched fertigation cotton production systems. This paper aims to review the current efforts to improve PUE for cotton production in XJ and to provide feasible insights into nature-based solutions for high-P utilisation of cotton in the future. We focused on the following: (ⅰ) identifying the main factors that lead to the low PUE of cotton in XJ and the potential influencing mechanisms, (ⅱ) exploring innovative P management options to maximise the biological potential for improving high-P utilisation and reducing soil P surplus in mulched fertigation cotton production systems, (ⅲ) providing future perspectives of high P utilisation in mulched fertigation cotton production systems. We conducted a systematic literature search on the Web of Science and China National Knowledge Infrastructure (CNKI) to review publications that are related to the topic of cotton yield and P utilisation under different P management practices with field experiments in XJ. On the basis of the research, we proposed new insights into improving P management practices in mulched fertigation cotton production systems. We found that the soils exhibited a significant capacity for P fixation with high pH and carbonate content in XJ, leading to poor P migration when drip-irrigated. A mismatch between P fertiliser types and soil crop systems further hindered P absorption by cotton. Moreover, excessive P application and limited biological processes for soil P cycling impeded improvements in PUE. Therefore, we emphasised the importance of the root/mycorrhizal pathway in cotton P uptake and suggested using the high mycorrhizal dependence and P-efficient cotton genotypes, maximising the biological potential of the root morphology, mycorrhizal symbiosis, P-solubilising microorganisms and microbial biomass P (MBP) pool turnover in soil P mobilisation, which could increase cotton P uptake and PUE. Furthermore, we proposed a strategy of using starter P combined with ammonium sulfate fertigation after the plant emergency, and concentrated application of acidic P fertiliser with the pursuit of ammonium sulfate could achieve high cotton yields and P fertiliser efficiency. We also highlighted the potential of organic amendments in enhancing soil P bioavailability and P recycling by enlarging the MBP pool and improving the alkaline phosphatase activity, thus improving cotton P uptake and yield. Finally, we noted that nature-based solutions, which were commonly neglected in previous studies, should be integrated into zero-P surplus management strategies to maximise the biological potential for high-P utilisation in cotton production. The use of organic amendments enhanced the biological potential for the high-P utilisation of cotton. Combining starter P fertiliser with ammonium sulfate induced rhizosphere acidification and increased cotton yields and PUE. In addition, integrating nature-based solutions into zero-P surplus management strategies can increase cotton PUE and can maintain a high yield by maximising the biological potential for high-P utilisation. Overall, developing novel P management practices to optimise nature-based soil P cycling and to improve the biological potential for zero-P surplus can maximise the high yield and can underpin sustainable cotton production in an arid zone. • Starter P combined with ammonium sulfate fertigation can increase cotton yield and PUE. • Organic manure enhances the biological potential for high-P utilisation of cotton. • Innovative P management strategies can increase cotton PUE and maintain a high yield. • Zero-P surplus practice will focus on nature-based solutions in cotton production systems. [ABSTRACT FROM AUTHOR]

• A water security evaluation framework was established based on the DPSIR. • Five-element connection degree method was used to evaluate the water security. • Northwest China's water security developed to a very high level. • Five dimensions of water security showed a steadily improving trend. Since the the dawn of the 21st century, water security has occupied a pivotal position in fostering sustainable development. However, frequency of extreme weather events due to climate change and higher intensity of anthropogenic events, water security problems in Northwest China are becoming increasingly prominent. This paper elucidates water security levels and predicts future trends of Northwest China. Firstly, the Driving Force-Pressure-State-Impact-Response (DPSIR) conceptual framework served as the foundation for the establishment of the water security evaluation index system. Then used the entropy weight method to calculate the weight of indicators and the five-element connection degree method was used to assess the water security levels. The period for the data used in the study was from 2010 to 2019. Finally, a grey prediction model was employed to forecast the water security levels from 2023 to 2030. The findings showed that: (1) From 2010 to 2019, the water security situation gradually developed to a very high level in Northwest China: Shaanxi Province mostly attained a high level, and the water security grades of Ningxia Hui Autonomous Region and Xinjiang Uygur Autonomous Region were mostly at a very low level. (2) Generally, from 2023 to 2030, the water security level will continue to improve. The water security grades of Shaanxi, Gansu, and Qinghai will mostly be at the medium level, while that of Ningxia Hui Autonomous Region and Xinjiang Uygur Autonomous Region will be at the low level. (3) The five dimensions of water security in Northwest China demonstrated a fluctuating yet consistently upward trend. (4) Based on the results, suggestions were put forward regarding water security and sustainable development in Northwest China. [ABSTRACT FROM AUTHOR]

With the increasing proportion of variable renewable energy characterized by fluctuation and the promotion of the "Clean Heating" policy, the problem of seasonal energy imbalance of the system has become increasingly challenging. There is a lack of effective means to mitigate this challenge under the background of gradual compression of the construction space for traditional thermal units. Concentrated solar power (CSP) is a promising technology to replace thermal units by integrating emergency boilers to cope with extreme weather, and can meet long-time energy balance as a seasonal peak regulation source. In this paper, we propose a long-term high-resolution expansion planning model of energy systems integrating CSP to address seasonal energy imbalances. The model takes into account both investment and operation considerations in full hourly resolution for the whole year based on a fast cluster optimization method. With the projection to 2050, we take the energy system in Xinjiang province which is a typical area of the "Clean Heating" project with rich irradiance as a case study. It shows that the optimal deployment of CSP and electric boiler (EB) can result in a 8.73 % reduction in costs, a 19.72 % decrease in the peak-valley difference of net load, and a substantial 58.24 % reduction in renewable curtailment at 65 % renewable penetration compared to the base scenario. [ABSTRACT FROM AUTHOR]

With the continuous global increase in installed wind power capacity and subsequent surge in power generation, the contradiction between the safe operation of the grid and the efficient consumption of new energy after large-scale grid connection has become increasingly prominent. This paper presents a hybrid model prediction method to further improve the stability and accuracy of wind power prediction. Firstly, variational modal decomposition (VMD) optimized by the coati optimization algorithm (COA) is employed to decompose original wind power, mitigating the non-stationary characteristics of the power sequence. Subsequently, permutation entropy (PE) is used to recombine the decomposed components, and the combined feature selection method is achieved by integrating the Spearman correlation coefficient (SCC) and the autocorrelation function (ACF). Then, the multivariate combined model is constructed, and the improved multi-objective coati optimization algorithm (IMOCOA) determines the weight coefficients of each model to enhance the performance of the hybrid model. Finally, research and analysis are conducted from multiple scenarios and time scales using historical operating data from a wind farm in Xinjiang. The experimental results show that the proposed prediction model effectively improves the accuracy and stability of the wind power prediction compared with other popular prediction models. • A new hybrid model is proposed to implement wind power prediction. • An improved multi-objective coati optimization algorithm is introduced. • A combined feature selection method is presented. • A new weight coefficients determination method is proposed. [ABSTRACT FROM AUTHOR]

Textiles unearthed along the Silk Road are important material evidence of the spread and exchange of cultures in ancient Eurasia. This paper examines fibers and natural dyes in archaeological textiles from the Wei and Jin dynasties (220–420 CE) unearthed in Bazhou, a major route in the Xinjiang region of the ancient Silk Road. Optical microscope (OM), scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FT-IR), and proteomics analysis were used to examine and compare ancient and modern fibers. The result showed that the ancient textile samples tested consisted of silk, wool and cotton. Two species of silk were accurately identified, Bombyx mori silk and Bombyx mandarina silk. In addition, dyes were extracted from textile artifacts using a mild extraction method and analyzed by high performance liquid chromatography coupled with a diode array detector and a mass spectrometry (HPLC-DAD-MS). As a result, the identified red dyes can be divided into plant dyes and insect dyes, such as Rubia cordifolia , Rubia tinctorum and Kermes vermilio. The yellow dyes identified were derived from plants such as Reseda luteola , Phellodendron chinense , Phellodendron amurense , Berberis spp. The blue dyes identified in these textiles indicate the presence of indigo. These dyes are not only local, but also many exotic dyes from central or eastern China, and even from the Mediterranean coast. This study reveals the diversity of fibers and natural dyes used in the historic Lop Nor area of Bazhou, providing insights into their species origination and distribution. • Multi-analytical techniques were used for fiber identification in archaeological textiles from Bazhou, Xinjiang. • Bombyx mori silk and Bombyx mandarina silk were distinguished by proteomics analysis. • Plant and insect dyes were identified by HPLC-DAD-MS technique. • Exotic dyes from the Mediterranean coast were found in wool. • These fibers and dyes attest to the material spread of the Silk Road during the Wei and Jin dynasties. [ABSTRACT FROM AUTHOR]

This paper develops a method for intelligent identification and semi-automated extraction of 3D fracture parameters based on images in rock slope or underground engineering. Firstly, the exposed fracture trace is automatically identified by the enhanced deeplabv3 + model. Using transfer training methods, rock fracture images of multi-location and multi-type were used to train the model to improve the generalization ability. Secondly, rock sparse point cloud is generated using Structure from Motion (SfM) algorithm and dense reconstruction of rock is realized by Multi-View Stereo (MVS) method, which serve to transform the 2D fracture features to 3D. Finally, the coordinates of fracture feature points are obtained by fitting the planar features using algorithm. The methodology is validated through three types of data fields: (1) a simple and small-size laboratory installation is made up of two regular-shaped concrete blocks; (2) a slope with outcrop fractures in practical engineering; (3) a palm face from a tunnel project in Xinjiang, China. The error of the discontinuity parameters obtained using this method is about 1 %∼20 %, indicating that the proposed method is effective for parameter extraction and analysis of rock masses. [ABSTRACT FROM AUTHOR]

With the increase of wind power installed capacity, wind energy is playing an increasingly important role in power grids. However, this also aggravates the volatility of a power grid. Accurate wind power prediction is particularly important for grid dispatching and grid connection modes. However, the existing forecasting methods mostly consider the meteorological environmental factors and historical power data and do not consider the impact of wind turbine generator parameters. Therefore, this study proposes a double-layer Shared Weight Long Short-Term Memory Network (SWLSTM) prediction model based on manifold learning considering meteorological environment factors, wind turbine generator parameters, and historical wind power. First, 17 indicators such as meteorological environment factors and wind turbine parameters are determined as influencing factors to construct high-dimensional data sets. Second, manifold learning is used to reduce the dimension of high-dimensional data sets and determine the best dimension reduction. The data after dimensionality reduction and historical wind power are used as the input of the prediction model. Finally, considering the influence of errors on the prediction results, a double-layer SWLSTM prediction model is constructed. The double-layer model is constructed simultaneously, and the final power prediction result is obtained by adding the prediction error of the lower model and the prediction wind power of the upper model. Moreover, a wind farm in Xinjiang is used to verify the effectiveness of the proposed prediction model. The results show that the prediction strategy and the model proposed in this paper have high prediction accuracy. • Meteorological and wind turbine parameters are chosen as influencing power factors. • Manifold learning is used to reduce the dimension of high-dimensional input data. • A Double-Layer SWLSTM prediction model is proposed to predict wind power. • The proposed forecasting model has lower prediction errors and more robust. [ABSTRACT FROM AUTHOR]

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide. • An integrated framework combining DBN, SD, PLUS and InVEST models was proposed. • Impact of natural and socioeconomic factors on ESs was determined under climate scenarios. • The spatial pattern of ESs was optimized and partitioned using DBNs. • ESs in arid oasis urban agglomerations are mainly affected by natural factors. [ABSTRACT FROM AUTHOR]

Conventional high-density planting of machine-picked cotton in Xinjiang is associated with poor light distribution and low fiber quality. The objective of this study was to assess how different row spacing configurations, irrigation rates, and planting densities impact cotton photosynthetic production and fiber quality. We conducted a 3-year field experiment in Shihezi, Xinjiang, China, to evaluate the impact of different row spacing configurations and irrigation amounts on cotton photosynthesis and fiber quality. The four row spacing configurations used were: RS 66+10 H (high-density with 66 +10 cm spacing), RS 66+10 L (low-density with 66 +10 cm spacing), RS 76 H (high-density with 76 cm spacing), and RS 76 L (low-density with 76 cm spacing). Additionally, two irrigation amounts were tested: MD-T 3 (mulch drip irrigation with three tapes on one film) and MD-T 2 (mulch drip irrigation with two tapes on one film). We measured and compared the distribution of cotton canopy leaves, light, canopy apparent photosynthesis, reproductive organs and fiber quality among these groups. The results showed that RS 66+10 H had a higher lint yield than RS 76 L under MD-T 2 , with an increase of 5.4–7.1%. This can be attributed to the higher fraction of photosynthetically active radiation, canopy apparent photosynthesis (CAP), and boll biomass in RS 66+10 H between the full boll and boll opening stages. The maximum lint yield (3227–3580 kg ha−1) was obtained in RS 66+10 H under MD-T 3. Interestingly, RS 76 L had a similar lint yield to RS 66+10 H under MD-T 3 , mainly due to the middle and lower canopy having higher leaf area index (LAI) values ranging from 1.8 to 2.1. This resulted in increased CAP and cotton boll biomass of the middle and lower canopy compared to RS 66+10 H during the full boll to boll opening stage. Under MD-T 3 , RS 76 L also exhibited longer fiber length in the middle and lower canopy (30.2–32.0 mm and 31.6–33.3 mm, respectively) compared to RS 66+10 H, with a difference of 3.2–3.8% and 5.6–6.5%. Furthermore, RS 76 L demonstrated higher fiber strength in the middle and lower canopy (31.4–32.7 cN tex−1 and 33.2–33.9 cN tex−1, respectively) compared to RS 66+10 H, with a difference of 2.6–6.2% and 5.9–6.8%. Maintaining an LAI of around 2.0 during the transition from full boll to boll opening stage optimized canopy architecture and enhanced photosynthetic productivity. Under adequate irrigation, RS 76 L could be a suitable replacement for RS 66+10 H in order to improve cotton fiber quality by optimizing leaf-boll spatial distribution. This study provides guidance for selecting the appropriate row spacing configuration under different water conditions. RS 66+10 H is recommended for increasing yield under water-deficient conditions, while RS 76 L is suitable for improving fiber quality under adequate irrigation. [Display omitted] • Conventional high-density planting has poor fiber quality. • RS 76 L under adequate irrigation maintained high cotton yield and improved fiber quality. • Around 2.0 of LAI in the middle and upper canopy can maintain high photosynthetic rate. • RS 76 L maintained high yield and improved quality by optimizing leaf-boll spatial distribution. [ABSTRACT FROM AUTHOR]

• Synergistic effects of surface and groundwater on desert riparian vegetation were analyzed. • The cautionary depth to groundwater for vegetation expansion in the lower Tarim River is 4.0–5.0 m. • Flooding by surface water (lateral water transfer coverage) acts as the main driver for the expansion of vegetation in arid areas. • A zonal ecological water transfer strategy helps to promote effective ecosystem restoration in the region. Restoring degraded ecosystems in drylands is challenging because of low precipitation and high evapotranspiration. Water availability determines the existence and spatiotemporal distribution patterns of key endangered species struggling to survive in the severely damaged desert riparian ecosystem of the lower Tarim River (LTR) in NW China. Ecological water flow restoration measures with high costs and uncertainties have been implemented since 2000. The key task for scientists is to assess the success and drawbacks of this restoration project and propose specific recommendations for stakeholders to optimize the ecological benefits of the project in a cost-effective manner. In this study, Landsat time-series data from 1990 to 2020 were used to extract vegetation index values from river corridors along the LTR and explore the spatiotemporal patterns of desert riparian forest (DRF) vegetation. Based on land use and surface water data, we extracted the interannual spatial distribution range of vegetation and surface water, and selected several sample plots to demonstrate the influence of surface water and groundwater on vegetation change trends. The study results showed the following. 1) From 1990 to 1999, the vegetation slowly degraded; from 2000 to 2012, the extent of vegetation restoration was approximately 1 km from the river; and from 2013 to 2020, the extent of vegetation restoration at the Yingsu and Karday sections increased to 4.5–6.0 km, while that at the Arghan and Yiganbujima sections remained within 1 km from the river. 2) The vegetation growth rate increased substantially and was spatially heterogeneous when the groundwater level reached 4.0–5.0 m and was influenced by surface water divergence; the average vegetation expansion rates per square km for P 1 –P 4 were 0.017 km2/a, 0.011 km2/a, 0.005 km2/a, and 0.002 km2/a, respectively. 3) Flooding by surface water was the main driver of vegetation expansion when groundwater was stable and elevations in surface vegetation area were often accompanied by the spreading of surface water. Surface water was significantly correlated with vegetation cover and normalized difference vegetation index (NDVI) (Water pixels–Vegetation cover [P 1 : R2 = 0.57, P < 0.001; P 2 : R2 = 0.45, P < 0.01]; Water pixels–NDVI [P 1 : R2 = 0.22, P < 0.05; P 2 : R2 = 0.40, P < 0.01]). The increase in surface vegetation has alleviated the desertification process in the LTR, and the survival of the poplar has been mitigated. The results of this paper can help decision makers to formulate reasonable ecological water conveyance strategies and realize sustainable management of DRF ecosystems in arid zones. [ABSTRACT FROM AUTHOR]

The development of phytogeography is an essential aspect of floral evolution. The Silurian is considered a pivotal period for the development of early land vegetation based on both macrofossil and microfossil evidence; however, the evolution of phytogeographic zonation at that time remains controversial. In this paper, we describe a new species of the rhyniopsid, Aberlemnia junggaria sp. nov., from the Pridoli (upper Silurian) of West Junggar, Xinjiang, China. The new plant consists of at least three orders of axes, an oval to reniform terminal sporangium bearing in sparse ultimate axes and sporangium with a nearly enclosed dehiscence line. A dataset of global Silurian macrofossil records was compiled, including 30 species within 24 genera from 38 localities covering 8 main paleogeographic blocks. Using these data, the spatial-temporal distribution of Silurian plant macrofossils was mapped. Biogeographic evolution is revealed for the first time by means of network analysis and cluster analysis, and two phytogeographic realms, the West Junggar-Vietnam and Laurussia-North Gondwana realms are provisionally recognized during the Pridoli Epoch. Considering the widespread microfossil evidence for land plant diversification and dispersal, this global phytogeographic zonation might have onset at an even earlier time. • A new species of rhyniopsid Aberlemnia from the upper Silurian of Junggar, Xinjiang, China. • Global Silurian macroplant occurrence dataset released. • Combined study on phytogeography with specimens and data analysis. • The first phytogeographic zonation started not later than the Pridoli Epoch. [ABSTRACT FROM AUTHOR]

Accurately assessing the clogging risk of the drainage pipe envelopes is a prerequisite for the reasonable design and proper management of subsurface drainage. However, the current assessment of clogging risk for subsurface drainage envelopes in arid areas mainly focuses on the impacts of single factors such as physical or chemical factors, with less attention given to combined physical–chemical clogging, which is more likely to occur and causes significant harm. Based on the evaluation indicators for physical and chemical clogging risks, this study utilized the entropy weight method to determine the weights of physical and chemical clogging risks, then proposed evaluation indicators for combined clogging risks and generated risk maps of physical, chemical, and combined clogging within the Xinjiang watershed. Furthermore, field excavation and sampling of subsurface drainage pipes were conducted to apply the proposed evaluation indicators for combined clogging risks and analyze the influencing factors. The results show that the combined clogging risk map based on the proposed combined clogging evaluation indicators is consistent with the actual clogging situation in the subsurface drainage system. Both physical and chemical clogging coexisted in the subsurface drainage system in Xinjiang, with a higher mass content of SiO 2 than CaCO 3 in the clogging substances. The combined clogging risk increased with rises in the drainage temperature and salinity. Under current conditions, the proportions of high, moderate, and low-risk areas of combined clogging risk are 22.10%, 30.37%, and 47.53%, respectively. The high-risk areas are mainly concentrated in Southern Xinjiang, which accounts for 91.7% of the high-risk area. This study can provide technical support for the prevention and control of clogging in subsurface drainage systems for saline-alkali farmland in arid areas. • Risk evaluation indicators for combined clogging of subsurface drainage were proposed. • Risk maps of physical, chemical, and combined clogging in Xinjiang were generated. • The combined clogging risk increased with rises of drainage temperature and salinity.. [ABSTRACT FROM AUTHOR]