Search

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D's short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system's SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn–Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn–Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effectively enhances the performance of DUs in an FD-D2D underlying cellular network when compared to the sum rate maximization design. [ABSTRACT FROM AUTHOR]

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations. [ABSTRACT FROM AUTHOR]

There is an urgent need to develop technologies that can physically manipulate the structure of biocompatible and green polymer materials in order to tune their performance in an efficient, repeatable, easy-to-operate, chemical-free, non-contact, and highly controllable manner. Ultrasound technology produces a cavitation effect that promotes the generation of free radicals, the fracture of chemical chain segments and a rapid change of morphology. The cavitation effects are accompanied by thermal, chemical, and biological effects that interact with the material being studied. With its high efficiency, cleanliness, and reusability applications, ultrasound has a vast range of opportunity within the field of natural polymer-based materials. This work expounds the basic principle of ultrasonic cavitation and analyzes the influence that ultrasonic strength, temperature, frequency and induced liquid surface tension on the physical and chemical properties of biopolymer materials. The mechanism and the influence that ultrasonic modification has on materials is discussed, with highlighted details on the agglomeration, degradation, morphology, structure, and the mechanical properties of these novel materials from naturally derived polymers. [ABSTRACT FROM AUTHOR]

• A novel ultrasound technique to regenerate flexible protein film without any chemical procedures. • Protein structure, physico-chemical property and biological responses can be tuned by varying ultrasound power. • Mechanism of structural and property changes of silk fibroin films from domestic mulberry and wild tussah under ultrasound is comparative studied. • Tissue bioengineering reuse of natural resource materials. Ultrasonic technology has emerging applications in the advanced biomaterials due to its environmentally friendly and high efficiency. In this work, by scanning electron microscopy, fourier transform infrared spectroscopy, x-ray powder diffraction and thermal analysis techniques as well as hydrophilic and cytocompatible tests, the morphology, structure and physical-biological properties of the silk fibroin (SF) film from Bombyx mori silkworm of Thailand and wild SF from Antheraea pernyi cocoon under 0∼800 W power ultrasonic cavitation were investigated systematically. When the ultrasonic intensity increased from 0 to 800 Mpa, the diameters decreased from 80 to 34 nm for Thailand SF film, and from 110 to 48 nm for tussah film, which exhibited more smooth for the surface of domestic silkworm SF film. It was found that the silk II structure significantly increased after the ultrasonic process, which suggested that the changes of microstructure could provide better thermal stability and mechanical property for regenerated films, such as the ductility of Thailand SF film was more enhanced, whereas the stronger stiffness obtained for Tussash. Besides, the random coils in the domestic SF film were easy to convert into β -sheet structure after ultrasonic treatment, while the wild possessed higher hydrophilic and more cell attachment and proliferation activity due to its abundant (-Ala-) n and (Arg-Gly-Asp) sequence. This comparative study will provide a new strategy of the green design for novel fibroin-based functional materials with tunable structure and properties. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a micro‐bare‐tube heat exchanger structure with the tube bundle diameter varying gradually in the range of 1.0–0.4 mm is proposed. The airside friction and heat transfer performance correlations for this structure are given. This paper applies CFD simulation to develop and verify the correlation of the diameter gradually varied micro‐bare‐tube heat exchangers' performance. Compared with the traditional same diameter micro‐bare‐tube heat exchanger, the new structure not only reduces the pressure drop, but also improves the thermal hydraulic performance. The friction coefficient f is decreased by 8.59%, the pressure drop is reduced by 43.14%, the heat transfer coefficient j is increased by 12.07% compared to the constant diameter micro‐bare‐tube heat exchanger. At the same time, it reduces the void volume by 47.32% and the tube bundle manufacturing material by 28.57%. The proposed airside performance prediction equation can make the error of more than 70% source data within 10%, and the error of more than 90% source data within 20%. It is suggested to apply this empirical equation to 7–29 rows. [ABSTRACT FROM AUTHOR]

The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods. [ABSTRACT FROM AUTHOR]

• A monthly-scale pixel-based CCD model is developed. • The CCD between SAI and REE in Beijing is investigated. • Spatiotemporal distribution and changing trend of CCD are discussed in detail. • A schematic model on urban spatial dynamic CCD interactions is further built. Urbanization demands finer coordinated development of the urban human-earth relationship with high quality. Although a large number of studies have explored the long-term and large-scale coupling effect of urbanization and eco-environment, there is a lack of finer spatiotemporal method and assessment. Therefore, this paper proposes a monthly-scale pixel-based coupling coordination degree (CCD) model, aiming to explore the relationship between urban socioeconomic activity intensity (SAI) and regional eco-environment (REE) in Beijing during the period of January 2018 to May 2020 based on nighttime light data and MODIS images. The results show that the SAI in Beijing has shown a trend of increasing, and the overall REE was relatively stable during the investigated period. The CCD between SAI and REE in Beijing has been improving both spatially and quantitatively. Until May 2020, more than 50% of the human activity areas were in a state of high-level coordinated, and the mean CCD reached a basic-coordinated level. A schematic model is further built to explain the characteristics and process of coupling coordinated interactions between socioeconomic activity and eco-environment. The inner-annual micro-scale analysis and assessment provide theoretical support for fine-scale monitoring and high-quality urban development planning and policies from decision-makers. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel ultrasound technique to fabricate flexible protein materials. • Protein structure and properties can be tuned by varying ultrasound power and time. • Biological responses can be controlled by ultrasound-generated protein materials. • Ultrasound technique is a high efficient and easy to operate without any chemical procedures. • Mechanism of ultrasonic effects on protein differents properties are revealed. Ultrasound can be used in the biomaterial field due to its high efficiency, easy operation, no chemical treatment, repeatability and high level of control. In this work, we demonstrated that ultrasound is able to quickly regulate protein structure at the solution assembly stage to obtain the designed properties of protein-based materials. Silk fibroin proteins dissolved in a formic acid-CaCl 2 solution system were treated in an ultrasound with varying times and powers. By altering these variables, the silks physical properties and structures can be fine-tuned and the results were investigated with Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), gas permeability and water contact angle measurements. Ultrasonic treatment aids the interactions between the calcium ions and silk molecular chains which leads to increased amounts of intermolecular β -sheets and α -helix. This unique structural change caused the silk film to be highly insoluble in water while also inducing a hydrophilic swelling property. The ultrasound-regulated silk materials also showed higher thermal stability, better biocompatibility and breathability, and favorable mechanical strength and flexibility. It was also possible to tune the enzymatic degradation rate and biological response (cell growth and proliferation) of protein materials by changing ultrasound parameters. This study provides a unique physical and non-contact material processing method for the wide applications of protein-based biomaterials. [ABSTRACT FROM AUTHOR]

Tweezers based on optical, electric, magnetic, and acoustic fields have shown great potential for contactless object manipulation. However, current tweezers designed for manipulating millimeter‐sized objects such as droplets, particles, and small animals exhibit limitations in translation resolution, range, and path complexity. Here, a novel acoustic vortex tweezers system is introduced, which leverages a unique airborne acoustic vortex end effector integrated with a three‐degree‐of‐freedom (DoF) linear motion stage, for enabling contactless, multi‐mode, programmable manipulation of millimeter‐sized objects. The acoustic vortex end effector utilizes a cascaded circular acoustic array, which is portable and battery‐powered, to generate an acoustic vortex with a ring‐shaped energy pattern. The vortex applies acoustic radiation forces to trap and spin an object at its center, simultaneously protecting this object by repelling other materials away with its high‐energy ring. Moreover, The vortex tweezers system facilitates contactless, multi‐mode, programmable object surfing, as demonstrated in experiments involving trapping, repelling, and spinning particles, translating particles along complex paths, guiding particles around barriers, translating and rotating droplets containing zebrafish larvae, and merging droplets. With these capabilities, It is anticipated that the tweezers system will become a valuable tool for the automated, contactless handling of droplets, particles, and bio‐samples in biomedical and biochemical research. [ABSTRACT FROM AUTHOR]

Halide perovskite materials have broad prospects for applications in various fields such as solar cells, LED devices, photodetectors, fluorescence labeling, bioimaging, and photocatalysis due to their bandgap characteristics. This study compiled experimental data from the published literature and utilized the excellent predictive capabilities, low overfitting risk, and strong robustness of ensemble learning models to analyze the bandgaps of halide perovskite compounds. The results demonstrate the effectiveness of ensemble learning decision tree models, especially the gradient boosting decision tree model, with a root mean square error of 0.090 eV, a mean absolute error of 0.053 eV, and a determination coefficient of 93.11%. Research on data related to ratios calculated through element molar quantity normalization indicates significant influences of ions at the X and B positions on the bandgap. Additionally, doping with iodine atoms can effectively reduce the intrinsic bandgap, while hybridization of the s and p orbitals of tin atoms can also decrease the bandgap. The accuracy of the model is validated by predicting the bandgap of the photovoltaic material MASn1−xPbxI3. In conclusion, this study emphasizes the positive impact of machine learning on material development, especially in predicting the bandgaps of halide perovskite compounds, where ensemble learning methods demonstrate significant advantages. [ABSTRACT FROM AUTHOR]

Summary: To verify the feasibility of applying Taguchi method to fuel cell simulation, an SOFC cathode optimization experiment is purposely designed based on a validated solid oxide fuel cell model. It is found that (a) the maximum power densities with and without the Taguchi method are 6144.7 and 6157.8 W/m2, respectively, which proves that the optimal results with the Taguchi method are reliable; (b) the use of the Taguchi method may lead to deviations in conclusions related to less influential factors, thus more attention should be paid to these factors. [ABSTRACT FROM AUTHOR]

COVER CAPTION:The cover image is based on the Original Article Airside friction and heat transfer performance of micro‐bare‐tube heat exchangers with bundle diameters varying gradually from 1.0 to 0.4 mm by Zhenguo Gong et al., https://doi.org/10.1002/ese3.1600..By Zhenguo Gong; Wenjie Zhou; Bowen Cai and Junliang LuReported by Author; Author; Author; Author [Extracted from the article]

Bi2Te3‐based alloys have great market demand in miniaturized thermoelectric (TE) devices for solid‐state refrigeration and power generation. However, their poor mechanical properties increase the fabrication cost and decrease the service durability. Here, this work reports on strengthened mechanical robustness in Bi2Te3‐based alloys due to thermodynamic Gibbs adsorption and kinetic Zener pinning at grain boundaries enabled by MgB2 decomposition. These effects result in much‐refined grain size and twofold enhancement of the compressive strength and Vickers hardness in (Bi0.5Sb1.5Te3)0.97(MgB2)0.03 compared with that of traditional powder‐metallurgy‐derived Bi0.5Sb1.5Te3. High mechanical properties enable excellent cutting machinability in the MgB2‐added samples, showing no missing corners or cracks. Moreover, adding MgB2 facilitates the simultaneous optimization of electron and phonon transport for enhancing the TE figure of merit (ZT). By further optimizing the Bi/Sb ratio, the sample (Bi0.4Sb1.6Te3)0.97(MgB2)0.03 shows a maximum ZT of ≈1.3 at 350 K and an average ZT of 1.1 within 300–473 K. As a consequence, robust TE devices with an energy conversion efficiency of 4.2% at a temperature difference of 215 K are fabricated. This work paves a new way for enhancing the machinability and durability of TE materials, which is especially promising for miniature devices. [ABSTRACT FROM AUTHOR]

GeTe and (Bi,Sb)2Te3 are two representative thermoelectric (TE) materials showing maximum performance at middle and low temperature, respectively. In order to achieve higher performance over the whole temperature range, their segmented one‐leg TE modules are designed and fabricated by one‐step spark plasma sintering (SPS). To search for contact and connect layers, the diffusion behavior of Fe, Ni, Cu, and Ti metal layers in GeTe is studied systematically. The results show that Ti with a similar linear expansivity (10.80 × 10−6 K−1) to GeTe, has low contact resistance (3 µΩ cm2) and thin diffusion layer (0.4 µm), and thus is an effective metallization layer for GeTe. The geometric structure of the GeTe/(Bi,Sb)2Te3 segmented one‐leg TE module and the ratio of GeTe to (Bi,Sb)2Te3 are determined by finite element simulation method. When the GeTe height ratio is 0.66, its theoretical maximum conversion efficiency (ηmax) can reach 15.9% without considering the thermal radiation and thermal/electrical contact resistance. The fabricated GeTe/(Bi,Sb)2Te3 segmented one‐leg TE module showed a ηmax up to 9.5% with a power density ≈ 7.45 mW mm−2, which are relatively high but lower than theoretical predictions, indicating that developing segmented TE modules is an effective approach to enhance TE conversion efficiency. [ABSTRACT FROM AUTHOR]

Realizing high average thermoelectric figure of merit (ZTave) and power factor (PFave) has been the utmost task in thermoelectrics. Here the new strategy to independently improve constituent factors in ZT is reported, giving exceptionally high ZTave and PFave in n‐type PbSe. The nonstoichiometric, alloyed composition and resulting defect structures in new Pb1+xSe0.8Te0.2 (x = 0–0.125) system is key to this achievement. First, incorporating excess Pb unusually increases carrier mobility (µH) and concentration (nH) simultaneously in contrast to the general physics rule, thereby raising electrical conductivity (σ). Second, modifying charge scattering mechanism by the authors' synthesis process boosts a magnitude of Seebeck coefficient (S) above theoretical expectations. Detouring the innate inverse proportionality between nH and µH; and σ and S enables independent control over them and change the typical trend of PF to temperature, giving remarkably high PFave ≈20 µW cm−1 K−2 from 300 to 823 K. The dual incorporation of Te and excess Pb generates unusual antisite Pb at the anionic site and displaced Pb from the ideal position, consequently suppressing lattice thermal conductivity. The best composition exhibits a ZTave of ≈1.2 from 400 to 823 K, one of the highest reported for all n‐type PbQ (Q = chalcogens) materials. [ABSTRACT FROM AUTHOR]

In order to promote the economic development of China's provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China's provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction. [ABSTRACT FROM AUTHOR]

Wi-Fi-based gesture recognition represents an emerging paradigm of human–computer interaction. While deep learning-based and model-based solutions are crucial for improving accuracy and generalization performance, they often depend heavily on extensive datasets and the widespread deployment of sensing devices. In this paper, we address the challenge involving limited sensing data and devices. We propose a signal-based gesture recognition method that leverages gesture coding based on stroke trajectory and feature representation based on dynamic phase changes. Specifically, we develop an endpoint detection algorithm to ensure precise gesture recognition. Additionally, a subcarrier selection algorithm is designed to select optimal subcarriers, capturing comprehensive gesture information. Extensive experiments are conducted to evaluate the performance. Results demonstrate an average accuracy of 93.67% for six gestures. This approach effectively mitigates the impact of location and environment factors on gesture characteristics, and reduces dependence on large quantity of samples and transceiver devices, obviating the need for model training. • An endpoint detection method is proposed to accurately extract the gesture information. • A subcarrier selection algorithm is proposed to select the subcarriers carrying a higher proportion of gesture-related information while being less susceptible to noise interference. • A gesture coding-based recognition method that leverages the stroke trajectory and unique feature representation is presented. This approach eliminates the need for model training and mitigates the dependence on quantity of samples and sensing devices. • Improve generalization performance of Wi-Fi-based gesture recognition system. [ABSTRACT FROM AUTHOR]

Background: The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods: In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results: The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion: The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters. [ABSTRACT FROM AUTHOR]

• Assess the exacerbated severity and magnitude of flood occurrences. • Reflect long-term trends in areas impacted by floods. • Revealed the extent of spatial clustering of flood events. • Explored the magnitude, range, and progression impact of floods on vegetation. The impact of flooding on vegetation ecosystems worldwide has resulted in severe and widespread threats. Despite this, there is a significant knowledge gap in understanding the impact of floods on vegetation. To gain a comprehensive understanding of the response mechanism of the global vegetation ecosystem to flood disasters, it is crucial to assess the loss of vegetation caused by flood events. In this study, we analyzed 3325 flood events around the world from 2001 to 2020, including the geographic distribution, start and end dates, duration, severity and affected areas. Using kernel density estimation, we assessed the exacerbated severity and magnitude of flood occurrences, identifying spatial clustering of events. Through moving average was utilized to reveal the prolonged trends in areas impacted by floods. Additionally, by spatially matching with global land cover products, we comprehensively explored the magnitude, range, and progression of the impact of floods on vegetation. The findings indicate that (1) Extreme flood events have been increasing in number, affected area, severity, and magnitude since the 21st century, with the majority of events occurring between 10°S-50°N. (2) Strong spatial clustering of flood events was identified in the southeast of North America, the southeast of South America, the east of Africa, the south of Europe, and the south of Asia. (3) Flood hazards in frequent flood-affected areas pose severe threats to the terrestrial vegetation ecosystem, highlighting the necessity of further detailed estimation of flood damages to vegetation at a global scale. This research provides an extensive evaluation of the impact of floods on vegetation at the global-national-regional scale, with a focus on understanding the temporal trends and spatial distribution characteristics over the past 20 years (2001–2020). [ABSTRACT FROM AUTHOR]

Membership inference attacks (MIA) exploit the fact that deep learning algorithms leak information about their training data through the learned model. It has been treated as an indicator which reveals the privacy leakage of machine learning models. In this work, we aim to understand the advantage achieved by White-box MIA, and defend against White-box MIA. Firstly, we estimate the KL divergence on the hidden layers' features between training set and test set as the internal generalization gap. By comparing the internal generalization gap and the generalization gap on the output layer, we raise two insights including (1) the existence of larger generalization gaps on hidden layers and (2) feasibility of generalization gap minimization in defending White-box MIA. Based on our insights, we further design a novel defense method named Nirvana. It intentionally minimizes generalization gap to defend White-box MIA. Formally, Nirvana works by selecting a hidden layer with large generalization gaps and executing a multi-samples convex combination among features on the layer during the training to defend against White-box MIA. Finally, we empirically evaluate Nirvana with state-of-the-art defense methods on CIFAR100 dataset, Purchase100 dataset, and Texas dataset. The experiment results show that Nirvana achieves a trade-off between utility and privacy. It can defend both White-box MIA and Black-box MIA while the test accuracy of the model is maintained. It outperforms previous defense methods in defending against White-box MIA. [ABSTRACT FROM AUTHOR]

• Climate change played a dominant role in driving the increase in NPP. • The impact of urbanization on NPP was overestimated in arid regions. • Soil salinization weakens NPP more strongly than urbanization in FES. • Quantified the joint impacts of "climate-urbanization-soil-AMPs" on NPP. Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of "climate-urbanization-soil-AMPs" on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of "climate-urbanization-soil" that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security. [ABSTRACT FROM AUTHOR]

• Distribution of soil salinization was obtained from RS images and soil sample. • Dynamics of soil salinization and landscape patterns were studied during 2005–2014. • Land conversion has a direct impact on the changes of soil salinization. • The increase in soil salinization is mainly attributed to the expanding cultivated land. Soil salinization has been a topic due to the widespread recognition of the food crisis and the shortage of cultivated land resources in arid regions. Unfortunately, the evolution of soil salinization under the background of landscape dynamics is rarely addressed due to the lack of spatial coupling techniques. Recent developments in soil science and remote sensing technology provide the possibility for accurate management of land resources. This study aims to explore the spatiotemporal variations of soil salinization by coupling the spatial distribution of soil salinization and landscape patterns in the irrigated northern slopes of Tianshan Mountains. We graded soil salinization levels based on analytic hierarchy process (AHP) method. The results indicated that the proportion of salinized cultivated land in 2014 (32.11%) was higher than that in 2005 (28.11%). We argue that simply quantifying the changes of soil salinization can neither accurately reflect the effectiveness of salinized cultivated land management nor meet the need for land resource planning. In response to these shortcomings, the evolution of soil salinization was explored in three categories, i.e., "stable cultivated land", "increased cultivated land", and "decreased cultivated land". We found that the proportion of soil salinization in stable cultivated land has dropped considerably from 38.11% in 2005 to 29.22% in 2014. This number in the increased cultivated land (59.11%) is much higher than that of the whole study area. These results are expected to benefit local authorities and assist in alleviating the food crisis in arid regions. [ABSTRACT FROM AUTHOR]

The performance of deep learning is heavily influenced by the size of the learning samples, whose labeling process is time consuming and laborious. Deep learning algorithms typically assume that the training and prediction data are independent and uniformly distributed, which is rarely the case given the attributes and properties of different data sources. In remote sensing images, representations of urban land surfaces can vary across regions and by season, demanding rapid generalization of these surfaces in remote sensing data. In this study, we propose Meta-FSEO, a novel model for improving the performance of few-shot remote sensing scene classification in varying urban scenes. The proposed Meta-FSEO model deploys self-supervised embedding optimization for adaptive generalization in new tasks such as classifying features in new urban regions that have never been encountered during the training phase, thus balancing the requirements for feature classification tasks between multiple images collected at different times and places. We also created a loss function by weighting the contrast losses and cross-entropy losses. The proposed Meta-FSEO demonstrates a great generalization capability in remote sensing scene classification among different cities. In a five-way one-shot classification experiment with the Sentinel-1/2 Multi-Spectral (SEN12MS) dataset, the accuracy reached 63.08%. In a five-way five-shot experiment on the same dataset, the accuracy reached 74.29%. These results indicated that the proposed Meta-FSEO model outperformed both the transfer learning-based algorithm and two popular meta-learning-based methods, i.e., MAML and Meta-SGD. [ABSTRACT FROM AUTHOR]