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Salt marshes are pivotal in the global carbon cycle, serving as significant contributors to the blue carbon sink. Accurately estimating carbon stock in salt marshes relies on precise vegetation classification. Here, we developed the Salt Marsh Vegetation Classification Network (SVCN), a deep learning algorithm designed to classify three primary vegetation canopies (S. alterniflora, P. australis, and S. mariqueter) spanning from 2016 to 2023 over the Yangtze River estuary, China. The SVCN was initially trained using 412 vegetation samples and Sentinel-1 Synthetic Aperture Radar (SAR) data in 2018. Additionally, we trained three traditional machine learning models – Classification and Regression Trees, Random Forests, and K-Nearest Neighbors – to facilitate a comparative analysis of model performance. Leveraging the classified vegetation outcomes, we conducted salt marsh carbon stock estimations using the InVEST model. The results showed that the SVCN model outperformed the other three models, achieving an overall accuracy of 0.97. Salt marsh carbon stocks in the Yangtze River estuary exhibited an overall increasing trend from 2016 to 2023, with an average annual increase of 3.13 ×103Mg⋅year−1. However, there was a notable decrease of 10.36% in 2017, primarily attributed to reductions in the area covered by S. alterniflora and P. australis, which decreased by 11.18% and 10.11%, respectively. These findings highlight the potential of deep learning models and the incorporation of salt marshes in carbon stock estimates to enhance accuracy.

This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs’ throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration’s performance is essentially restricted by the CH’s energy-intensive consumption (EC), which requires to transfer every WDs’ information, covering its own. In order to figure out the question, the HAP’s energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD’s throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN’s throughput fairness based on nonlinear EH.

It is shown that the presence of hundreds of ppm of water in 1,3-dimethylurea (DMU) powder led to the large depression of the transition temperature between the two enantiotropically related polymorphic forms of DMU (Form II → Form I) from 58 °C to 25 °C, thus explaining the reported discrepancies on this temperature of transition. Importantly, this case study shows that thermodynamics (through the construction of the DMU–water temperature-composition phase diagram) rather than kinetics is responsible for this significant temperature drop. Furthermore, this work also highlights the existence of a monohydrate of DMU that has never been reported before with a non-congruent fusion at 8 °C. Interestingly, its crystal structure, determined from X-ray powder diffraction data at sub-ambient temperature, consists of a DMU–water hydrogen bonded network totally excluding homo-molecular hydrogen bonds (whereas present in forms I and II of DMU).

The use and transformation of land by humans are the main cause of the increase in the concentration of carbon dioxide in the atmosphere. Using land-use data and socioeconomic statistics for 1995, 2000, 2005, 2010, 2015, and 2018, this study examines the carbon emissions from land use and their intensity in the entire Yangtze River Delta Region. We constructed a spatial correlation network of land-use carbon emissions by using a modified gravity model to analyze the characteristics of the spatial correlations and spillover effects of land-use carbon emissions. Results reveal that: (1) the spatial differences in land-use carbon emissions gradually increased, whereas those in land-use carbon emission intensity gradually narrowed from 1995 to 2018; (2) the high-degree centrality of Shanghai, Wuxi, and Suzhou indicated that they had always played leading roles in the network from 1995 to 2018. Moreover, Shanghai and Wuxi had large land-use carbon radiation ranges and together with Suzhou, Hangzhou, Changzhou, and Nanjing, exhibited above-average betweenness centrality from 1995 to 2018 and strong bridging capabilities across the entire network; (3) the land-use carbon emissions had obvious spatial correlations and spillover effects. Our results can provide a scientific basis from an urban agglomeration perspective for the transformation of China’s current economy into a low-carbon one, as well as the realization of regionally differentiated and coordinated emission reduction.

In this article, we propose a novel wireless powered communication network, which is composed of two multiple antennas hybrid access points and a series of distributed wireless devices. The two hybrid access points transmit downlink wireless energy to the wireless devices and receive uplink wireless messages from the wireless devices; meanwhile, the information of wireless devices nearer to their corresponding hybrid access point should be transmitted to the faraway hybrid access point. To improve the throughput performance of some wireless devices away from their corresponding hybrid access point, we propose a clustering-collaboration interactive communication protocol with multiple antennas by Time Division Multiple Access, where two of the distributed wireless devices are selected as cluster heads to help relay information of other cluster members, which can efficiently improve some faraway wireless devices’ throughput performance. However, its performance is also constrained by cluster heads’ high-energy consumption. To solve this energy imbalance problem, multi-antenna energy beamforming technology is exploited for the hybrid access points, which distributes more transmission power to the cluster heads to balance all the wireless devices’ energy consumption. In particular, we obtain the proposed system’s throughput performance through the multi-antenna cluster-based collaboration, and verify through simulations that this scheme can effectively enhance user unfairness and improve the throughput performance.

Human societal growth has greatly pressured available land resources. The key to reducing land pressure and fostering regional synergistic development is revealing the transmission effect of land pressure. We used a modified gravity model to construct a spatial correlation network (SCN) of the land pressure in the Yangtze River Delta region (YRDR) for the years 1995, 2000, 2005, 2010, 2015 and 2020. To examine how the land pressure is transmitted throughout the cities in the YRDR, we used a social network analysis to examine the overall network structure, individual network characteristics and spatial clustering characteristics. Finally, the center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-smoothness further revealed the spatiotemporal evolution and the different patterns of the influencing factors. The results revealed that (1) the spatial correlation structure of the land pressure in the YRDR was relatively stable. Nanjing, Shanghai, Suzhou, Hangzhou and Changzhou played a significant role as linkages. (2) The YRDR was beyond the geographical limit for the land pressure transmission effect and each block had a considerable and mostly steady transmission impact. (3) The center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-stationarity was a viable method for analyzing the factors that influence the land pressure. (4) There were significant regional and temporal variations in the factors influencing land pressure. The influencing factors differed in intensity and direction from city to city. Our results can provide a new perspective on relieving land pressure from the perspective of urban agglomerations and help accomplish the sustainable development of regional land resources.

This paper proposes a novel multi-path and multi-hop wireless powered sensor network in case of hardware impairment, constituting an energy node, one source node, single sink node, and a series of distributed relay sensor nodes, where the energy node transmits wireless energy to all terminals in the first stage, and the relay sensor nodes relay the information of the source node to the sink node in the second stage. There exists M available paths between the source node and sink node, one of which is chosen for serving source-sink communication. To enhance the minimum achievable data rate, we propose a multi-hop communication protocol based on time-division-multiple-access and an optimal throughput path algorithm. We formulate the time allocation optimization problem about energy and information transmission of the proposed multi-hop cooperation, and confirm through abundant simulation experiments that the proposed scheme can availably improve user unfairness and spectral efficiency, and thus enhance its throughput performance.

Characterizing urban expansion patterns is of great significance to planning and decision-making for urban agglomeration development. This study examined the urban expansion in the entire Yangtze River Delta Region (YRDR) with its land-use data of six years (1995, 2000, 2005, 2010, 2015, and 2018). On the basis of traditional methods, we comprehensively considered the four aspects of urban agglomeration: expansion speed, expansion difference, expansion direction, and landscape pattern, as well as the interconnection of and difference in the expansion process between each city. The spatiotemporal heterogeneity of urban expansion development in this region was investigated by using the speed and differentiation indices of urban expansion, gravity center migration, landscape indices, and spatial autocorrelations. The results show that: (1) over the 23 years, the expansion of built-up land in the Yangtze River Delta Region was significant, (2) the rapidly expanding cities were mainly located along the Yangtze River and coastal areas, while the slowly expanding cities were mainly located in the inland areas, (3) the expansion direction of each city varied and the gravity center of the urban agglomeration moved toward the southwest, and (4) the spatial structure of the region became more clustered, the shape of built-up land turned simpler, and fragmentation decreased. This study unravels the spatiotemporal change of urban expansion patterns in this large urban agglomeration, and more importantly, can serve as a guide for formulating urban agglomeration development plans.

Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.

This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R¯) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R¯ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.

In this paper, we consider a wireless powered communication network (WPCN) consisting of a multi-antenna hybrid access point (HAP) that transfers wireless energy to and receives sensing data from a cluster of low-power wireless devices (WDs). To enhance the throughput performance of some faraway WDs, we allow one of the WDs to act as the cluster head (CH) that helps forward the messages of the other cluster members. However, the performance of the proposed cluster-based cooperation is fundamentally limited by the high energy consumption of the CH, who needs to transmit all the WDs' messages including its own. To tackle this issue, we exploit the capability of multi-antenna energy beamforming (EB) at the HAP, which can focus more transferred power to the CH to balance its energy consumption in assisting the other WDs. Specifically, we first derive the throughput performance of each individual WD under the proposed scheme. Then, we jointly optimize the EB design, the transmit time allocation among the HAP and the WDs, and the transmit power allocation of the CH to maximize the minimum data rate achievable among all the WDs (the max-min throughput) for improved throughput fairness among the WDs. An efficient optimal algorithm is proposed to solve the joint optimization problem. Moreover, we simulate under practical network setups and show that the proposed multi-antenna enabled cluster-based cooperation can effectively improve the throughput fairness of WPCN.

Bis(demethoxy)curcumin (BDMC) is one of the main active components found in turmeric. Major drawbacks for its usage are its low aqueous solubility, and the challenging separation from other curcuminoids present in turmeric. Co-crystallization can be applied to alter the physicochemical properties of BDMC in a desired manner. A co-crystal screening of BDMC with four hydroxybenzenes was carried out using four different methods of co-crystal production: crystallization from solution by slow solvent evaporation (SSE), and rapid solvent removal (RSR), liquid-assisted grinding (LAG), and crystallization from the melt phase. Two co-crystal phases of BDMC were obtained with pyrogallol (PYR), and hydroxyquinol (HYQ). PYR-BDMC co-crystals can be obtained only from the melt, while HYQ-BDMC co-crystals could also be produced by LAG. Both co-crystals possess an equimolar composition and reveal an incongruent melting behavior. Infrared spectroscopy demonstrated the presence of BDMC in the diketo form in the PYR co-crystals, while it is in a more stable keto-enol form in the HYQ co-crystals. Solubility measurements in ethanol and an ethanol-water mixture revealed an increase of solubility in the latter, but a slightly negative effect on ethanol solubility. These results are useful for a prospective development of crystallization-based separation processes of chemical similar substances through co-crystallization.

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.

Volcanic activity remains highly detrimental to populations, property and activities in the range of its products. In order to reduce the impact of volcanic processes and products, it is critically important to conduct comprehensive volcanic risk assessments on volcanically active areas. This study tests a volcanic risk assessment methodology based on numerical simulations of volcanic hazards and quantitative analysis of social vulnerability in the Spanish island of Tenerife, a well-known tourist destination. We first simulated the most likely volcanic hazards in the two eruptive scenarios using the Volcanic Risk Information System (VORIS) tool and then evaluated the vulnerability using a total of 19 socio-economic indicators within the Vulnerability Scoping Diagram (VSD) framework by combining the analytic hierarchy process (AHP) and the entropy method. Our results show good agreement with previous assessments. In two eruptive scenarios, the north and northwest of the island were more exposed to volcanic hazards, and the east registered the highest vulnerability. Overall, the northern municipalities showed the highest volcanic risk in two scenarios. Our test indicates that disaster risk varies greatly across the island, and that risk reduction strategies should be prioritized on the north areas. While refinements to the model will produce more accurate results, the outputs will still be beneficial to the local authorities when designing policies for volcanic risk reduction policies in Tenerife. This study tests a comprehensive volcanic risk assessment for Tenerife, but it also provides a framework that is applicable to other regions threatened by volcanic hazards.

Crystallization-based separation of curcumin from ternary mixtures of curcuminoids having compositions comparable to commercial extracts was studied experimentally. Based on solubility and supersolubility data of both, pure curcumin and curcumin in presence of the two major impurities demethoxycurcumin (DMC) and bis(demethoxy)curcumin (BDMC), seeded cooling crystallization procedures were derived using acetone, acetonitrile and 50/50 (wt/wt) mixtures of acetone/2-propanol and acetone/acetonitrile as solvents. Starting from initial curcumin contents of 67−75% in the curcuminoid mixtures single step crystallization processes provided crystalline curcumin free of BDMC at residual DMC contents of 0.6−9.9%. Curcumin at highest purity of 99.4% was obtained from a 50/50 (wt/wt) acetone/2-propanol solution in a single crystallization step. It is demonstrated that the total product yield can be significantly enhanced via addition of water, 2-propanol and acetonitrile as anti-solvents at the end of a cooling crystallization process.

In the present work, freeze crystallization studies, as a novel concentration method for aqueous 1,5-diazabicyclo[4.3.0]non-5-enium acetate ([DBNH][OAc]) ionic liquid solution, were conducted. In order to find the appropriate temperature and composition range for freeze crystallization, the solid−liquid equilibrium of a binary [DBNH][OAc]−water compound system was investigated with differential scanning calorimetry (DSC). Results of this analysis showed that the melting temperature of the pure ionic liquid was 58 ℃, whereas the eutectic temperature of the binary compound system was found to be −73 ℃. The activity coefficient of water was determined based on the freezing point depression data obtained in this study. In this study, the lowest freezing point was -1.28 ℃ for the aqueous 6 wt.% [DBNH][OAc] solution. Ice crystal yield and distribution coefficient were obtained for two types of aqueous solutions (3 wt.% and 6 wt.% [DBNH][OAc]), and two freezing times (40 min and 60 min) were used as the main parameters to compare the two melt crystallization methods: static layer freeze and suspension freeze crystallization. Single-step suspension freeze crystallization resulted in higher ice crystal yields and higher ice purities when compared with the single-step static layer freeze crystallization. The distribution coefficient values obtained showed that the impurity ratios in ice and in the initial solution for suspension freeze crystallization were between 0.11 and 0.36, whereas for static layer freeze crystallization these were between 0.28 and 0.46. Consequently, suspension freeze crystallization is a more efficient low-energy separation method than layer freeze crystallization for the aqueous-ionic liquid solutions studied and, therefore, this technique can be applied as a concentration method for aqueous-ionic liquid solutions.

Solvate formation of bis(demethoxy)curcumin (BDMC) was screened. Six solvates were obtained out of the nineteen solvents investigated. In the present work, three solvates, i.e., BDMC-tetrahydrofuran (THF), BDMC-1,4-dioxane (DIO) and BDMC-dimethyl sulfoxide (DMSO), were thoroughly studied. Their desolvation processes were characterized by temperature-resolved powder X-ray diffraction (TR-PXRD), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), and hot-stage microscopy (HSM). TR-PXRD shows that all the solvates desolvate as the mother BDMC form and no new polymorph could be obtained. The stoichiometric ratio of solvates was calculated via the mass loss of solvents determined by TGA. The thermal stabilities of the solvates were obtained from DSC data and followed the order: BDMC-DMSO > -THF > -DIO. Moreover, stability performances at ambient storage conditions recorded by PXRD show that BDMC-DMSO was stable over three months.