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Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6 dB Q improvement after 2800 km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features. Long-distance fiber communications still face many fundamental challenges in capacity due to nonlinearities. The authors develop a neural-network based tool to compensate nonlinearities, without prior knowledge of the transmission link, with low complexity.

In this study, the ability of Acidithiobacillus ferrooxidans to oxidize Fe 2+ to Fe 3+ and recover battery black powder was investigated, establishing a system for leaching decommissioned lithium iron phosphate battery black powder from A. ferrooxidans . Black powder reduced the consumption of reagents and subsequent pressure for treating iron-bearing minerals using the iron source in waste LiFePO 4 batteries. This study used ultrasonic waves to remove impurities on the surface and cracks in battery black powder, hindering the dissolution layer and enhancing the leaching effect through a cavitation reaction and microbial activation to promote the leaching process. A filter bag experiment was designed using the selective permeability of filter bags to investigate whether the leaching mechanism of A. ferrooxidans lithium iron phosphate is contact or non-contact. Under optimal leaching conditions, the lithium leaching rate reached 99.7%, and the leaching time was reduced from 7 to 5 days, achieving efficient leaching of lithium. The filter bag experiment concluded that A. ferrooxidans leaching of lithium iron phosphate was mainly a contact leaching mechanism.

A large number of subsided wetlands have formed in eastern China in areas with high-intensity mining. However, data are not currently available to indicate their spatial distribution and expansion in the past thirty years. This paper uses a modified normalized difference water index (mNDWI) and a maximum between-cluster variance (OTSU) image segmentation algorithm to extract the subsided wetlands in mining areas with high ground-water levels of eastern China from 1988 to 2018 based on Google Earth Engine. The results show that the overall accuracy of the extraction of subsided wetlands is 98%; the Kappa coefficient reached 0.81. The total area of subsided wetland in 2018 was 26,034.88 ha, of which 14,290.97 ha was in Anhui Province, accounting for 54.89% of all such wetlands. The spatial extent of subsided wetlands has grown rapidly in the past three decades with the area of subsided wetlands expanding by 11.86 times from 1988 to 2018. The total area of subsided wetlands in the winter of 2018 was 25,296.25 ha, which was smaller than in summer. This indicates that seasonal precipitation affects the spatial extent of subsided wetlands. Although some restoration activities have been successful, most of the subsided wetlands still need active development and management. In conclusion, mNDWI and OTSU image segmentation algorithms could quickly and accurately allow the extraction of the spatial extent of subsided wetlands. Subsided wetlands have strong potential for development in future ecological restoration. The ecosystem services of wetlands and availability of dynamic monitoring technology should be considered important in the future.

Remotely sensed images with low resolution can be effectively used for the large-area monitoring of vegetation restoration, but are unsuitable for accurate small-area monitoring. This limits researchers’ ability to study the composition of vegetation species and the biodiversity and ecosystem functions after ecological restoration. Therefore, this study uses LiDAR and hyperspectral data, develops a hierarchical classification method for classifying vegetation based on LiDAR technology, decision tree and a random forest classifier, and applies it to the eastern waste dump of the Heidaigou mining area in Inner Mongolia, China, which has been restored for around 15 years, to verify the effectiveness of the method. The results were as follows. (1) The intensity, height, and echo characteristics of LiDAR point cloud data and the spectral, vegetation indices, and texture features of hyperspectral image data effectively reflected the differences in vegetation species composition. (2) Vegetation indices had the highest contribution rate to the classification of vegetation species composition types, followed by height, while spectral data alone had a lower contribution rate. Therefore, it was necessary to screen the features of LiDAR and hyperspectral data before classifying vegetation. (3) The hierarchical classification method effectively distinguished the differences between trees (Populus spp., Pinus tabuliformis, Hippophae sp. (arbor), and Robinia pseudoacacia), shrubs (Amorpha fruticosa, Caragana microphylla + Hippophae sp. (shrub)), and grass species, with classification accuracy of 87.45% and a Kappa coefficient of 0.79, which was nearly 43% higher than an unsupervised classification and 10.7–22.7% higher than other supervised classification methods. In conclusion, the fusion of LiDAR and hyperspectral data can accurately and reliably estimate and classify vegetation structural parameters, and reveal the type, quantity, and diversity of vegetation, thus providing a sufficient basis for the assessment and improvement of vegetation after restoration.

A rapid increase in underground mining in a semi-arid area of China has led to serious concerns about the health of vegetation overlying these coal seams. However, there have been no empirical studies to illustrate the response and persistence of surface vegetation in these underground mining areas. A combination of field assessments with remote sensing was used to examine vegetation patterns and responses to underground mining, while laying a foundation for environmental protection. The study area lies in a vulnerable watershed exposed to hazards caused by underground coal mining, located on the southern edge of Inner Mongolia in China. The results demonstrate that hydrological factors and soil attributes, including groundwater levels, soil organic matter, and soil moisture, control the structure of the local vegetation community. After mining begins, the vegetation community index based on plant density, coverage, and biomass in areas affected by subsidence fractures decreases by 0–21.5%. Nevertheless, the average Normalized Differential Vegetation Index at the entire watershed scale increased by 15% from 2001 to 2016, although this change appeared to be primarily related to rainfall. This study confirmed that underground coal mining in the watershed has not caused extensive vegetation degradation as feared. Positive climatic trends, the maintenance of important mudstone strata below a phreatic aquifer and the adaptation of vegetation to drought, contributed to the persistence of surface vegetation in underground mining areas. Considering that mining activities usually last for several years, resilience management, including approaches such as protection of important variables, long-term monitoring, and adaptive management, should be adopted in support of conservation and sustainable mining in this watershed and at similar mine sites.

The quantum Cramér–Rao bound sets a fundamental limit on the accuracy of unbiased parameter estimation in quantum systems, relating the uncertainty in determining a parameter to the inverse of the quantum Fisher information. We experimentally demonstrate near saturation of the quantum Cramér–Rao bound in the phase estimation of a solid-state spin system, provided by a nitrogen-vacancy center in diamond. This is achieved by comparing the experimental uncertainty in phase estimation with an independent measurement of the related quantum Fisher information. The latter is independently extracted from coherent dynamical responses of the system under weak parametric modulations, without performing any quantum-state tomography. While optimal parameter estimation has already been observed for quantum devices involving a limited number of degrees of freedom, our method offers a versatile and powerful experimental tool to explore the Cramér–Rao bound and the quantum Fisher information in systems of higher complexity, as relevant for quantum technologies.

Background: This study examined external intensity and acute neuromuscular responses across multiple games played during a single-day official 3 × 3 basketball tournament. Methods: Twelve male players (Tier 2–3; age: 24.7 ± 4.5 years; height: 186.4 ± 8.5 cm; body mass: 86.5 ± 13.0 kg) were monitored with microsensors (Movement Intensity (MI), while countermovement jump (CMJ) variables—jump height (JH); time to takeoff (TTTO); and Modified Reactive Strength Index (RSImod)—were obtained before the start of the tournament and after each game. Linear mixed models examined differences in MI and CMJ variables across tournament phases. Additionally, the smallest worthwhile change (SWC) calculations were applied to all comparisons. Results: No statistical differences were found across tournament stages for MI (p = 0.466), JH (p = 0.762), TTTO (p = 0.990), or RSImod (p = 0.951). SWC comparisons showed that MI was higher in GG1 than GG2, GG3, and QF; higher in GG2 than GG3; and lower in G3 than QF and SF. Regarding JH, the post-QF value was higher than the baseline and post-GG2. For TTTO, post-QF was higher than post-GG1. RSImod post-GG2 was lower than post-GG3 and post-SF. Conclusions: While no significant changes were observed, MI showed a practically meaningful decline in GG3 and recovery in QF, while RSImod initially declined before improving post-SF. These findings highlight the importance of pacing and recovery strategies in 3 × 3 basketball tournaments.

Harvested wood/bamboo products (HWP/HBP) constitute a large global carbon stock. However, the contribution of HBP to carbon stocks has been neglected in mixed wood and bamboo data, especially in China. Therefore, the production approach and the first-order decay method were used to estimate the spatiotemporal carbon stock change in HWP/HBP based on provincial production data from the China Forestry Statistical Yearbooks for 1987–2020. The results showed that China’s total carbon stocks of HWP and HBP were 328.7 teragram carbon (TgC) and 129.7 TgC between 1987 and 2020. Of this, the HWP carbon stock was mainly sourced from three provinces across the north and south: Guangxi (60.8 TgC), Heilongjiang (37.2 TgC), and Fujian (24.2 TgC), and HBP carbon stock was mainly sourced from three southern provinces: Fujian (33.4 TgC), Guangxi (20.3 TgC), and Zhejiang (13.7 TgC). The proportion of the HBP carbon stock in the total carbon stock increased from 20% in 2010 to 28% in 2020, indicating that bamboo products play an important role in the accumulation of carbon stocks in China. The differences in contributions to spatiotemporal trends between the provinces provide more specific information to make precise decisions about forest management and carbon sequestration.

The lethality and chemotherapy resistance of pancreatic cancer necessitates the urgent development of innovative strategies to improve patient outcomes. To address this issue, we designed a novel drug delivery system named GDMCN2,which uses iron‐based metal organic framework (Fe‐MOF) nanocages encased in a covalent organic framework (COF) and modified with the pancreatic cancer‐specific antibody, NRP2. After being targeted into tumor cells, GDMCN2 gradually release the sonosensitizer sinoporphyrin sodium (DVDMS) and chemotherapeutic gemcitabine (GEM) and simultaneously generated reactive oxygen species (ROS) under ultrasound (US) irradiation. This system can overcome gemcitabine resistance in pancreatic cancer and reduce its toxicity to non‐targeted cells and tissues. In a mechanistic cascade, the release of ROS activates the mitochondrial transition pore (MPTP), leading to the release of Ca 2+ and induction of endoplasmic reticulum (ER) stress. Therefore, microtubule‐associated protein 1A/1B‐light chain 3 (LC3) is activated, promoting lysosomal autophagy. This process also induces autophagy‐dependent ferroptosis, aided by the upregulation of Nuclear Receptor Coactivator 4 (NCOA4). This mechanism increases the sensitivity of pancreatic cancer cells to chemotherapeutic drugs and increases mitochondrial and DNA damage. The findings demonstrate the potential of GDMCN2 nanocages as a new avenue for the development of cancer therapeutics.

Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.