All-optical format conversion-based flexible optical interconnection using nonlinear MZI with nested-pump assisted NOLM

An all-optical format conversion (AOFC) scheme of star-m-ary quadrature amplitude modulation (star-mQAM) based on a nonlinear Mach-Zehnder interferometer (MZI) with nested-pump assisted nonlinear optical loop mirror (nested-PA-NOLM) is proposed and numerically simulated. In this scheme, input multi-Gbps star-8QAM signals can be converted into three quadrature phase shift keying (QPSK) signals (namely QPSK-A, -B and -C) through the PA-NOLM under different input power of the signal and the pump. The nonlinear MZI is formed by two PA-NOLMs of the upper and the lower arms, the former and the latter 3-dB optical couplers (OCs), a directional variable optical attenuator (VOA) in the upper arm and a directional variable phase shifter (VPS) in the lower arm. A VOA and a VPS are used to adjust the power ratio (PR) and relative phase shift (RPS) between any two of QPSK-A, -B and -C. When any two adjusted signals in QPSK-A, -B and -C are coherently superposed, the aggregated star-8QAM signal can be extracted again. Furthermore, the proposed scheme can also be used to convert the 20 Gbps bipolar 4-ary pulse amplitude modulation (PAM4) signal into two 10 Gbps BPSK signals and a 20 Gbps QPSK signal. When the proposed scheme is combined with the phase-sensitive amplification (PSA), it can also be used to convert one 16QAM into two QPSK signals. The scheme performance is analyzed via constellation diagrams, power waveforms, the error vector magnitude (EVM) and the bit error rate (BER) of the optical signals. The scheme can not only be deployed in optical gateways to connect optical networks using different modulation formats, but also has a potential applied advantage in security information transmission between different optical networks

How to Detect Scale Effect of Ecosystem Services Supply? A Comprehensive Insight from Xilinhot in Inner Mongolia, China

Spatial scale plays a crucial role in the assessment and management of ecosystem services (ES), yet explicit information for identifying and understanding the scale effect on ES supply remains limited. In an attempt to detect scale effect on ES supply from a comprehensive perspective, this study developed a framework for integrating scale effect in three aspects, including individual ES patterns, pairwise ES interactions, and ecosystem service bundles (ESB). The framework was tested in Xilinhot, a prairie landscape city of Inner Mongolia, at four different levels of spatial scale. The results indicated that, most ES showed a decreasing clustering at coarser scales in terms of spatial pattern. At the same time, coarser scales resulted in fewer trade-offs and stronger synergies between pairwise ES. The identification of ESB varied greatly with scale, and this change reflected in the composition of ES variables and spatial distribution of bundles. We attributed the scale effect of the above three aspects to differences in social-ecological factors and their driving mechanisms at different scales. This comprehensive framework could support local managers to coordinate the management of multiple ES at different scales

Monocular vision based on the YOLOv7 and coordinate transformation for vehicles precise positioning

Logistics tracking and positioning is a critical part of the discrete digital workshop, which is widely applied in many fields (e.g. industry and transport). However, it is distinguished by dispersed manufacturing machinery, frequent material flows, and complicated noise environments. The positioning accuracy of the conventional radio frequency positioning approach is severely impacted. The latest panoramic vision positioning technology relies on binocular cameras. And that cannot be used for monocular cameras in industrial scenarios. This paper proposes a monocular vision positioning method based on YOLOv7 and coordinate transformation to solve the problem of positioning accuracy in the digital workshop. Positioning beacons are placed on the top of the moving vehicle with a uniform height. The coordinate position of the beacon on the image is obtained through the YOLOv7 model based on transfer learning. Then, coordinate transformation is applied to obtain the real space coordinates of the vehicle. Experimental results show that the proposed single-eye vision system can improve the positioning accuracy of the digital workshop. The code and pre-trained models are available on https://github.com/ZS520L/YOLO_Positioning

Ecosystem Services and Their Relationships in the Grain-for-Green Programme—A Case Study of Duolun County in Inner Mongolia, China

Grassland restoration projects are currently being implemented to mitigate human disturbance to the natural environment and reduce grassland degradation. China’s Grain-for-Green Programme (GFGP), including one project implemented in Duolun County, China, in 2000, has significantly improved the overall ecological health of this region. Using a modeling approach, this study quantified changes in four ecosystem services (ESs), including Net Primary Production (NPP), soil conservation (SC), water yield (WY), and sandstorm prevention (SP), in Duolun County between 2000 and 2016. We found the total NPP, water yield, and soil conservation increased by 80.44%, 248.2%, and 12.2%, respectively, during this period, while the sandstorm prevention decreased by 55.9%. Unlike other areas of GFGP implementation, the improvement of the ecological environment in Duolun County is largely attributed to the increased of vegetation coverage (88%) instead of land use circulation (12%). We found the grassland is a factor that reduces the trade-off while this effect was related with the grassland coverage. Future policies should be based on the mechanisms of vegetation underlying the ESs change and the relationships of ESs in order to achieve sustainable provision of ESs

Fault diagnosis method for mine hoisting motor based on VMD and CNN-BiLSTM

The traditional motor fault diagnosis method based on the audio signal is insufficient to obtain the feature information of the motor audio signal and the fault diagnosis precision is not high. In order to solve the above problems, a mine motor fault diagnosis method based on optimized variational mode decomposition (VMD) and convolutional neural network CNN bidirectional long short-term memory (BiLSTM) is proposed. The whale algorithm (WOA) optimized VMD is used to decompose the motor audio signal to address the issues of modal aliasing and endpoint effects. The motor audio signal is decomposed into K intrinsic mode functions (IMF). After Pearson correlation coefficient screening, the 13-dimensional static MFCC feature parameters of the main IMF component are extracted. In order to obtain the dynamic features of the signal, the first and second-order difference coefficients of the 13-dimensional static MFCC are extracted to form a 39-dimensional feature vector. By combining dynamic and static features, the performance of fault diagnosis can be improved. In order to improve the precision of fault diagnosis, a BiLSTM layer is introduced into the CNN. The CNN extracts local features of the audio signal in the spatial dimension. The BiLSTM preserves bidirectional time series information of the audio signal in the temporal dimension. It captures long-distance dependencies of the audio signal, thereby maximizing the preservation of global and local features. The experimental results show the following points. ① Each IMF component of VMD decomposition has an independent center frequency and uniform distribution, and exhibits sparsity in the frequency domain. It can effectively avoid modal aliasing problems. In IMF solving, VMD decomposition avoids endpoint effects in empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) through mirror extension. ② The fault diagnosis accuracy based on 13-dimensional static MFCC features is 97.5%. The fault diagnosis accuracy based on 39-dimensional dynamic and static MFCC features is 1.11% higher than that based on 13-dimensional static MFCC features. ③ The accuracy of the diagnostic model based on CNN-BiLSTM reaches 98.61%, which is 5.83%, 4.17%, and 3.89% higher than the current universal diagnostic models CNN, BiLSTM, and CNN-LSTM, respectively

Removal and Recovery of Phosphate and Fluoride from Water with Reusable Mesoporous Fe3O4@mSiO2@mLDH Composites as Sorbents

Three core/shell/shell MgAl-LDH composites using Fe3O4 microspheres as the core, a SiO2 matrix as the inner layer and a MgAl-LDH layer as the outer shell have been synthesized for the removal and recovery of phosphate and fluoride from water by a magnetic separation technique. The synthetic mesoporous MgAl-LDH composites show good magnetic separability, well-defined pore distributions, and have specific surface areas of 73 m2 g−1, 168 m2 g−1, and 137 m2 g−1 for Fe3O4@SiO2@LDH350, Fe3O4@SiO2@mLDH350, and Fe3O4@mSiO2@mLDH350, respectively. The adsorption isotherms of both phosphate and fluoride on these MgAl-LDH composites can be well fitted with the Langmuir model. The maximum adsorption capacities of 57.07 mg g−1 and 28.51 mg g−1 were obtained on Fe3O4@mSiO2@mLDH350 for phosphate and fluoride, respectively, much higher than those of other LDH-type materials. The adsorbed phosphate and fluoride could be successfully recovered by NaNO3-NaOH solution, and the regenerated MgAl-LDH composites could be reused for phosphate and fluoride removal. Owing to their high adsorption capacities of both phosphate and fluoride, easy magnetic separation from solution, and great reusability, the mesoporous MgAl-LDH composites are expected to have potential applications in removal or recovery of fluoride or phosphate from water

Quantitative Analysis of Natural and Anthropogenic Factors Influencing Vegetation NDVI Changes in Temperate Drylands from a Spatial Stratified Heterogeneity Perspective: A Case Study of Inner Mongolia Grasslands, China

The detection and attribution of vegetation dynamics in drylands is an important step for the development of effective adaptation and mitigation strategies to combat the challenges posed by human activities and climate change. However, due to the spatial heterogeneity and interactive influences of various factors, quantifying the contributions of driving forces on vegetation change remains challenging. In this study, using the normalized difference vegetation index (NDVI) as a proxy of vegetation growth status and coverage, we analyzed the temporal and spatial characteristics of the NDVI in China’s Inner Mongolian grasslands using Theil–Sen slope statistics and Mann–Kendall trend test methods. In addition, using the GeoDetector method, a spatially-based statistical technique, we assessed the individual and interactive influences of natural factors and human activities on vegetation-NDVI change. The results show that the growing season average NDVI exhibited a fluctuating upward trend of 0.003 per year from 2000 to 2018. The areas with significant increases in NDVI (p < 0.05) accounted for 45.63% of the entire region, and they were mainly distributed in the eastern part of the Mu Us sandy land and the eastern areas of the Greater Khingan Range. The regions with a decline in the NDVI were mainly distributed in the central and western regions of the study area. The GeoDetector results revealed that both natural and human factors had significant impacts on changes in the NDVI (p < 0.001). Precipitation, livestock density, wind speed, and population density were the dominant factors affecting NDVI changes in the Inner Mongolian grasslands, explaining more than 15% of the variability, while the contributions of the two topography factors (terrain slope and slope aspect) were relatively low (less than 2%). Furthermore, NDVI changes responded to the changes in the level of specific influencing factors in a nonlinear way, and the interaction of two factors enhanced the effect of each singular factor. The interaction between precipitation and temperature was the highest among all factors, accounting for 39.3% of NDVI variations. Findings from our study may aid policymakers in better understanding the relative importance of various factors and the impacts of the interactions between factors on vegetation change, which has important implications for preventing and mitigating land degradation and achieving sustainable pasture use in dryland ecosystems

All-Optical Regeneration and Format Conversion for 4APSK Signals Based on Nonlinear Effects in HNLF

An all-optical format conversion and regeneration scheme about 4-ary amplitude and phase shift keying (4APSK) signals is proposed and numerically simulated based on nonlinear effects in the high nonlinear fiber (HNLF). The input 4APSK signal is firstly converted into a regular quadrature phase shift keying (QPSK) signal by the nonlinear Mach-Zehnder interferometer (MZI) based on the self-phase modulation (SPM). Secondly, a degenerate phase-sensitive amplification (PSA) based on the four-wave mixing (FWM) is utilized to convert the regular-QPSK into two binary phase shift keying (BPSK) signals. The nonlinear MZI configuration is also used to compress the amplitude noise of BPSK. Thirdly, one phase shifter and one variable optical attenuator (VOA) are used to adjust the relative phase and power relationships of the two amplitude-regenerated BPSK signals. The regenerated 4APSK and converted QPSK signals can be generated in one 3-dB optical coupler through coherent addition of the two regenerated BPSK signals. The error-vector-magnitude (EVM) and the bit-error-rate (BER) are calculated and compared to evaluate the scheme performance. The proposed scheme can be applied as an optical regenerator or format convertor at the network gateway to increase the transmission distance or connect optical networks with different modulation formats

Quantitative Analysis of Natural and Anthropogenic Factors Influencing Vegetation NDVI Changes in Temperate Drylands from a Spatial Stratified Heterogeneity Perspective: A Case Study of Inner Mongolia Grasslands, China

The detection and attribution of vegetation dynamics in drylands is an important step for the development of effective adaptation and mitigation strategies to combat the challenges posed by human activities and climate change. However, due to the spatial heterogeneity and interactive influences of various factors, quantifying the contributions of driving forces on vegetation change remains challenging. In this study, using the normalized difference vegetation index (NDVI) as a proxy of vegetation growth status and coverage, we analyzed the temporal and spatial characteristics of the NDVI in China’s Inner Mongolian grasslands using Theil–Sen slope statistics and Mann–Kendall trend test methods. In addition, using the GeoDetector method, a spatially-based statistical technique, we assessed the individual and interactive influences of natural factors and human activities on vegetation-NDVI change. The results show that the growing season average NDVI exhibited a fluctuating upward trend of 0.003 per year from 2000 to 2018. The areas with significant increases in NDVI (p p < 0.001). Precipitation, livestock density, wind speed, and population density were the dominant factors affecting NDVI changes in the Inner Mongolian grasslands, explaining more than 15% of the variability, while the contributions of the two topography factors (terrain slope and slope aspect) were relatively low (less than 2%). Furthermore, NDVI changes responded to the changes in the level of specific influencing factors in a nonlinear way, and the interaction of two factors enhanced the effect of each singular factor. The interaction between precipitation and temperature was the highest among all factors, accounting for 39.3% of NDVI variations. Findings from our study may aid policymakers in better understanding the relative importance of various factors and the impacts of the interactions between factors on vegetation change, which has important implications for preventing and mitigating land degradation and achieving sustainable pasture use in dryland ecosystems