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ZnAl2O4 with a typical spinel structure is highly expected to be a novel rare‐earth‐free ion‐activated oxide phosphor with red emission, which holds high actual meaning for advancing phosphor‐converted light‐emitting diode (pc‐LED) lighting. Among the rare‐earth‐free activators, Mn4+ ions have emerged as one of the most promising activators. Considering the price advantage of MnCO3 generating Mn2+ ions and the charge compensation effect potentially obtaining Mn4+ ions from Mn2+ ions, this research delves into a collection of ZnAl2O4:Mn2+(Mn4+), x Li+ (x = 0%–40%) phosphors with Li+ as co‐dopant and MnCO3 as Mn2+ dopant source prepared by a high temperature solid‐state reaction method. The lattice structure was investigated using X‐ray diffraction (XRD), photoluminescence (PL), and photoluminescence excitation (PLE) spectroscopy. Results suggest a relatively high probability of Li+ ions occupying Zn2+ lattice sites. Furthermore, Li+ ion doping was assuredly found to facilitate the oxidization of Mn2+ to Mn4+, leading to a shift of luminescence peak from 516 to 656 nm. An intriguing phenomenon that the emission color changed with the Li+ doping content was also observed. Meanwhile, the luminescence intensity and quantum yield (QY) at different temperatures, as well as the relevant thermal quenching mechanism, were determined and elucidated detailedly. [ABSTRACT FROM AUTHOR]

Introduction: The efficacy and safety of ainuovirine+lamivudine+tenofovir (ANV+3TC+TDF) and efavirenz+lamivudine+tenofovir (EFV+3TC+TDF) have been confirmed in previous clinical trials; however, there are no related studies on patient‐reported outcomes. This study aimed to evaluate the effectiveness and safety of these 2 antiretroviral therapy regimens and to understand the patient's symptom experience and subjective experience of sleep quality through patient‐reported outcomes. Methods: This is a single‐center prospective cohort study with 243 patients evaluated from October 1, 2021 to June 30, 2022. Virological effectiveness and patient‐reported outcomes results were analyzed. The primary endpoint was the proportion of HIV viral load <50 copies/mL (virological suppression rate) at 48 weeks and the changes in the HIV symptom index and Pittsburgh sleep quality index. Results: The virological suppression rates in the ANV+3TC+TDF and EFV+3TC+TDF groups were 83.6% (102/122) and 87.6% (106/121), respectively, at 48 weeks. In the ANV+3TC+TDF group, the scores of HIV symptom index and pittsburgh sleep quality index in the 48th week were lower than the baseline level (p < 0.05). Logistic regression results showed that the baseline regimen EFV+3TC+TDF was a risk factor for dizziness/lightheadedness (odds ratio = 3.153, 95% confidence interval: 1.473–6.748, p = 0.003), sadness/depression odds ratio = 2.404, 95% confidence interval:1.188–4.871, p = 0.015), and difficulty sleeping (odds ratio = 2.802, 95% confidence interval: 1.437–5.463, p = 0.002) at 48 weeks. Conclusions: Both regimens showed good virological effectiveness; however, compared with ANV+3TC+TDF, the EFV+3TC+TDF regimen reduced the prevalence of HIV‐related symptoms. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the therapeutic effect and tolerance of bictegravir/emtricitabine/tenofovir alafenamide fumarate (BIC/FTC/TAF) use for 24 weeks in anti-retroviral therapy (ART)-naïve patients in China. This single-center retrospective cohort study included ART-naïve patients who received BIC/FTC/TAF from July 2021 to April 2022. The proportion of patients with HIV RNA < 50 copies/mL at the end point of 24 weeks (virological suppression rate) was the primary outcome, and the changes in CD4 cell count, CD4/CD8 ratio, weight, blood lipid, and safety were secondary outcomes. A total of 80 ART-naïve patients were enrolled. The virological suppression rate was 86.3% at 24 weeks. The median CD4 cell count increased from 212 cells/μL (interquartile range [IQR]: 90.3–398.3) at baseline to 348 cells/μL (IQR: 219.8–541.0) at 24 weeks. The median CD4/CD8 ratio increased from 0.25 (IQR: 0.13–0.37) at baseline to 0.40 (IQR: 0.26–0.66) at 24 weeks. During the follow-up of 80 ART-naïve patients using BIC/FTC/TAF, 16 participants had adverse events; however, these events did not lead to drug withdrawal. This real-world cohort study showed that BIC/FTC/TAF could achieve good immunological and virological responses in ART-naïve patients. In addition, this study also shows good safety. [ABSTRACT FROM AUTHOR]

Toxic epidermal necrolysis (TEN) is a type of drug eruption in dermatology emergencies that is rare in clinical practice but has a high mortality rate. The main causes are drug and viral infections. Unfortunately, no expert consensus on treating this disease exists, and a standard therapy is absent. Up to now, glucocorticoids combined with gamma globulin are commonly used in clinical practice, but their efficacy is highly controversial. This study reports on a 7-year-old girl with TEN who did not respond to traditional therapy, such as methylprednisolone combined with gamma globulin, but was finally cured with an additional low-dose etanercept. The results showed that etanercept therapy in paediatric TEN is safe, reliable and worth recommending. [ABSTRACT FROM AUTHOR]

Image interpretation algorithms based on deep learning are becoming increasingly important in land cover information acquisition. We propose CSSwin-unet, a network designed for the semantic segmentation of remote sensing images. CSSwin-unet is based on Swin-unet, follows the U-shaped codec structure of U-Net, but utilizes Swin transformer blocks with superior global modelling capabilities to constitute the codec. In addition, we design a parallel branch in the encoder with a context aggregation module (CAM) to enhance contextual information extraction and alleviate the semantic ambiguity problem resulting from occlusion. To address the problem of semantic information mismatch between codecs and improve the model's ability to extract spatial information, we constructed a space extraction module (SEM) in the skip connections, which replaces the direct copying of encoder features in Swin-unet. To reduce information loss during the downsampling process and strengthen the segmentation capacity of the network, we designed a feature shrinkage module (FSM) in the downsampling session. We conducted comprehensive ablation experiments on a dataset we produced ourselves and compared the results with other advanced methods. The test results showed significant improvement, with mIoU, mF1, and OA values improving by 2.83%, 2.47%, and 2.05%, respectively, compared to the second best performing model, Swin-unet. The above results prove the excellent performance of CSSwin-unet. [ABSTRACT FROM AUTHOR]

The study block opens up with the already found oil reservation, through the comprehensive analysis to seismic, well logging, well log and core data, collate and stipulate the system of fault, and then make sure the oil source fault; Meanwhile we make use of Geo-Office and Jason multiply technology, to launch micro faces and single sand body spread research. The result shows that, the study block mostly have 4 kinds of oil reservation: fault-lithologic, fault block, fault-screened and lithologic reservoir, the main target layer is Fuyu oil layer of Quantou Formation 3 and 4 part, the reservation and spread of oil is mainly controlled by the distance to the oil source, the space spread which is coordinated to form transportation system by fault concentrated belt and sand body, the high fault block trap. The oil reservation mainly exist in the internal part of fault concentrated belt and the sand body nearby, all in all the spread feature shows that the oil stays above and the water keeps low, and the formations which includes oil becomes less and less from the downstructure location to the updip; The major period for hydrocarbon accumulation is the late period of the Mingshui Formation deposition; Here are the modes of the oil transformation and reservation : the oil goes downward and flow backward and transforms into Fuyu formation, which origins from the source rocks of Qingl member in Sanzhao depression, under the abnormal pressure through the source fault, and then the oil in Fuyu formation of Sanzhao depression transforms to study block under the affect of floatage along the two fault concentrated belt which was linked up by the sand body, at the upstructure location lithology or fault aligns with lithology forms the trap which keeps together to oil reservation.

Background: Prospective studies examining long‐term therapeutic outcomes of the Bictegravir/Emtricitabine/Tenofovir Alafenamide (BIC/FTC/TAF) regimen in human immunodeficiency virus (HIV) infection remain limited. This study assessed the actual efficacy and safety of BIC/FTC/TAF in HIV‐infected individuals in southwest China. Methods: This was a single‐center, prospective study enrolling ART‐naïve (n = 32) and ART‐experienced (n = 177) HIV‐infected patients administered BIC/FTC/TAF treatment between March 2022 and August 2022. The data were collected until February 28, 2023. Virological reactions and adverse events to the treatment were recorded, and patient subjective feelings in the form of Electronic Patient Reporting Outcome (ePRO) were collected. The primary endpoint was the rate of patients with HIV viral load <50 copies/mL at Week 24. Results: At Week 24, 87.5% and 95.5% of ART‐naïve and ART‐experienced HIV patients had a viral load <50 copies/mL, respectively. CD4 cell counts in ART‐naïve and ART‐experienced patients increased significantly by 163.5 cells/μL (p =.002) and 55.0 cells/μL (p =.022), respectively. By Week 24, no patients had discontinued the BIC/FTC/TAF treatment due to adverse events. Based on ePRO data, ART‐naïve and ART‐experienced patients at Week 24 had stable disease symptom burden, quality of life, and depression level after treatment with BIC/FTC/TAF. Conclusion: BIC/FTC/TAF reduces the viral load in ART‐naïve patients with high viral load as well as ART‐experienced patients with residual viremia. The patient's subjective experience was maintained stable after treatment with BIC/FTC/TAF. This study also revealed a very low incidence for BIC/FTC/TAF drug‐related side effects. [ABSTRACT FROM AUTHOR]

Accurate road network data for open-pit mine is crucial for the effective implementation of ore mining and transportation scheduling systems as well as the operation of unmanned mining trucks. It is still a challenging task to accurately extract roads from open-pit mine because of their blurred boundaries and varying scales. Based on the characteristics of open-pit mine roads, we propose a local point feature enhancement network (LPFE-Net). The core of LPFE-Net is a point feature enhancement unit (PFE Unit) and a multiscale residual module. Among them, the PFE unit is used to fuse the key features of the neighboring points. The multiscale residual module superimposes the PFE unit to expand the perceptual field and uses multilevel residual connections to improve information flow and reduce model complexity. The experimental results show that the proposed LPFE-Net outperforms all the compared methods in the open-pit mine road extraction task while having a relatively fast extraction speed. [ABSTRACT FROM AUTHOR]

Background: Short-term and long-term comparative outcomes after percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for multivessel coronary artery (MVCA) or left main coronary artery (LMCA) disease are highly debated. Goals: We performed a meta-analysis to evaluate the difference between PCI and CABG for the treatment of patients with MVCA or LMCA in long-term follow-up. Methods: Literatures were searched in PubMed, EMBASE and The Cochrane Library from January 1, 2000 to January 1, 2021, including RCTs and observational studies (OSs). The primary outcome was all-cause mortality at 10 years follow-up, and the secondary outcomes included cardiac mortality, repeated revascularization, myocardial infarction, and stroke. Results: A total of 5 RCTs reporting data from 3013 participants and 4 OSs of 5608 participants were included for analysis. There was no significant difference between PCI and CABG in all-cause mortality (Odds Ratio (OR) 1.03 [95% confidence interval (CI) 0.89 to 1.19]), whereas PCI was associated with higher cardiac mortality (OR 0.76 [95% CI 0.65 to 0.90]) and repeated revascularization rate comparing to CABG (OR 1.77 [95% CI 1.08 to 2.89]; I2 = 94.61%). The difference between PCI and CABG in repeated revascularization in either RCTs or OSs, in myocardial infarction in either RCTs or OSs were not significant. In OSs, stroke rate in PCI group was lower than those in CABG, but not in RCTs. There was a significant increase of stroke rate in CABG comparing to PCI (OR 0.65 [95% CI 0.53 to 0.80]; I2 = 0.00%). No significant difference between PCI and CABG in myocardial infarction was not observed (OR 0.92 [95% CI 0.64 to 1.31]; I2 = 57.84%). Conclusion: Evidence from our study and prior studies suggested the superiority of CABG over PCI in improving 5- but not 10-year survival among patients with MVCA. In the contrast, there was no significant difference between CABG and PCI for treating patients with LMCA in either 5- or 10-year survival rate. More long-term trials are needed to better define differences of outcome between 2 techniques. [ABSTRACT FROM AUTHOR]

Long-term outcome data from real-world studies on the implementation of a two-drug dolutegravir plus lamivudine (DTG+3TC) regimen for the treatment of human immunodeficiency virus (HIV) infection remain limited. This study evaluated the real-world effectiveness and safety of DTG+3TC in people living with HIV (PLHIV) in Southwestern China. This was an observational, single-center, retrospective study that enrolled antiretroviral therapy (ART)-naïve (n = 36) and ART-experienced patients with HIV (n = 86) between January 2019 and April 2021. The virological response to therapy and adverse events were documented. The primary endpoint was an HIV viral load (VL) <50 copies/mL at week 48. The proportion of treatment-naïve and ART-experienced PLHIV with a VL <50 copies/mL at 48 weeks was 97.2% and 97.7%, respectively. The CD4 count increased significantly by 80.2 cells/μL (P = 0.012) and 79.0 cells/μL (P = 0.021) in the ART-naïve and ART-experienced patients, respectively. No patients discontinued DTG+3TC by week 48 due to adverse events. Virologic suppression may be achieved with DTG+3TC, in ART-naïve patients with a high VL, and in ART-experienced patients with residual viremia. This study also demonstrated a low prevalence of drug-related side effects. [ABSTRACT FROM AUTHOR]

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity. [ABSTRACT FROM AUTHOR]

The average grade of iron ore is low in China, and it is significant for the effective utilization of iron ore to obtain grades. In the paper, the high-precision model was constructed based on the spectrum of hematite and the multiple hidden layers extreme learning machine with random values and dense connection, which took the output of all previous layers as the input of each hidden layer. It has achieved high precision during the detection of the total iron content for hematite, which meets the requirements for the construction and development of new green and intelligent mines. [ABSTRACT FROM AUTHOR]

Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO2. This study aims to estimate the content of SiO2 and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO2. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO2. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO2 and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO2 and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy. [ABSTRACT FROM AUTHOR]

With the further development of the construction of “smart mine,” the technology of mine unmanned truck has developed rapidly. Road data are an important prerequisite for the mature application of mining and transportation dispatching system and unmanned driving, so it is particularly important to extract the road in open-pit mines timely and accurately. Different from urban roads, mine roads have no clear road edge and more background interference. In view of the above problems, a convolutional neural network called RATT-UNet (R: residual connection; ATT: attention), which combines residual connection, attention mechanism, and U-Net, is proposed to extract mine road from high-resolution remote sensing images. The advantages of the proposed method are listed as follows: first, the well-designed RATT unit combining residual connection and attention mechanism is used to construct the proposed neural network, which improves the network’s perception ability of detailed features. Second, the abundant skip connections and residual connections can improve the information flow, allowing a better network to be designed with fewer parameters. Third, a composite loss function based on structural similarity is proposed, which effectively alleviates the noise and edge blurring phenomenon and improves segmentation quality. Finally, we perform postprocessing optimization operations on the road extraction results. Experimental results demonstrate that the proposed RATT-UNet outperforms all the comparing network models in terms of the quality of extraction results and evaluation indicators in the mining road extraction task. [ABSTRACT FROM AUTHOR]

Iron ore is an important raw material for the steel industry, so it is of great economic significance to determine the grade of the iron ore quickly and accurately. And the TFe content is the main indicator that determines the grade of the iron ore and whether the iron ore can be smelted directly. Unlike manual methods and methods for chemical analysis, the paper uses the selection of band for the near-infrared spectrum based on the pruning method and the two-hidden-layer extreme learning machine based on LU decomposition and seagull optimization algorithm (LU-TELM-SOA) to identify the TFe content. First of all, the paper proposes the selection of band based on the pruning method to retain the sensitive band of the near-infrared spectrum. Aiming at the problems of poor stability and low accuracy of a single LU-TELM (the two-hidden-layer extreme learning machine based on LU decomposition) model, the paper proposes LU-TELM-SOA. The experimental results show that LU-TELM-SOA has the advantages of high accuracy and strong stability. [ABSTRACT FROM AUTHOR]

Based on the extended Huygens–Fresnel integral and the Rytov phase structure function, the closed-form expression of a partially coherent array finite Airy beams (PCAFABs) cutting through the Kolmogorov atmospheric turbulence is derived in the space domain under the paraxial approximation. The characteristics of the PCAFABs evoluting in the atmospheric environment are investigated in detail on the basis of the derived wave propagation formulae. We mainly illustrate the intensity profile of this beam changed with the truncation parameter, coherence length, and turbulence factor at several cross sections of the atmospheric space by means of numerical figures. It is convinced that the present concept and derived conclusions will provide useful exploration for learning the optical properties of the PCAFABs transmitting in the atmospheric turbulence, especially for free-space optical communication area. [ABSTRACT FROM AUTHOR]

In view of the many shortcomings of traditional copper ore detection methods, this paper proposes a new method for copper ore content detection, which is to detect copper ore content through spectral information. And we propose an error compensation extreme learning machine model. Using the error compensation extreme learning machine and the spectral information of the copper ore can quickly and accurately identify the copper content in the copper ore. Finally, the satellite remote sensing data were simulated, and the copper content distribution in the mining area was obtained. This provides guidance for future mining, reclamation and ore prospecting. [ABSTRACT FROM AUTHOR]

A very broad acidity scale (≈40 pK units) for about 400 N‐heterocyclic carbene precursors (NHCPs) with various backbones and electronic features, including imidazolylidenes, 1,2,4‐triazolylidenes, cyclic diaminocarbenes (CDACs), diamidocarbenes (DACs), thiazolylidenes, cyclic (alkyl)(amino)carbenes (CAACs) and mesoionic carbenes (MICs), was established in DMSO by a well examined computational method. Varying the backbone structure or flanking N‐substituents can have different extent of acidifying effects, depending on both the nature and number of substituent(s). The Gibbs energies (ΔGrs) for the reactions between the corresponding NHCs and CO2 were also calculated. There is a good linear correlation between the pKas of most NHCPs and ΔGrs, suggesting that a greater basicity of NHC leads to a more stable NHC‐CO2 adduct. Interestingly, the nearby asymmetric environment has virtually no differential effect on the acidities of the chiral NHCP enantiomers, but has a pronounced effect on the ΔGr values. [ABSTRACT FROM AUTHOR]

Loxoprofen sodium (LOX) is a propionic acid derivative non-steroidal anti-inflammatory drug (NSAID), existing in the form of four stereoisomers. The main metabolites of LOX in vivo were trans - and cis -alcohol, each consisting of four stereoisomers. The objectives of the present study were to examine the selective pharmacokinetic behavior of LOX stereoisomers and stereoselective formation of its alcohol metabolites in rats based on a chiral liquid chromatographytandem mass spectrometry (LC-MS/MS) method by using an FLM Chiral NQ-RH column, which was reported for the first time. The significant difference in pharmacokinetic parameters of four stereoisomers indicated that stereoselective behavior has occurred in rats after oral administration of LOX. (1′ S ,2 S)-LOX showed the highest concentration among the four stereoisomers in both plasma and urine samples. Trans - and cis -alcohol metabolites of LOX were also detected in plasma and urine, and trans -alcohol was found to be primary and the stereoisomeric fractions (SF s) of its four stereoisomers at different times were calculated. Examination of the stereoisomeric composition indicated a stereo preference for (2 S)-configuration with respect to trans -alcohol formation. The overall results of the present study revealed the enantioselective pharmacokinetic properties of LOX stereoisomers in rats, which provided a means to advance understanding of the complex pharmacokinetic of LOX. [ABSTRACT FROM AUTHOR]

Currently, a number of crowdsourcing-based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real-time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software-defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA) is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput. [ABSTRACT FROM AUTHOR]

Eastern China has a long history of coal mining and a large number of coal mine sites have been abandoned in this region leaving a legacy of mining-related land subsidence, coal waste piles and brownfield sites. Furthermore, as a result of the historical planning approach in the region, many cities were built around coal mines. Therefore, the ecological restoration of abandoned mine land has become vital to the sustainable development in these cities. This paper uses the Datong coal mine site in Huainan as a case study to develop a detailed ecological restoration plan and functional division for an abandoned underground coal mine land by considering the local conditions and mining history, site assessment and integrated monitoring tools. [ABSTRACT FROM AUTHOR]

Abstract: Amorphous oxide semiconductor InGaZnO (a‐IGZO) thin films were deposited on glass and sapphire substrates using RF magnetron sputtering at room temperature. H2 annealing effect on the electrical conduction, optical transmittance, and structural properties of a‐IGZO films has been systematically studied. Temperature‐dependent electrical conductivity of a‐IGZO films exhibited n‐type degenerated conduction behavior in the range of 10–300 K. Electrical properties of the films do not fit with variable range hopping carrier transport model. Optical transmission of a‐IGZO films has been enhanced by H2 annealing at elevated temperatures. Fourier‐Transform Infrared Spectroscopy analysis indicated formation of O‐H bonding state in H2‐annealed a‐IGZO films which is thermally stable up to 300°C. Electron concentration of a‐IGZO films decreases with annealing temperature above 300°C resulting from the reduced H incorporation from OH absorption. [ABSTRACT FROM AUTHOR]

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model's accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect. [ABSTRACT FROM AUTHOR]

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry. [ABSTRACT FROM AUTHOR]

Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately. [ABSTRACT FROM AUTHOR]

• A remote sensing inversion method for saline and alkaline land based on D-TELM. • The advantages of this method are high accuracy. • The different hidden layers of D-TELM have different activation functions. In order to obtain the distribution of saline and alkaline land in Wuyuan County and take appropriate improvement measures, it is necessary to invert the distribution map accurately. This paper introduces an analysis method for saline and alkaline land, which is used to analyze the salt content (‰), organic matter content (g/kg), and PH value. First of all, thirteen saline and alkaline land samples of Wuyuan County were collected in the field. Then, the spectral data was acquired by the SVC HR-1204 portable spectrometer, and the salt content, organic matter content, and PH value data were acquired by chemical analysis. Taking different hidden layers with different activation functions as the innovation point, the TELM is improved to propose D-TELM algorithm. Finally, D-TELM was used to construct three model of the relationship between the spectral data and the salt content, organic matter content, and PH value data, and the inversion and distribution maps were performed. The experimental result shows that most of the D-TELM models are better than the TELM models. According to the principle of minimum standard deviation, the inversion model of salt content uses the dsig-sin model, the inversion model of organic matter content uses the tanh-dsig model, and the inversion model of pH value uses the dsig-sin model. [ABSTRACT FROM AUTHOR]

Summary: Smart city can integrate cloud computing, big data, Internet of Things, edge computing, and other modern information technologies, which focuses on integration, sharing, utilization, and service of information resources, and emphasizes the cooperation and coordination of urban management, to achieve deep integration of industrialization and informatization. Data center network planning based on big data is involved in rational deployment and interconnection of network infrastructures, which can improve the network survivability, energy‐efficiency, flexibility, etc. Therefore, a survivable network planning model based on software‐defined networking is presented in this paper. The elastic network planning problem is formulated as a mixed‐integer optimization whose objective is to minimize the number of unprotected nodes. Then, 3 effective schemes including K‐means clustering algorithm based on simulated annealing, greedy routing algorithm, and Lagrangian relaxation algorithm are proposed for feasible solutions. Numerical results on practical network topology reveals that (1) K‐means clustering algorithm based on simulated annealing can make effective classification of different scale topologies; (2) compared with the traditional random deployment method, the greedy routing algorithm and Lagrangian relaxation algorithm can generate the controller deployment strategy more reasonably, and thus improve network survivability and deployment cost‐benefit. We present a survivable network planning model based on SDNThe elastic network planning problem is formulated as a mixed ILP problem to minimize the number of unprotected nodesThree effective schemes including K‐means clustering algorithm based on simulated annealing, greedy routing algorithm, and Lagrangian relaxation algorithm are proposed for feasible solutions. [ABSTRACT FROM AUTHOR]

• Pretreatment combined algorithms combining multiple mathematical transformations and dimensionality reduction algorithms to comprehensively process raw spectral data. • The advantage of this method is high accuracy". • A method for selecting the optimal Pretreatment combined algorithm is proposed and can accurately predict the accuracy of skarn-type iron ore. Iron ore grade determination is a key step in mining production, and its efficiency has a significant impact on mining efficiency and profitability. At present, the most commonly used method for ore grade determination is chemical analysis, which not only is expensive but also has a long assay cycle and lag effect relative to the ore allocation process and cannot effectively reduce the loss depletion rate of ore mining. Therefore, in situ iron ore grade data determination technology based on visible and near-infrared hyperspectral data analysis is an effective way to solve the above problems, but the poor quality of raw hyperspectral data and low accuracy of the ore grade inversion model are still one of the bottlenecks in the application of this technology. In this paper, 225 ore samples were collected from Hongling skarn-type iron ore in Chifeng, Inner Mongolia Autonomous Region, China. The visible and near-infrared hyperspectral data of the sample set and the grade data obtained from chemical analysis were used as data sources. The raw spectral data were first smoothed, and then the smoothed spectral data were processed using two algorithms, continuum removal (CR) and standard normal variate transform (SNV). Then, two dimensionality reduction algorithms, principal component analysis (PCA) and genetic algorithm (GA), were used to reduce the dimensionality of the spectral data before and after preprocessing, and the data sources were obtained after the four different preprocessing combination algorithms were processed. Finally, several quantitative inversion models were established based on the random forest algorithm and the backpropagation neural network (BPNN) algorithm. The stability, accuracy and credibility of the models were evaluated by four metrics: the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation(RPD) and mean relative error (MRE). The results showed that the quantitative inversion model (SNV-PCA-BPNN) established based on the BP neural network algorithm using the data processed by SNV and PCA for dimensionality reduction was the best, with an R2 of 0.99, an RMSE of 0.0048, an MRE of 1.10% and a RPD of 37.70, providing an effective method for the high-precision inversion of iron ore grades. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for rapid and accurate detection of soil organic matter content. • The method is based on reflectance spectroscopy and machine learning. • Quality band combinations are obtained by spectral pre-processing methods. • Genetic strategy is introduced into the model to enhance its exploration capability. • We propose an improved hybrid extreme learning machine model. The continuous exploitation of mining resources and a number of other factors have led to serious soil pollution problems. Soil organic matter (SOM) can be used as an important measure of soil fertility and ecological environment quality in mining areas. The traditional method of measuring SOM content by chemical methods is costly and time-consuming, so it is important to develop a rapid, accurate and economical method to measure SOM content in reclaimed mines for the ecological restoration and the evaluation of the ecological reclamation effect of mines. In this paper, the visible-near infrared spectral data of soil were obtained using an open pit mine as the study area. The three pre-processing methods of multiple scattering correction, continuous removal and Savitzky-Golay smoothing were compared. Then the two-band ratio spectral index RI was constructed to obtain the sensitive band combinations. To address the problem that the HHO-TELM hybrid model tended to converge prematurely, the GHO-TELM model was proposed in this paper. GHO-TELM introduced a gene learning strategy in HHO-TELM. The crossover, mutation and selection mechanisms were used to cultivate high-quality examples, enhance the quality of individuals and prevent premature convergence of the model. GHO-TELM was compared with several machine learning models. The experimental results showed that GHO-TELM obtained the best performance indexes, with correlation coefficient (r) of 0.9586, and relative percentage deviation (RPD) of 3.0823, indicating that the model had a good prediction effect. [ABSTRACT FROM AUTHOR]

• The spectral data are pre-processed using three dimensionality reduction algorithms. • Optimize the parameters of the dimensionality reduction algorithm. • Establishment of a high-precision grade inversion model for Derni copper deposit. At present, chemical analysis is the main method for copper ore grade verification. Because of its long test cycle and lag effect relative to mining and ore blending processes, this approach cannot effectively reduce losses in mineral resource mining. An effective way to solve this problem is to conduct research on in situ grade determination methods for ore bodies based on visible-near-infrared hyperspectral grade modelling techniques. However, the accuracy of the modelling is seriously reduced by the large redundancy of hyperspectral data and the self-limiting nature of some machine learning methods. Therefore, the accurate selection of dimensionality reduction algorithms and machine learning algorithms determines the accuracy of the inverse model, and the setting of parameters such as dimensionality reduction, number of nearest neighbour points and number of hidden layers in neural networks is crucial. In this paper, 190 ore samples were collected from the open pit of the Derni copper deposit in Dawu town, Guoluo Tibetan Autonomous Prefecture, Qinghai Province, China. First, the visible-near-infrared spectral data for the sample set were obtained with an SVC HR-1024 spectrometer. The copper grade of the sample set was verified by chemical analysis. Three algorithms—the Laplacian eigenmap (LE), local tangent space alignment (LTSA) and local linear embedding (LLE)—were used to reduce the dimensionality of the original hyperspectral data. The original data and the three hyperspectral datasets after dimensionality reduction were used as data sources. Several Derni copper grade inversion models based on two machine learning algorithms, the backpropagation neural network and radial basis function (RBF) neural network, were developed. Finally, the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the accuracy of each model. The results show that the accuracy of the model built with spectral data processed by the dimension reduction algorithm is higher than that of the model constructed with the original spectral data. Among the above combined models, the LLE-RBF model has the highest inversion accuracy. The MAE of the model is 0.117%, the RMSE is 0.136, and R2 is 0.934. [ABSTRACT FROM AUTHOR]

• Combination of multiple pre-processing methods to comprehensively process raw spectral data. • The advantages of this method are high accuracy. • A method for selecting the optimal principle of spectral index is proposed. With saline-alkali land in Zhenlai County, Baicheng city, Jilin Province, China as the research object, a quantitative inversion model for the heavy metal content of manganese (Mn), cobalt (Co) and iron (Fe) in saline-alkali soil and the visible-near-infrared spectroscopy data were established. First, Savitzky-Golay (SG) smoothing, multivariate scattering correction (MSC), continuum removal (CR), and combined transformation methods were performed on the original spectral data. By analysing the correlation between processed data and heavy metal content, the characteristic bands corresponding to different spectral transformations were extracted. Next, the ratio index (RI), difference index (DI) and normalized difference index (NDI) were constructed, and the spectral index was determined to have the most significant correlation with the combination of Mn-, Co- and Fe-content-sensitive bands and its corresponding Spearman rank correlation coefficient. Finally, a quantitative inversion model for the heavy metal content (Mn, Co and Fe) in soda saline-alkali land was established, and its accuracy was verified. The research results show that the optimal band selection principle for the quantitative inversion model of heavy metals in soda saline-alkali land is as follows: Mn selected 219 groups of sensitive bands with correlation coefficient r > 0.70, Co selected 1377 groups of sensitive bands with correlation coefficient r > 0.80, and Fe selected 104 groups of sensitive bands with correlation coefficient r > 0.80. Based on these selection principles, the random forest algorithm was used to perform an inversion for the Mn, Co and Fe contents with the best results, the goodness of fit (R2) values between predicted and measured values were 0.76, 0.92 and 0.91, and the mean relative accuracy was 91.75%, 92.45% and 93.90%, respectively. This method has improved prediction accuracy of the Mn, Co and Fe contents in saline-alkali land. [ABSTRACT FROM AUTHOR]