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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]

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985–1995, 1995–2005, 2005–2015, and 2015–2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management. [Display omitted] • The carbon storage impacted by LULC driving processes in high groundwater coal basins was investigated. • 56.31 % of the study area has undergone land use transition during 1985–2020. • The carbon storage decreased by19.69 % during the study years. • Urbanization and village relocation was the primary driver of carbon loss, ecological restoration was the leading driver of carbon gain. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of post-exposure prophylaxis (PEP) depends on participants adherence, making it crucial to assess and compare regimen options to enhance human immunodeficiency virus (HIV) prophylaxis strategies. However, no prospective study in China has shown that the completion rate and adherence of single-tablet regimens in HIV PEP are higher than those of multi-tablet preparations. Therefore, this study aimed to assess the completion rate and adherence of two HIV PEP regimens. Methods: In this single-center, prospective, open-label cohort study, we included 179 participants from May 2022 to March 2023 and analyzed the differences in the 28-day medication completion rate, adherence, safety, tolerance, and effectiveness of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and tenofovir disoproxil fumarate, emtricitabine, and dolutegravir (TDF/FTC + DTG). Results: The PEP completion rate and adherence were higher in the BIC/FTC/TAF group than in the TDF/FTC + DTG group (completion rate: 97.8% vs. 82.6%, P = 0.009; adherence: 99.6 ± 2.82% vs. 90.2 ± 25.29%, P = 0.003). The incidence of adverse reactions in the BIC/FTC/TAF and TDF/FTC + DTG groups was 15.2% and 10.3% (P = 0.33), respectively. In the TDF/FTC + DTG group, one participant stopped PEP owing to adverse reactions (1.1%). No other participants stopped PEP due to adverse events. Conclusions: BIC/FTC/TAF and TDF/FTC + DTG have good safety and tolerance as PEP regimens. BIC/FTC/TAF has a higher completion rate and increased adherence, thus, is recommended as a PEP regimen. These findings emphasize the importance of regimen choice in optimizing PEP outcomes. Trial registration: The study was registered in the Chinese Clinical Trial Registry (registration number: ChiCTR2200059994(2022-05-14), https://www.chictr.org.cn/bin/project/edit?pid=167391). [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil organic carbon and labile organic carbon fractions decreased in the subsidence waterlogging areas. • The largest carbon depletion was observed in the 0–10 cm soil layer. • The liability of particulate organic carbon was highest among the three types of labile organic carbon fractions. • Labile organic carbon fractions were closely correlated with soil properties. • The shallow waterlogging area has the best carbon accumulation potential. The restoration of subsidence waterlogging areas has the potential to facilitate carbon accumulation in coal mining areas with high ground water tables. However, the soil organic carbon (SOC) and labile organic carbon (LOC) have not been sufficiently investigated in different types of subsidence waterlogging areas. In this study, SOC and LOC fractions, including light fraction organic carbon (LFOC), particulate organic carbon (POC), and dissolved organic carbon (DOC) within 0–50 cm soil profiles, were determined in a reference cropland (RC) and three types of subsidence waterlogging area in Xinglongzhuang Coal Mine, Shandong Province, China, including an aquaculture waterlogging area (AWA), deep waterlogging area (DWA) and shallow waterlogging area (SWA) with water depths of 1.8, 3.2, and 0.65 m, respectively. Compared with RC, SOC and LOC decreased by 4.13%, 25.03%, and 77.91% in SWA, DWA, and AWA, respectively. The largest depletion was in the 0–10 cm layer, indicating that there was a time lag for the recovery of the total functional pools of SOC after subsidence. The lability of POC was highest among the three types of LOC fractions, indicating that mining subsidence triggered soil redistribution and impacted on carbon accumulation. With comparatively little soil profile interference in DWA and SWA, the lability of LFOC and POC decreased with soil depth, while the DOC increased. There was a large discrepancy in AWA, with the lability of LOC fractions fluctuating because of soil disturbance by intensive engineering. The LOC fractions were closely associated and correlated with soil properties, especially total nitrogen (TN) and total phosphorus (TP). Restoration and management of the subsidence waterlogging areas have a vital impact on the SOC pool. It was concluded that shallow water and a natural geomorphology-based topography, with little soil disturbance, well-developed vegetation communities, and continuous monitoring of soil and water properties would facilitate the carbon accumulation process. [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]

Based on the whole life cycle theory, the carbon effect of three different sizes of high-standard basic farmland construction projects is measured and analysed. The results show that the carbon emissions generated during the construction of high-standard basic farmland projects and the carbon absorption capacity at the later stage are different for projects of different sizes. The carbon emissions of different scales of high-standard basic farmland projects will increase during the construction stage. The results of carbon effect changes in the later operation and management stage show that the high-standard basic farmland construction projects will help reduce the carbon emissions of the field ecosystem where the farmland is located and increase its carbon sink capacity after the completion of construction, which is more obvious in larger projects. The emission reduction and carbon sequestration capacity of the farmland after remediation are improved to different degrees, which is more conducive to the ecological development of agricultural production and ecological environmental protection in the relevant areas. The study contributes to the green development of farmland, which is of some significance for the sustainable development of agriculture in Tianjin and the whole country. [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]

Introduction: Bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and dolutegravir plus lamivudine (DTG + 3TC) are well tolerated and effective in clinical trials. This study aimed to evaluate the safety and efficacy of these two schemes in a real-world setting and to obtain the first dataset for switching to BIC/FTC/TAF and DTG + 3TC in a Chinese population. Methods: This retrospective single-center cohort study in China included participants who switched to DTG + 3TC or BIC/FTC/TAF between January 2020 and February 2023. The main endpoint was the proportion of participants with HIV-1 RNA levels of ≥ 50 copies/mL. Safety, tolerance, and the incidence of low-level viremia (LLV) were evaluated. Results: A total of 525 participants were included, 454 of whom were included in the PP analysis. At week 48, the proportions of participants with HIV-1 RNA ≥ 50 copies/mL were 4.4% (10/225) for DTG + 3TC and 6.1% (14/229) for BIC/FTC/TAF; virological efficacy did not differ significantly between the two groups. Consistent results were obtained in an intent-to-treat (ITT) analysis. The incidences of LLV were 3.6% (7/193) and 4.9% (10/206), respectively. During the study, none of the participants stopped taking drugs because of a lack of efficacy or adverse reactions. Conclusions: Both regimens are well tolerated and effective for switching HIV-1 infection therapy. However, the detection of genotypic drug resistance should be considered when baseline virological non-suppression is observed. [ABSTRACT FROM AUTHOR]

Introduction: Ainuovirine/lamivudine/tenofovir is a novel antiretroviral therapy regimen used to treat human immunodeficiency virus-1 (HIV-1) infection. This study aimed to compare the pharmacokinetics of ainuovirine/lamivudine/tenofovir in HIV-1-infected patients aged ≥ 65 (elderly patients) and ≤ 40 years (young patients). Methods: This prospective, open-label, parallel controlled clinical study included 15 young and 15 elderly patients. Blood (1 mL) was collected 30 min before dosing and at 0.5, 1, 1.5, 2, 3, 4, 8, 12, 16, and 24 h after dosing, to measure the plasma concentrations of ainuovirine/lamivudine/tenofovir. Safety was assessed by monitoring the adverse events, physical examinations, and clinical laboratory tests. Results: Plasma concentrations of each ainuovirine/lamivudine/tenofovir component reached peak levels 1–4 h after dosing and gradually decreased during the remaining observation period. Compared with the young group, ainuovirine had significantly higher T1/2Ke, AUC0–24, and AUC0–inf (all P < 0.05) in the elderly group, whereas Ke (P = 0.002) was significantly lower. However, the Cmax and Tmax of ainuovirine did not differ significantly. Lamivudine and tenofovir also had a significantly higher Cmax (p = 0.004 and p = 0.008, respectively) and AUC0–inf (P = 0.014 and P = 0.006, respectively) in the elderly group, whereas there was no significant difference in Tmax, Ke, and T1/2Ke. Ainuovirine/lamivudine/tenofovir was well tolerated in both the young and elderly groups. Conclusion: This study suggests that the ainuovirine/lamivudine/tenofovir regimen might be an effective and safe treatment regimen for HIV-1-infected patients aged ≥ 65 years and ≤ 40 years. Further studies are needed to confirm these results and develop optimal dosing regimens for elderly HIV-1-infected patients. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

The photoelectric properties of lead halide perovskite have been extensively investigated. However, its application in the field is greatly limited due to its poor instability and high lead content. With the aim of addressing these problems, in this study, Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals were initially synthesized using a simple precipitation method. By manipulating the lead concentration, the emission ranging from cyan to green could be adjusted in Cs 2 ZnBr 4 :Pb2+. Co-doped Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals with the precise regulation of the doping concentration of Pb2+ and Mn2+ were obtained through precipitation. The dual-mode emission of cyan and orange-red is successfully obtained. It is important that single white light emission is achieved in Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals, exhibiting tunability from cold white to warm white. Moreover, Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals are demonstrated remarkable air stability: it retains 86.27 % of the initial fluorescence intensity after 45 days in ambient conditions; it exhibits excellent UV stability, with 88.3 % of the initial fluorescence intensity maintained even after nearly 30 h of ultraviolet irradiation. Therefore, this research is anticipated to have broad applications not only in the field of solid-state lighting as UV-excited white LEDs but also in serving as a valuable reference for the exploration of low-lead halide luminescent materials. [ABSTRACT FROM AUTHOR]

Loxoprofen sodium is a chiral drug with two chiral centers. In our previous work, we found that the elimination of its four isomers showed stereospecificity in rats, while how the stereospecific behavior occurred in vivo was unclear. To clarify this issue, each single isomer of loxoprofen sodium was prepared by a chiral semi‐preparative high‐performance liquid chromatography (HPLC) and then administered to rats. By analysis of each isomer in rat plasma utilizing an analytical chiral HPLC, it was discovered that the chiral inversion occurred only to its (2R)‐isomers, one from (1′S,2R)‐ to (1′S,2S)‐isomer and the other from (1′R,2R)‐ to (1′R,2S)‐isomer. The reduction of α‐substituted cyclopentanone occurred only to its (1′R)‐isomers, with (1′R,2R)‐isomer reduced to (2′S,1′R,2R)‐trans‐alcohol and (1′R,2S)‐ to (2′S,1′R,2S)‐trans‐alcohol. Interestingly, both the inversion and the reduction reaction occurred to its (1′R,2R)‐isomer due to the special stereo‐structure with both (2R)‐ and (1′R)‐configuration, and conversely, neither of them occurred to its (1′S,2S)‐isomer, which caused the significantly different elimination rate in vivo. These new findings were meaningful for evaluation of the safety and efficacy of chiral drugs. [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]

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]

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]

• 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]

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]

In this article, the in-situ passivation and encapsulation strategy of perovskite quantum dots (PQDs) is proposed using inorganic transparent host (FB, FIREBIRD) and PMMA， respectively. CsPbBr 3 are deposited on the surface of FB and FB@ CsPbBr 3 composites are obtained. Then FB@CsPbBr 3 is coated with PMMA and FB@CsPbBr 3 /PMMA is prepared. The in-situ passivation and encapsulated CsPbBr 3 is very stable in the moisture, heat, and irradiation environment due to the hydrophobic and thermal insulation layers. Meantime, the luminous efficiency of FB@CsPbBr 3 /PMMA is 70.96%. Furthermore, the white LED (WLED) device is fabricated by combining as-prepared FB@CsPbBr 3 /PMMA composites, FB@CsBrI 2 /PMMA composites and a blue LED chip. The Commission Internationale de I'Eclairage (CIE) color coordinates of the fabricated WLED is (0.29, 0.29). A color area of LED device is about 128% of the National Television Standards Committee or 99% of the Rec. 2020 standards. This work provides a novel in-situ passivation and encapsulated strategy through a combination of thin-film packaging and heat-insulating transparent materials which presents a new idea and solution for improving the stability of CsPbBr 3. This work provides a novel in-situ passivation and encapsulated strategy through a combination of thin-film packaging and heat-insulating transparent materials which presents a new idea and solution for improving the stability of CsPbBr 3. [Display omitted] • The in-situ passivation and encapsulated CsPbBr 3 QDs are very stable in the moisture, heat, and irradiation environment due to the hydrophobic and thermal insulation layers. • The luminous efficiency of FB@CsPbBr 3 /PMMA is 70.96%. • The Commission Internationale de I'Eclairage (CIE) color coordinates of the fabricated WLED is (0.29, 0.29). A color area of LED device is about 128% of the National Television Standards Committee or 99% of the Rec. 2020 standards. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for rapid coal proximate analysis is proposed. • The method combines spectroscopy with deep learning. • A deep learning model named DR_TELM is proposed, which includes dilated convolution, multi-level residual connection and TELM. • The method proposed achieves high precision and fast speed. Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R2) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal. [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]

[Display omitted] • A method for rapid and accurate detection of cadmium content in saline soils is proposed. • The method is based on visible-near-infrared spectroscopy and machine learning. • We pre-processed the original reflection spectra using fractional order derivatives. • We constructed three-band spectral index by optimal band combination algorithm, and obtained the corresponding optimal band combinations. • We proposed the MSC-ELM model based on the two-hidden-layer extreme learning machine. Heavy metal pollution in saline-alkali land has a significant impact on the ecological environment and human health. Rapid and accurate inversion of cadmium (Cd) element content in the saline-alkali land is important for environmental protection, saline-alkali soil improvement and conversion of saline-alkali land to cultivated land. Using traditional chemical detection methods to detect the content of heavy metal elements requires a long testing time and has the drawback of high prices. In this paper, we select the saline-alkali land of Zhenlai County as the study area and combine visible-NIR spectroscopy with machine learning models to invert the Cd content in the saline-alkali land. We preprocess the original reflection spectra using fractional order derivatives (FOD), then construct six three-band spectral indices (TBIs) and obtain the corresponding optimal band combination parameters by the optimal band combination (OBC) algorithm. To address the shortcomings of two-hidden-layer extreme learning machine (TELM), this paper introduces new weight parameters among the nodes of the first hidden layer, further extends it to multiple layers on this basis, and proposes the MSC-ELM model. The improved model is compared with several models, such as random forest (RF), partial least squares (PLS) and extreme learning machine (ELM). And the model performance is analyzed and compared by introducing several performance indicators, such as root mean square error (RMSE) and the ratio of the performance to interquartile (RPIQ). The experimental results show that the FOD transformation can eliminate the baseline drift and reduce the spectral noise. The constructed TBIs can effectively enhance the correlation with Cd content relative to the original single band, reduce redundant information and enhance the spectral features. The MSC-ELM model achieves better performance metrics compared to the other models and obtains the optimal prediction performance. This study provides an accurate and rapid method for the detection of Cd content in saline soil, which is important for the improvement and ecological recovery of saline soil. [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]

• 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]

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]

• 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]

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]