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The genesis of the giant Ag-Cu-Pb-Zn polymetallic mineralization in the northern Lanping basin, Southwest China, remains controversial. To address the sources of metals, a systematic study on Hg isotope compositions was conducted for the Cu-dominated deposit at Baiyangping and the Pb-Zn-dominated deposits at Fulongchang and Liziping. The Cu deposit shows positive Δ199Hg signatures (0.14 ± 0.13‰), in contrast to the Δ199Hg of the Pb-Zn deposits (− 0.09 ± 0.06‰). As positive Δ199Hg values are commonly observed in marine sediments and the Lanping Triassic marine sedimentary rocks show exclusively positive Δ199Hg signals (0.03 ± 0.07‰), the Hg in Cu ores was mainly sourced from the Triassic strata. The negative Δ199Hg signals observed in the Pb-Zn deposits, typical of terrestrial Hg, agree roughly with those of the Jurassic to Paleocene terrestrial sedimentary rocks (− 0.05 ± 0.08 ‰), indicating that the terrestrial strata provided the Hg in Pb-Zn ores. Compared to the source rocks, the Cu deposit shows isotopically lighter Hg enrichments (δ202Hg = − 2.30 ± 0.35‰), possibly resulting from fractionations induced by Hg2+ sorption, organic complexation, and precipitation of Hg-bearing sulfides. The Pb-Zn deposits show comparable or slightly heavier δ202Hg (− 0.56 ± 0.48‰); moreover, δ202Hg values of late-stage sulfides are higher than early-stage δ202Hg values, suggesting that the δ202Hg variation was primarily caused by sulfide precipitation. Thus, Hg isotope data indicate that separate hydrothermal events resulted in Cu and Pb-Zn mineralization. More importantly, this study reveals the great potential of Hg isotopes to discriminate sedimentary sources of metals for low-temperature hydrothermal deposits.

The present work aimed to prepare new starch-based Pickering emulsions for topical application of resveratrol. A sustained-release profile was observed for up to 12 h. Superior stability was achieved for 60 days at 4 °C and 25 °C compared to −20 °C and 40 °C. Good centrifugal stability and dilution stability were shown at different conditions. The entrapment efficiency (EE) was maintained at over 85% in various situations. DPPH radical scavenging study confirmed that resveratrol-loaded starch-based Pickering emulsions had a more conspicuous sustained-release effect on resveratrol, and the antioxidant capacities of resveratrol were not affected. Compared to the resveratrol ethanol solution, the resveratrol-loaded starch-based Pickering emulsions showed a higher ability to improve skin permeation of resveratrol and significantly enhanced the distribution of resveratrol in the skin as well. These studies demonstrated that starch-based resveratrol-loaded Pickering emulsions might be a promising drug delivery system for topical application of resveratrol.

The Jinding deposit in the Lanping basin, southwest China, is the largest sandstone-hosted Zn deposit in the world and the second largest Zn-Pb deposit in China. However, questions related to the metal compositions and origin of the ore fluids remain. In this study, microthermometry and laser ablation-inductively coupled plasmamass spectrometry (LA-ICP-MS) were employed to determine the properties and compositions of individual fluid inclusions trapped in sphalerite and calcite. The results show that the fluid inclusions trapped in sphalerite and calcite have similar homogenization temperatures (79°–173°C with the majority 100°–130°C), salinities (10.3–29.1 wt % NaCl + CaCl2 equiv with the majority 24.5–27.4 wt % NaCl + CaCl2 equiv), and concentrations of alkali and alkali earth elements (e.g., Na, Ca, Mg, K, Sr, Ba, Li, Rb, and Cs). However, the concentrations of ore and associated metals (e.g., Pb, Sb, Ag, and Tl) in the fluid inclusions hosted by sphalerite are significantly higher than those hosted by calcite. Based on these observations, we propose that the sulfides including sphalerite were precipitated from a low-temperature, high-salinity, Ca-rich, metal-rich fluid, while the gangue minerals such as calcite crystallized subsequently from fluids depleted in metals due to prior precipitation of sulfides, and that the high salinities of the fluid inclusions are likely due to a combination of seawater evaporation and subsequent dissolution of evaporitic sequences during fluid percolation. The LA-ICP-MS analyses reveal that the fluid inclusions have K/Na, Rb/Na, and Cs/Na ratios within the range of modern basinal brines, and Li/Na, Ba/Na, and Ca/Na ratios share similar compositions with the ore fluids of basement interacted deposits in the world. The Jinding ore fluids contain ~200 to 650 ppm Pb, based on the data of fluid inclusions trapped in sphalerite. The estimated concentrations of Zn in the ore fluids are also very high at ~200 to 6,500 ppm. Our results reveal that anomalously metal rich fluids played a critical role in the formation of the giant Jinding sediment-hosted Pb-Zn deposit. We concur with the previous suggestion that sulfide precipitation at Jinding occurred when ascending metal-rich brines encountered an H2S-rich, Ca-rich fluid, which was produced by interaction of hydrocarbons with evaporites, in the cap of the Jinding dome.

The Baiyangping Cu–Ag polymetallic ore district is located in the northern part of the Lanping-Simao foreland fold belt, between the Jinshajiang-Ailaoshan and Lancangjiang faults, and the deposit can be divided into eastern and western ore zones. Based upon microscope observation of ore minerals and analysis of zinc, copper, and strontium isotope composition, we conclude that: (1) the zinc isotopic compositions of sphalerite from the eastern and western ore belt of the Baiyangping polymetallic ore deposits are enriched in both the heavy (− 0.09‰ to + 0.15‰) and light (− 0.19‰ to − 0.01‰) zinc isotopes. Rayleigh fractionation is likely the additional factor controlling the observed temporal and spatial variations in zinc isotopes in the two studied ore zones. The zinc isotopic composition in the Baiyangping polymetallic Pb–Zn deposits may have the same fractionation as that of magmatic-hydrothermal, VHMS, SEDEX, and MVT deposits, as demonstrated by geological and other geochemical evidence; (2) the range of δ65Cu in massive tetrahedrite is from − 0.06‰ to + 0.12 ‰ that relates to the early stages of ore-formation, which are higher than that of venial chalcopyrite (from − 0.72‰ to − 0.07‰) formed at a late ore-forming stage in the western ore belt. Different ore-forming stages and alteration or leaching processes are likely the main factors controlling the observed variations in copper isotopes in the western ore zone; (3) the 87Sr/86Sr value of hydrothermal calcite in eastern (0.7080–0.7093) and western (0.7085–0.7113) ore belt suggested that mineralization of early calcite, with 87Sr/86Sr values much higher than in ancient Late Triassic seawater, may be related to recrystallization from a radiogenic Sr-rich or silicifying fluid, either from the strata that the ore-forming fluid flows through or from other fluids.

The reduced nature of the subducting slab has been used to explain the relatively poor endowment of economic porphyry copper deposits in Paleo-Tethyan arc systems. The presence of numerous porphyry Cu deposits in the southern Yidun terrane potentially challenges this model, because these deposits are thought to be the product of Paleo-Tethyan subduction. To investigate this, two representative deposits (i.e., Pulang and Songnuo porphyry Cu deposits) from the southern Yidun terrane have been studied. Zircon U-Pb ages of pre- and syn-mineral porphyries in Pulang are 221 ± 2 Ma (MSWD = 0.89) and 215 ± 2 Ma (MSWD = 1.50), respectively. Zircon and garnet U-Pb ages of the Songnuo syn-mineral porphyry are 217 ± 2 Ma (MSWD = 1.15) and 223 ± 5 Ma (MSWD = 0.97), respectively. The Pulang and Songnuo porphyries both have high whole-rock Sr/Y ratios with minor or no negative Eu anomalies, consistent with derivation from hydrous magmas that underwent minor early plagioclase crystallization but abundant amphibole fractionation. The two porphyry intrusions are characterized by high large-ion-lithophile elements (LILE: Th, U, Ba, Rb) abundances, and low concentrations of high-field-strength elements (HFSE: Nb, Ta, Ti, Zr, Hf). They have similar initial Sr isotope ratios from 0.7055 to 0.7073, ƐNd (t) values from − 4.3 to − 1.6 and zircon ƐHf (t) values of − 1.81 ± 1.34 (n = 47) that are distinct from published results for the Permian–Early Triassic Paleo-Tethyan arc granitoids in the Sanjiang region but similar to the Neoproterozoic arc intrusions in the nearby western Yangtze craton. The Pulang and Songnuo porphyries have relatively high zircon EuN/EuN* ratios (0.59 ± 0.04, n = 52) and calculated Δ log fO2 (FMQ) values (1.61 ± 0.23, n = 52), consistent with relatively oxidized magmas. This feature is similar to the published data for the Neoproterozoic arc intrusions (EuN/EuN* = 0.55 ± 0.08; Δ log fO2 (FMQ) = 2.14 ± 0.60), but distinct from the published data for the reduced normal Paleo-Tethyan arc magmas (EuN/EuN* = 0.29 ± 0.10; Δ log fO2 (FMQ) = − 0.68 ± 0.88). Given that the most recent tectonic reconstruction models suggest that the Paleo-Tethys Ocean closed before ~ 220 Ma, we propose that the Pulang and Songnuo porphyries Cu deposits were formed in a postsubduction setting from oxidized melts generated by reactivating the Neoproterozoic arc root, unrelated to Paleo-Tethyan subduction.

Compared with coarse-grained forecasting, fine-grained tourism demand forecasting is a more challenging task, but research on this issue is very scarce. To address this issue, a decomposition ensemble deep learning model is proposed by integrating CEEMDAN, CNNs, LSTM networks, and AR models. The CEEMDAN can decompose complex tourism demand data into multiple components with simpler characteristics, thereby reducing the complexity of forecasting. The CNNs and LSTM networks can fully capture the locally recurring patterns and the long-term dependencies of the components obtained by CEEMDAN. The AR model can capture the scale of tourism demand data, which can overcome the problem that the output scale of the deep neural networks (i.e., CNNs and LSTM networks) is not sensitive to the scale of the inputs. The effectiveness of the proposed model is verified by comparing with five benchmark models using real-time data on tourist volumes at two attractions.

Most of the World’s large tungsten deposits are genetically related to fluorine-rich granitic magmas, their ores contain fluorine minerals, such as fluorite and topaz, and their host rocks have commonly undergone fluorine metasomatism to form greisens. In view of this evidence that tungsten ore fluids are enriched in fluorine, and the potential for fluoride to form stable complexes with hard acids (e.g., W6+), we investigated the solubility and speciation of tungsten in fluoride-bearing fluids at hydrothermal conditions. Experiments were conducted to measure the solubility of tungsten trioxide solid in fluoride-bearing aqueous fluids at temperatures between 100 and 250 °C and vapour-saturated water pressure. Based on the results of these experiments, tungsten is dissolved predominantly as H3WO4F2− in solutions containing 0.05–0.30 m HF at pH(T) values between 1.5 and 3.4. Significantly, the concentration of H3WO4F2− was up to two orders of magnitude higher than that of tungstate species in these experimental solutions. This species formed via the reaction WO3cryst + H2O + 2HF = H3WO4F2− + H+, logarithms of the equilibrium constants for which, vary from −4.39 ± 0.18 at 100 °C to −5.10 ± 0.17 at 250 °C. In solutions containing

This study explores how to select the optimal number of lagged inputs (NLIs) in international tourism demand forecasting. With international tourist arrivals at 10 European countries, the performances of eight machine learning models are evaluated using different NLIs. The results show that: (1) as NLIs increases, the error of most machine learning models first decreases rapidly and then tends to be stable (or fluctuates around a certain value) when NLIs reaches a certain cutoff point. The cutoff point is related to 12 and its multiples. This trend is not affected by the size of the test set; (2) for nonlinear and ensemble models, it is better to select one cycle of the data as the NLIs, while for linear models, multiple cycles are a better choice; (3) significantly different prediction results are obtained by different categories of models when the optimal NLIs are used.