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Determining the source and history of hydrothermal fluids are key to better understanding the genesis of lode gold deposits. However, it is difficult to rigorously constrain the fluid history because the fluids typically have a prolonged and complicated history that may obscure the original source signature. We integrate textural characterization, trace element geochemistry, and in situ oxygen isotope analyses of quartz covering three major paragenetic stages of the world-class Dongping lode gold deposit to fingerprint the ore-fluid source and document the deposit’s complex hydrothermal history. Six quartz generations were identified from three paragenetic stages. They consistently display small to large ranges in δ18O values from 13.1 to 0.6‰ (VSMOW), which correspond to calculated δ18O values of 7.4 to –6.0‰ for the quartz-forming fluids. These isotope data are intermediate between the δ18O ranges of magmatic fluids and meteoric water and thus are best interpreted in terms of episodic fluxes of magmatically derived fluids and their subsequent mixing with variable proportions of external meteoric water. Our results highlight the important role of magmatic hydrothermal fluids and their mixing with meteoric water to form the giant Dongping gold deposit. Given the high abundance of quartz that typically spans the entire mineralization process of lode gold deposits, we envisage the versatility and reliability of in situ oxygen isotope analysis of well-characterized quartz in deciphering the origin and complex evolution history of gold-forming fluids.

Overexpression of carboxyl/cholinesterase (CCE) genes has been reported to be associated with many cases of pesticide resistance in arthropods. However, it has been rarely documented that CCEgenes participate in spirodiclofen resistance in Panonychus citri. In previous research, we found that spirodiclofen resistance is related to increased P450 and CCE enzyme activities in P. citri. In this study, we identified two CCEgenes, PcCCE3and PcCCE5, which were significantly upregulated in spirodiclofen-resistant strain and after exposure to spirodiclofen. RNA interference of PcCCE3and PcCCE5increased the spirodiclofen susceptibility in P. citri. In vitro metabolism indicated that PcCCE3 and PcCCE5 could interact with spirodiclofen, but metabolites were detected only in the PcCCE3 treatment. Our results indicated that PcCCE3 participates in spirodiclofen resistance through direct metabolism, and PcCCE5 may be involved in the spirodiclofen resistance by passive binding and sequestration, which provides new insights into spirodiclofen resistance in P. citri.

Abstract: In this study, a series of organic amine-intercalated α-ZrP were prepared. The crystal phase, structures and properties of these intercalated compounds were analyzed and characterized using several analysis methods. The interlayer distance of organic amine-intercalated α-ZrP has increased regularly along with the increase of chain elongation of organic amine. The composites were applied to adsorb phenol from wastewater. The adsorption equilibrium, kinetics, and dynamic adsorption of phenol onto intercalated α-ZrP were studied. Adsorption isotherms of phenol were determined at different temperatures, and they were well fitted to the Freundlich equation (R2> 0.99), and the corresponding maximum adsorption capacity of phenol was 0.832 mmol/g. Batch kinetic experiments revealed that the adsorption process followed a quasi-second-order kinetic model. The dynamic adsorption revealed that the adsorption capacity for phenol increased with the increase in temperature. The adsorption process was rapid and equilibrium was reached within 30 min. When the adsorption dosage is 0.1 g/15 mL, the adsorption yield for 10 mmol/L phenol solution can reach 61.2%.

This article proposes a gallium nitride (GaN)-based isolated silicon carbide (SiC) MOSFET gate driver with an on-chip metal–insulator–metal (MIM) capacitor that has high data rate and low propagation delay. The improved common-mode transient immunity (CMTI) envelope detection technique eliminates the common-mode current ( $I_{\mathrm {CM}}$ ) to improve the CMTI. In addition, the proposed isolated gate driver (IGD) with quad-drive control (QDC) technique reduces power loss and gate ringing effect. Experimental results show that the proposed IGD can achieve a slew rate of 109 kV/ $\mu \text{s}$ . At a switching frequency of 100 kHz, the efficiency of the half-bridge isolated dc–dc converter can be kept higher than 90% when $V_{\mathrm {IN}}$ changes from 800 to 1700 V, and the peak efficiency is 98.6% when $V_{\mathrm {IN}}$ = 800 V.

To investigate the association between blood calcium concentration and incident kidney stone as well as to assess the role played by genetic susceptibility.

Uncertainty exists about whether lowering systolic blood pressure to less than 120 mm Hg is superior to that of less than 140 mm Hg, particularly in patients with diabetes and patients with previous stroke.

The effects of Gd element on the nanocrystallization of Mg alloys during swaging were studied by means of the microhardness test, tensile test, and transmission electron microscopy. The results indicate that it is impossible to achieve nanocrystallization in pure Mg via rotary swaging, which may be attributed to the difficulty of forming dislocation arrays. In contrast, adding Gd alone can achieve a remarkable grain refinement effect after swaging. In the early stage of swaging, twin lamellae are all formed in the pure Mg and Mg−Gd alloys. As the swaging process continues, more non-basal dislocations are formed in the Mg−Gd alloys than in pure Mg. This promotes the formation of dislocation arrays and the resulting nanocrystallization in the Mg−Gd alloys. As the Gd content increases, the refinement effect is obviously enhanced. The critical content for the refinement of the grain size below 100 nm is about 5.4 wt.%.

All-inorganic two-dimensional (2D) CsSnI3nanosheets exhibit reduced biotoxicity, high mobility, and suitable band gap, making them highly promising for nanoelectronic or optoelectronic applications. However, the synthesis of 2D CsSnI3nanosheets remains challenging. In this study, 2D CsSnI3nanosheets were successfully synthesized by using a space-confined chemical vapor deposition (CVD) method for the first time. The average thickness and lateral size of 2D CsSnI3nanosheets were well-controlled by adjusting the gap distance within the confined space and the growth temperature. This approach also facilitated in situ construction of CsSnI3-based vertical heterostructures by directly growing CsSnI3nanosheets on the surface of target WSe2. The space-confined CVD growth of lead-free perovskite nanosheets will provide more options for the development of low-dimensional perovskite-based optoelectronic devices.

Motor neurons are the final common pathway1through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically2suppress a single class of proprioceptive neuron3that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive–motor loop.

Strongly correlated states commonly emerge in twisted bilayer graphene (TBG) with “magic-angle” (1.1°), where the electron–electron (e-e) interaction Ubecomes prominent relative to the small bandwidth Wof the nearly flat band. However, the stringent requirement of this magic angle makes the sample preparation and the further application facing great challenges. Here, using scanning tunneling microscopy (STM) and spectroscopy (STS), we demonstrate that the correlation-induced symmetry-broken states can also be achieved in a 3.45° TBG, via engineering this nonmagic-angle TBG into regimes of U/W> 1. We enhance the e-einteraction through controlling the microscopic dielectric environment by using a MoS2substrate. Simultaneously, the width of the low-energy van Hove singularity (VHS) peak is reduced by enhancing the interlayer coupling via STM tip modulation. When partially filled, the VHS peak exhibits a giant splitting into two states flanked by the Fermi level and shows a symmetry-broken LDOS distribution with a stripy charge order, which confirms the existence of strong correlation effect in our 3.45° TBG. Our result demonstrates the feasibility of the study and application of the correlation physics in TBGs with a wider range of twist angle.

Cancer treatment has faced severe obstacles due to the smart biological system of cancer cells. Herein, we report a three-in-one agent Ir-CAvia attenuation of cancer cell stemness with the down-regulated biomarker CD133 expression from the mitochondria-directed chemotherapy. Over 80% of Ir-CAcould accumulate in mitochondria, result in severe mitochondrial dysfunctions, and subsequently initiate mitophagy and cell cycle arrest to kill cisplatin-resistant A549R cells. In vitroand in vivoantimetastatic experiments demonstrated that Ir-CAcan effectively inhibit metastasis with down-regulated MMP-2/MMP-9. RNA seq analysis and Western blotting indicated that Ir-CAalso suppresses the GSTP1 expression to decrease the intracellular Pt-GS adducts, resulting in the detoxification and resensitization to cisplatin of A549R cells. In vivoevaluation indicated that Ir-CArestrains the tumor growth and has minimal side effects and superior biocompatibility. This work not only provides the first three-in-one agent to attenuate cancer cell stemness and simultaneously realize anticancer, antimetastasis, and conquer metallodrug resistance but also demonstrates the effectiveness of the mitochondria-directed strategy in cancer treatment.

This paper proposes a novel pipeline to estimate a non-parametric environment map with high dynamic range from a single human face image. Lighting-independent and -dependent intrinsic images of the face are first estimated separately in a cascaded network. The influence of face geometry on the two lighting-dependent intrinsics, diffuse shading and specular reflection, are further eliminated by distributing the intrinsics pixel-wise onto spherical representations using the surface normal as indices. This results in two representations simulating images of a diffuse sphere and a glossy sphere under the input scene lighting. Taking into account the distinctive nature of light sources and ambient terms, we further introduce a two-stage lighting estimator to predict both accurate and realistic lighting from these two representations. Our model is trained supervisedly on a large-scale and high-quality synthetic face image dataset. We demonstrate that our method allows accurate and detailed lighting estimation and intrinsic decomposition, outperforming state-of-the-art methods both qualitatively and quantitatively on real face images.

To investigate the feasibility of co-sintering of fluxed iron ore with magnetite concentrates, the mineralogical properties of a novel fluxed iron ore were studied using particle size analysis, microscopic morphology characterization, and X-ray diffraction Rietveld analysis. Following that, the experiments for granulation performance and basic sintering characteristics were designed under seven different fluxed iron ore ratios, and the integrated ranking of different fluxed iron ore ratios was determined using gray relation analysis. Finally, the results of the industrial trails were combined with the feasibility analysis. Test and experimental results show that the fraction of the fluxed iron ore particles larger than 0.5 mm can account for more than 48%, and the particles have two morphologies: spherical-rough and flaky-smooth. Ca elements are found in the form of calcite (CaCO3) and dolomite (CaMg(CO3)2). The average particle size of granules and powder removal rate can be improved from 2.50 to 3.16 mm and 39.60% to 24.20%, respectively, with the increase in the fluxed iron ore ratio. Furthermore, the fluxed iron ore can improve assimilability and liquid fluidity of magnetite concentrates. In terms of overall granulation performance and sintering characteristics, the fluxed iron ore ratios are graded from best to worst as follows: 12%, 15%, 9%, 18%, 21%, 6% and 3%. The industrial trails show that when the fluxed iron ore ratio is increased, the beneficial effect of the superior sintering characteristics of the fluxed iron ore itself is ideally balanced with the negative effect of the lower amount of additional CaO at 12% ratio, and thus, it is feasible to bring the fluxed iron ore into production at a level of roughly 12%.

In this letter, based on the theory of electromagnetic (EM) wave, an intelligent reflecting surface (IRS) assisted channel model for unmanned aerial vehicle (UAV) communications is proposed. The proposed model reveals the relationship between the received signal power of the IRS and the horizontal rotation angle, the size, and the reflection phases of IRS reflection units. The reflection phases are designed to ensure all signals reflected by IRS are phase aligned, and the theoretical result of the received signal power of IRS assisted link is in direct proportion to the square of the total geometric area of the IRS, which accords with the measurement results obtained in the real outdoor scene. In this letter, the smallest geometric area of the IRS, for which the path loss of the IRS-assisted link is equal to that of the direct link, is investigated. With the increase of the carrier frequency, the smallest geometric area gradually decreases. Finally, we investigate the ergodic sum capacity and obtain the closed-form tight upper bound, which is validated through Monte-Carlo simulation results.

Objective To provide theoretical support for the study of AD pathogenesis and therapeutic targets. Methods The AD data were downloaded from the GEO database for differential expression analysis to obtain DEGs, followed by enrichment analysis of GO and KEGG signalling pathways, construction of machine learning models to screen key genes, and construction of risk prediction models and prediction of transcription factors based on key genes. In addition, consistent clustering analysis was performed on AD samples. Results Seven key genes were finally screened in this study, and the risk prediction model constructed on the basis of these seven genes had an AUC of 0.877. Cluster analysis classified the AD samples into two subtypes, and there was also a significant difference in immune infiltration between the two subtypes. Conclusion This study provides new perspectives and potential therapeutic targets for exploring the potential mechanisms by which mitochondrial autophagy affects AD, as well as providing directions for individualised treatment of AD.

The majority of vehicle structural failures originate from joint areas. Cyclic loading is one of the primary factors in joint failures, making the fatigue performance of joints a critical consideration in vehicle structure design. The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries. Therefore, there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry. In this work, we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis (FEA) results. Different machine learning (ML) algorithms are adopted to investigate the fatigue life of three typical adhesive joints, namely lap shear, coach peel and KSII joints. After the feature engineering and tuned process of the ML models, the preferable model using the Gaussian process regression algorithm is established, fed with eight input parameters, namely thicknesses of the substrates, line forces and bending moments of the adhesive bonded joints obtained from FEA. The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation. It demonstrates that for life prediction of adhesive joints, the data-driven solutions can constitute an improvement over conventional solutions.

To ensure long-term food security, it's vital to detect pests and diseases in crops. Traditional monitoring methods are labour-intensive and lack real-time info, leading to ineffective interventions. To address this, an advanced plant health monitoring (APHM) system that uses remote sensing technology and machine learning-based classification to effectively identify plant diseases and pests in tomato plants is proposed. The proposed system allows unmanned aerial vehicles (UAV) to take detailed aerial images of tomato fields, which are processed and input into a convolutional neural network with a generating adversarial imitation learning (CNN-GAIL) model. The CNN-GAIL model efficiently differentiates between healthy plants, weak plants, and pest presence by capitalising on temporal relationships in image sequences. The APHM system's ability to quickly identify and fix problems, combined with remote sensing and machine learning in precision agriculture, could improve crop health, reduce yield losses, and promote long-term sustainability.

This paper considers a wireless downlink multiple-input multiple-output (MIMO) system, where communication between the base station and multiple users is facilitated by a reconfigurable intelligent surface (RIS) deployed on an unmanned aerial vehicle (UAV). Under the $\ell _{0}$-norm-based capacity-limited backhaul, the average weighted sum rate is maximized over the flying period by jointly designing the user scheduling, UAV's trajectory, active beamforming and passive beamforming. Given the highly coupled variables and non-convex combinatorial constraints, the underlying problem is decomposed into three subproblems. Relaxing the mixed-integer constraint as a weighted $\ell _{1}$-norm, the transmit precoding and user scheduling can be jointly obtained by the conic relaxation. The RIS phase shift with unit-modulus constraint is obtained via the alternating direction method of multipliers. The UAV's trajectory is optimized by the successive convex approximation approach. Numerical results demonstrate the effectiveness of the proposed algorithm in improving the average achievable rate within the constraints of a finite-capacity backhaul.

The progression of education reform causes the maximization of the English language’s importance in the learning process. The students require immediate English learning concerns to improve their understanding and learning in education. During the study, Machine Translation (MT) helps to simplify the learning process and helps to understand the huge volume of legal documents with minimum error in legal translations. The MT process utilizes Artificial Intelligence (AI) techniques while translating the text by incorporating the Google API translation techniques. However, the existing systems fail to correlate the user reading experience with system performance. The AI-MT systems are trained with the help of a set of words and text that are having difficulties while giving a large input volume. Therefore, the system faces computation complexity and decreases translation accuracy. The research issue is overcome by incorporating edge computing solutions with user data. The user inputs are investigated using the skip-gram model that generates the input feature vector. Then Beetle Antennae Search with Attention Long Short-Term Memory (BAS-ALSTM) neural model is applied to develop the ranking value for text. The generated word rank helps identify the edge server, and the respective translation process is initiated to maximize the translation matching degree. This process is utilized in the English learning process to increase language understanding ability and reduce learning difficulties. The effective utilization of the neural model understands every text, and translation is performed with the user query. The created system efficiency is evaluated using the experimental results and discussions.

Tool condition monitoring is essential to maintain the final product quality and machining efficiency of the manufacturing process. However, traditional physics-based and data-driven approaches have limitations either on prediction efficiency or performance generalization, due to the nature of the respective approaches. To address these issues, in this article, a physics-informed deep learning approach is developed, which integrates the tool wear mechanism into the data-driven model. First, some representative physical information is selected for the task learning. Then, four practical physics-informed methods are proposed to integrate various physical information into the data-driven models. Based on these physical constraints, a physics-informed deep learning model is specially designed for tool wear monitoring. Compared with previous studies, more diverse physical information can be effectively utilized to guide the hypothesis space, thereby improving the generality of the model. The effectiveness and feasibility of this model under various working conditions are verified in high-speed milling experiments. The results show that the wear prediction of the proposed approach is more accurate and consistent under unknown machining conditions.

Objective: To investigate dynamic iris changes in patients with primary angle-closure disease (PACD) with long axial length (AL) compared to those with short and medium AL. Methods: This observational cross-sectional study enrolled participants aged 35 years or older from the Handan Eye Study follow-up examination who were diagnosed with PACD and underwent Visante anterior segment optical coherence tomography (ASOCT) imaging under light and dark conditions. The right eye of each participant was included in the analysis. AL was categorized as short (<22.0 mm), medium (≥22.0 to ≤23.5 mm), or long (>23.5 mm). Anterior segment parameters, including iris dynamic changes, were compared among the three groups with different ALs. Results: Data from 448 patients with PACD were analyzed. We found that 10.9% of included eyes had a long AL with a flatter cornea; larger central anterior chamber depth, angle opening distance, anterior chamber width, anterior chamber area, and volume; and smaller lens thickness and lens vault (LV) (P< 0.05) than those with short AL. No significant difference existed between the three groups in iris thickness, iris cross-sectional area (IA), iris curvature, or pupil diameter (PD) change between light and dark (P> 0.05). The significant associated factors for IA changes were area recess area (ARA) in the dark, LV in the dark, and PD change from light to dark (P< 0.05). Conclusions: Dynamic and static iris parameters were consistent across patients with PACD with short, medium, or long AL and may contribute to the pathogenesis of angle closure in atypical PACD.

Trabecular bone undergoes changes in its morphology when subjected to external loading. This paper analyses changes in morphological parameters of individual trabeculae (trabecular thickness and bone volume fraction) in response to loadings at different directions: 0°, 20°, 45°, 70° and 90°. It was found that increase in the load incline caused increase in trabecular thickness and bone volume fraction. In addition, the equilibrium-state trabeculae obtained from adaptation to load at 90° (shear loading) had the highest axial and lateral stiffnesses, as compared to trabecular geometries obtained from other loading scenarios.

•River DOC concentration is higher in the north than in the south of China.•Climatic and anthropogenic factors synergetically impact river DOM dynamics.•Human activities have more pronounced impacts on riverine DOM than climate factors.•Keeping river systems healthy will benefit achieving sustainable development.

A 423.5-450 MHz low-power OOK/FSK/QPSK wireless transmitter is proposed with multi-phase injection locking and frequency multiplication techniques. A low-frequency phase-rotation-based frequency synthesizer is employed to provide multichannel support and FSK modulation. The far-out phase noise caused by the quantization noise of the $\Delta \Sigma $ modulator is filtered through a proposed N-path filter and two-stage injection-locked ring oscillators (ILROs). Phase modulation is achieved by changing the offset frequency between the oscillator free-running frequency and the injected frequency to control the steady-state phase offset of the injection-locked oscillator. Finally, a current mode class-D edge-combining power amplifier with high energy efficiency is used to achieve 5X frequency multiplication to generate the required carrier frequency. A prototype transmitter is fabricated in TSMC 90 nm CMOS process. It is shown that the device delivers an output power of −7 dBm with a power consumption of $870 \mu \text{W}$ under a 0.75 V supply voltage and supports data rates of 40/5/80 Mbps under OOK/BFSK/QPSK modulation, respectively, achieving the energy efficiency of 11 pJ/bit and 23% global efficiency.

Antibiotics are anthropogenic contaminants with a global presence and of deep concern in aquatic environments, while less is known about the occurrence and risks of their transformation products (TPs). Herein, we developed a comprehensive suspect and nontarget screening workflow based on high-resolution mass spectrometry to identify unknown antibiotic TPs in wastewater treatment plant effluents. We identified 211 compounds (35 parent antibiotics and 176 TPs) at confidence levels of ≥3 and 107 TPs originated from macrolides. TPs were quantified by 17 TPs standards and semiquantified by the predicted response factors and accounted for 55.6–95.1% (76.7% on average) of the total concentrations of parents and TPs. 22.2%, 63.1%, and 18.8% of the identified TPs were estimated to be more persistent, mobile, and toxic than their parent antibiotics, respectively. Further ecological risk assessment based on concentrations and toxicity to aquatic organisms revealed that the cumulative risks of TPs were generally higher than those of parents. Despite the newly formed N-oxide TPs, the tertiary treatment process (mainly ozonation) could decrease the averaged 20.3% of concentrations and 36.2% of the risks of antibiotic-related compounds. This study highlights the necessity to include antibiotic TPs in environmental scrutiny and risk assessment of antibiotics in different aquatic environments.

For the practical applications in solar cells and photodetectors, semiconductor colloidal nanocrystals (NCs) are assembled into a high-concentration film with carrier transport characteristics, the full understanding and effective control of which are critical for the achievement of high light-to-electricity conversion efficiencies. Here we have applied transient absorption microscopy to the solid film of giant CdSe/CdS NCs and discovered that at high pump fluences the carrier transport could reach a long distance of ∼2 μm within ∼30 ps after laser pulse excitation. This intriguing behavior is attributed to the metal–insulator transition and the associated bandlike transport, which are promoted by the enhanced electronic coupling among neighboring NCs with extended wave functions overlap of the excited-state charge carriers. Besides providing fundamental transport information in the regime of high laser pump fluences, the above findings shed light on the rational design of high-power light detecting schemes based on colloidal NCs.

Spectral search algorithms are widely used in radiation near-field (RNF) source locations because of their high estimation accuracy, but they have disadvantages such as large computation and long running time. To solve this problem, the Fresnel model is mainly adopted in the existing works of literature to realize the source location estimation in the algorithm of spectral search. Although the operation time is effectively reduced, there is a mismatch between the Fresnel model and the exact model, resulting in the existing method being not suitable for the source location under the exact model. In order to shorten the running time of the RNF source spectrum search algorithm under the exact model, an improved gray wolf optimal (IGWO) algorithm based on the exact model is proposed to estimate the RNF source position, which guarantees the high precision and shorten the operation time. The algorithm uses the noise subspace fitting method as the cost function and the power function as the convergence factor, which shortens the time required for source location and improves the estimation accuracy of the algorithm. The simulation results show that the IGWO algorithm takes less time and has better estimation accuracy than the existing source location algorithms.