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The adverse effects of metal(loid) accumulation on ecological health and human health have attracted widespread attention from the society, and the reduction of species diversity in the whole Yalong River basin due to dam construction. In this study, we investigated the distribution characteristics of metal(loid)s in the whole Yalong River basin from the point of view of metal(loid)s, identified the pollution sources, assessed the risk level, and further evaluated the risk to human health. A total of 9 heavy metals (Cr, Mn, Fe, Ni, Ag, Cd, Pb, Cu and Zn) and 1 metalloid (As) were investigated and surface water samples were collected from 17 sites along the Yalong River, which were categorized into industrial and agricultural production zones (YL1–5), alpine canyon zones (YL6–12), and alpine meadow zones (YL13–17) based on altitude and habitat. The results indicated that the density of metal(loid) in the alpine meadow area was considerably highest compared to other areas. The Zn content ranged from 64.42 to 84.55 μg/L, the Mn concentration ranged from 7.47 to 20.03 μg/L, and the Pb value ranged from 0.63 to 3.44 μg/L. In other places, the concentrations for Zn ranged from 17.88 to 37.78 μg/L, for Mn they ranged from 2.84 to 12.86 μg/L, and for Pb they ranged from 0.16 to 0.50 μg/L. The main sources of the metal(loid)s were determined by correlation analysis (CA) and principal component analysis (PCA) to be from natural factors such as crustal movement, atmospheric deposition, and water-rock washout. The water quality of Yalong River was evaluated by HEI, NI and WQI indices, and the results showed that the overall water quality of Yalong River was "excellent", in which the water quality of the alpine meadow area was better than that of the industrial and agricultural production area and the alpine canyon area. HQdermal, HQingestionand HI values are calculated to expose the harm caused by metal(loid) elements. The results indicate that all of the aforementioned values are below 1, suggesting that the potential harm caused by these elements is minimal. The value of HQdermaltends to be higher than HQingestion. Consequently, skin contact is likely the primary mechanism of exposure. This study provides a reference basis for metal(loid) detection and ecological changes caused by the dam construction process, and provides an important basis for the use and protection of surface water in the Yalong River.

Streaming triangle counting is a critical issue in graph stream mining, with applications in dense subgraph discovery, web mining, anomaly detection, and more. Recent efforts have focused on estimating triangle counts in graph streams, primarily through sampling methods. However, because of limited memory resources for handling high speed streams, traditional sampling methods suffer from reduced sampling rate and thereby performance loss. In this paper, we propose a new compact data structure called uHLL to process edge streams by considering the tradeoff between estimation accuracy and memory efficiency. Furthermore, different from conventional triangle counting algorithms, we solve the estimation of union set cardinality for edge-local triangle count under both centralized and distributed framework, so as to efficiently estimate the global triangle count by a one-pass streaming algorithm. To the best of our knowledge, this is the first implementation of a distributed framework using a compact data structure for streaming triangle counting. We provide theoretical proof of unbiasedness and derive the variance of the union set and global triangle count. We compare our scheme with 11 algorithms, showing that under the same experimental setting, uHLL and distributed uHLL are at least $ 2.3$2.3 and $ 1.7$1.7 times more accurate than the state-of-the-art, respectively.

Multi-motor synchronous drive system is increasingly widely used in industry and manufacturing, where its control structure and control strategy affect the quality and efficiency of production. In order to solve the contradiction between fastness and overshoot, and the difficulty in determining the compensation law in the conventional PID, cross-coupling control, and master-slave control strategies used in multi-motor control, this paper proposes a self-coupling PID control strategy based on ring adjacent compensation to reduce the complexity of the control structure. Furthermore, this paper analyzes the self-coupling PID parameter tuning rules and establishes the control structure of the ring coupling strategy, and proves its validity mathematically. The simulation results verify that the proposed strategy provides a fast response speed, high control precision, good disturbance rejection, and synchronization performance.

Precipitation hardening serves as a viable mechanism to enhance the strength of high/medium entropy alloys (H/MEAs). The nature of precipitates in HEAs varies based on the composition of multiple principal elements. In order to further enhance the performance of HEAs across different applications, it becomes imperative to exercise control over phase synthesis and suppress the precipitation of detrimental phases. In this investigation, we propose a strategy for regulating the precipitation of the second phase in (Co, Cr)58Ni30Mo6Al6(Co/Cr ratio = 1, 1.5, 2, 3) HEAs. Through precise adjustment of atomic ratios, the detrimental σ and μ phases are effectively restrained, while coherent nanoprecipitates are achieved through subsequent aging treatment. These fine nanoscale coherent phases alter the mode of dislocation slip, transforming planar slip into cross-slip, thereby providing a novel mechanism for the strengthening of the alloy. This controlled elimination and precipitation of phase contributes to a hierarchical improvement in the mechanical tensile properties. The aged HEA exhibits exceptional characteristics, including a strength of 1.3 GPa and 31% plasticity at 298 K (1.67 GPa and 40% at 77 K).

RFID (Radio Frequency Identification) computing devices in practical applications often suffer from their poor computing performance in terms of low task throughput (also known as task rate) due to scarce harvested power. For optimizing the task throughput, the basic idea is to choose an optimal processor clock frequency when executing a specific task ($e.g.$e.g., operate sensor) in order to maximize task execution rate. Existing methods are based on the common sense where the frequency and task throughput are directly proportional to each other, meaning that a higher frequency causes a higher task rate. In RFID-scale devices, however, we observe that the relationship between the frequency and task throughput overturns the common sense, in which if the device rises the frequency, the task throughput will increase first and then decrease due to intermittent task execution pattern on such devices. In this article, we present a systematic task throughput model to explain and formulate the non-monotonic relationship between the frequency and task throughput. Based on the throughput model, we further introduce dynamic optimal frequency scaling (DOFS) to calculate the optimal frequency for task execution and thus optimize the task throughput in the RFID-scale devices. The experimental results show that the task throughput can be improved by 45.8% on average compared to the existing best effort.

Seismic modeling is crucial for tackling waveform-based inverse problems in geophysics. Physics-informed neural networks (PINNs) have become a popular tool for simulating seismic waves. Their ability to incorporate partial differential equations (PDEs), initial conditions (ICs), and boundary conditions directly into the loss function allows for physically accurate modeling. The prevalent approach in the current literature treats the wave equation as a parametric PDE. However, the majority of the existing studies simulate wavefields for a specific velocity model, necessitating network retraining for different models, thereby diminishing modeling efficiency. In response, we present a velocity-encoded (VE) PINN (VE-PINN) that introduces feature parameters to represent various layered velocity models, integrating them into the network. Drawing inspiration from supervised learning, our approach employs a VE method to compute initial wavefields for variable layered models. Remarkably, our proposed VE-PINN demonstrates the ability to generalize across different ICs within the dataset. This eliminates the need to retrain the network for each new solution, offering significant efficiency gains. Numerical results show that the VE-PINN significantly enhances efficiency in solving the acoustic wave equation for various layered velocity models compared with finite-difference methods (FDMs). Subsequently, we extend the application of our method to time-domain simulation for variable source locations, demonstrating that the VE-PINN yields the results that are consistent with numerical wavefields.

Intelligence quotient (IQ) and adaptive behavior are the influencing factors of autism spectrum disorder (ASD) in children entering mainstream schools. This study explored the association between parental education level, IQ, and adaptive behavior in ASD groups. A total of 257 school-age ASD children were enrolled in our study from January 2017 to June 2021. Their parents completed a standard demographic form, including age at autism diagnosis, gender, school placement, and parents’ educational background. The Chinese version of the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV) was completed by a certified assessor for each enrolled child. Parents were interviewed on adaptive behavior using the Chinese version of the Adaptive Behavior Assessment System, Second Edition (ABAS-II). The average IQ of school-age ASD children was 76.88 (standard deviation (SD)=22.62) and boys had higher IQ levels than girls. The IQ was positively correlated with age. The General Adaptive Composite (GAC) score was 82.47 (SD=15.86) and adaptive behavior did not increase with age. ASD children who attended mainstream schools had better adaptive behavior profiles than other children. The mother’s education level showed a significant correlation with the IQ and adaptive behavior of autistic children, while the father’s education level did not. Consequently, better training and support for parents may help autistic children enter mainstream schools, with adaptive training being the most urgently required skill for parents.

Elastic full-waveform inversion (EFWI) can retrieve multiple subsurface elastic parameters beyond the capabilities of the simple acoustic assumption. Compared to acoustic FWI (AFWI), EFWI faces complexities in dealing with multiple parameters and high nonlinearity in elastic inversion. Wavefield reconstruction inversion (WRI) was proposed to mitigate the cycle skipping and improve the computational efficiency of AFWI. WRI uses the wave equation as a regularization term for the objective of data fitting. By controlling the weight factor for the regularization term, the accuracy of the wave equation is relaxed and the data fitting term is enhanced. Thus, the cycle-skipping issue is reduced. WRI requires wavefield reconstruction in the calculation of the model gradient, which is the key step in WRI. The success of this wavefield reconstruction step highly depends on the accuracy of the source wavelet. In this paper, we propose an elastic WRI (EWRI) with source estimation (SE) method for multiple elastic parameters inversion. In the proposed method, we first reconstruct multicomponent wavefields (vertical and horizontal displacements) and estimate the source wavelet, simultaneously. Then, we formulate the elastic waveform inversion problem into a linear inversion system to mitigate the nonlinearity in EFWI. Applications on synthetic data generated from a modified Overthrust model and a Section of the Sigsbee2A model show the effectiveness of the proposed method in inverting P- and S-wave velocity models with an unknown source wavelet.

Quantum many-body systems with a non-Abelian topological order can host anyonic quasiparticles. It has been proposed that anyons could be used to encode and manipulate information in a topologically protected manner that is immune to local noise, with quantum gates performed by braiding and fusing anyons. Unfortunately, realizing non-Abelian topologically ordered states is challenging, and it was not until recently that the signatures of non-Abelian statistics were observed through digital quantum simulation approaches. However, not all forms of topological order can be used to realize universal quantum computation. Here we use a superconducting quantum processor to simulate non-Abelian topologically ordered states of the Fibonacci string-net model and demonstrate braidings of Fibonacci anyons featuring universal computational power. We demonstrate the non-trivial topological nature of the quantum states by measuring the topological entanglement entropy. In addition, we create two pairs of Fibonacci anyons and demonstrate their fusion rule and non-Abelian braiding statistics by applying unitary gates on the underlying physical qubits. Our results establish a digital approach to explore non-Abelian topological states and their associated braiding statistics with current noisy intermediate-scale quantum processors.

Solar-driven CO2-to-fuel conversion assisted by another major greenhouse gas CH4is promising to concurrently tackle energy shortage and global warming problems. However, current techniques still suffer from drawbacks of low efficiency, poor stability, and low selectivity. Here, a novel nanocomposite composed of interconnected Ni/MgAlOxnanoflakes grown on SiO2particles with excellent spatial confinement of active sites is proposed for direct solar-driven CO2-to-fuel conversion. An ultrahigh light-to-fuel efficiency up to 35.7%, high production rates of H2(136.6 mmol min−1g−1) and CO (148.2 mmol min−1g−1), excellent selectivity (H2/CO ratio of 0.92), and good stability are reported simultaneously. These outstanding performances are attributed to strong metal-support interactions, improved CO2absorption and activation, and decreased apparent activation energy under direct light illumination. MgAlOx@SiO2support helps to lower the activation energy of CH* oxidation to CHO* and improve the dissociation of CH4to CH3* as confirmed by DFT calculations. Moreover, the lattice oxygen of MgAlOxparticipates in the reaction and contributes to the removal of carbon deposition. This work provides promising routes for the conversion of greenhouse gasses into industrially valuable syngas with high efficiency, high selectivity, and benign sustainability.

Alectinib is an ALK tyrosine kinase inhibitor, which is mainly used in patients with crizotinib-resistant nonsmall cell lung cancer. Alectinib has attracted much clinical attention for its longest progression-free survival time and the best therapeutic effect. The chemical adsorption of Au nanoclusters (AuNPs) with alectinib molecules is studied by density functional theory (DFT) and surface-enhanced Raman scattering spectroscopy (SERS) experiments. DFT/B3LYP-D3/6-311G** was used for optimization and vibration analysis of alectinib–Au6complexes, as well as molecular electrostatic potential, frontier molecular orbital, and electro-optic-based charge transfer descriptors. Comparing the results of the DFT theory and SERS experiment, alectinib and AuNPs can form Au–N6 bonds primarily through chemical adsorption of N6 atoms, and the experimental results showed that the enhancement factor (EFCHEM) could reach 4.27. The results provide a theoretical basis for exploring the mechanism of chemical enhancement between AuNPs and alectinib.

Plastic film mulch in agricultural production becomes essential to maintaining crop yields in arid and semiarid areas. However, the presence of residual film in farmland soil has also drawn much attention. In this study, three experiments were conducted. The first two experimental designs included 0, 450, 1 350, and 2 700 kg ha−1of residual film pieces of approximately 5 cm side length added to field soil (0–20 cm soil depth) for seven years and added to pots for four years. In the third experiment, 1 350 kg ha−1of the residual film with different side lengths (2–5, 5–10, 10–15, and 15–20 cm) was added to field soil for six years to explore the effect of residual film fragment size on soil nutrients, soil microorganisms, crop growth and yields. The residual film had little effect on the soil moisture at a field depth of 0–2 (or 0–1.8) m. There were no significant effects on organic carbon, total nitrogen, inorganic nitrogen, total phosphorus or available phosphorus in the 0–20 cm soil layer. The presence of residual film decreased the richness and diversity of the bacterial community of the surface soil of the residual film, but it had no significant effect on the microbial community of the non-surface soil. The emergence rates of wheat and lentils occasionally decreased significantly with different amounts of residue fragments added to the field. At 450–2 700 kg ha−1, the residual film reduced the plant height and stem diameter of maize and significantly reduced the shoot biomass of harvested maize by 11–19%. The average yields of maize and potato over the seven years decreased, but there were almost no significant statistical differences among the treatments. These results provide important data for a comprehensive scientific understanding of the effects of residual film on soil and crops in dryland farming systems.

Although microbial humus respiration plays a critical role in organic matter decomposition and biogeochemical cycling of elements in diverse anoxic environments, the role of methane-producing species (methanogens) is not well defined. Here we report that a major fraction of humus, humic acid reduction enhanced the growth of Methanosarcina acetivoransabove that attributed to methanogenesis when utilizing the energy sources methanol or acetate, results which showed both respiratory and fermentative modes of energy conservation. Growth characteristics with methanol were the same for an identically cultured mutant deleted for the gene encoding a multi-heme cytochrome c(MmcA), results indicating MmcA is not essential for respiratory electron transport to humic acid. Transcriptomic analyses revealed that growth with humic acid promoted the upregulation of genes annotated as cell surface pyrroloquinoline quinone (PQQ)-binding proteins. Furthermore, PQQ isolated from the membrane fraction was more abundant in humic acid-respiring cells, and the addition of PQQ improved efficiency of the extracellular electron transport. Given that the PQQ-binding proteins are widely distributed in methanogens, the findings extend current understanding of microbial humus respiration in the context of global methane dynamics.

Although microbial humus respiration plays a critical role in organic matter decomposition and biogeochemical cycling of elements in diverse anoxic environments, the role of methane-producing species (methanogens) is not well defined. Here we report that a major fraction of humus, humic acid reduction enhanced the growth of Methanosarcina acetivoransabove that attributed to methanogenesis when utilizing the energy sources methanol or acetate, results which showed both respiratory and fermentative modes of energy conservation. Growth characteristics with methanol were the same for an identically cultured mutant deleted for the gene encoding a multi-heme cytochrome c(MmcA), results indicating MmcA is not essential for respiratory electron transport to humic acid. Transcriptomic analyses revealed that growth with humic acid promoted the upregulation of genes annotated as cell surface pyrroloquinoline quinone (PQQ)-binding proteins. Furthermore, PQQ isolated from the membrane fraction was more abundant in humic acid-respiring cells, and the addition of PQQ improved efficiency of the extracellular electron transport. Given that the PQQ-binding proteins are widely distributed in methanogens, the findings extend current understanding of microbial humus respiration in the context of global methane dynamics.

ABSTRACTAnimal models are widely used in laboratory practicals in biomedical disciplines. However, experiments involving animal work are usually time-and-labor consuming. It is also costly to run an animal lab and the animal caring/holding request special training. Here we present a laboratory course design that provides students with wet lab experience in gene editing in mouse stem cell lines using CRSISPR/Cas9 and dry lab experience in generating transgenic mouse models using computer simulation. This workshop is for senior undergraduates as well as graduate students and is designed to help them understand the application of gene editing in biomedical research. The virtual simulation is incorporated as a computer-based alternative to using mice for embryo transplantation, breeding, and characterization. The integrated curriculum of wet and dry laboratories enabled us to complete this complicate multi-disciplinary experiment in one semester. In addition, the computer-based alternatives reduce the unnecessary animal tests down to zero and errors during practice are well-tolerated without raising any ethical issues. The survey and student’s feedback showed overall satisfaction in their learning experiences and learning benefits. The evaluation of the outcomes of learning objectives on the completion of this course revealed positive achievements in bioinformatics, molecular biology, and in general animal work

Quantum simulation provides platforms to study fundamental aspects of many-body physics in a controlled way, and to explore their implications for quantum technology. However, the dimension of the Hilbert space grows exponentially with the number of interacting particles, which means that investigations of strongly correlated problems such as quantum criticality and many-body localization in terms of few-body probes in real space are often insufficient. Here we demonstrate how to tackle such questions on a superconducting quantum processor from a Fock-space perspective. We map a many-body system onto an unconventional Anderson model on a network of many-body states, which allows the direct observation of wave-packet propagation in Fock space. We find a quantum-critical regime of anomalously enhanced wave-packet width and deduce a critical point from the maximum wave-packet fluctuations, which lends support to the two-dimensional many-body localization transition in finite-sized systems. Our results introduce an alternative picture for characterizing many-body dynamics and for exploring the controversial problems such as criticality and dimensionality. Moreover, the protocol is universal and scalable, and is therefore a promising strategy to solve a broader range of many-body problems on future quantum devices.

Cyromazine, when used as an insect growth regulator and low-toxicity insecticide, may degrade into melamine and pose a potential threat to the environment and soil health, which has thus attracted extensive research on eliminating such a harmful effect. In this paper, density functional theory (DFT)/LC-BLYP/6-311G(d,p) is used to optimize the geometric structure and analyze the vibration of cyromazine. The DFT/LC-BLYP/def2-SVP is used for the cyromazine–Au complex optimization and vibration analysis. The molecular electrostatic potential (MEP), frontier molecular orbitals (FMOs), vibration frequency, electrophilicity-based charge transfer (ECT) descriptor, binding energy (BE), polarizability, normal Raman spectroscopy (NRS), and surface-enhanced Raman spectroscopy (SERS) of cyromazine adsorbing on Au6and Au20are calculated. The study of the chemical enhancement mechanism of SERS of cyromazine at different adsorption sites of Au6or Au20confirms the existence of a charge transfer between cyclopromazine and Au6and Au20, which can adsorb and form stable cyromazine–Au complexes. The results show that N2, H13, and N4 are the adsorption sites of Au6and Au20. The Raman spectra of the cyromazine–Au complex can be selectively enhanced with a factor up to 9.07. Compared with those of cyromazine–Au6, the Raman spectra of cyromazine–Au20are enhanced more significantly.

Heavy metal distribution in mining areas has always been a hot research topic due to the special environment of these areas. This study aims to explore the impact of heavy metal pollution on soils and crops in the study area, ensure the safety of local crops and the health of local residents, and provide a basis for the subsequent environmental restoration and the prevention and control of environmental pollution. Based on the analysis of the heavy metal concentrations in local soils and crops, the study investigated the spatial distribution, pollution degrees, and potential ecological risks of heavy metals in the farmland of a mining area in the southeastern Nanyang Basin, Henan province, China explored the sources of heavy metals and assessed the health risks caused by crop intake. The results of this study are as follows. The root soils of crops in the study area suffered heavy metal pollution to varying degrees. The degree of heavy metal pollution in maize fields is higher than that in wheat fields, and both types of fields suffer the most severe Cd pollution. Moreover, the root soils of different crops suffer compound pollution. The root soils in the maize fields suffer severe compound pollution at some sampling positions, whose distribution is similar to that of the mining area. Cd poses the highest potential ecological risks among all heavy metals, and the study area mainly suffers low and moderate comprehensive potential ecological risks. The principal component analysis (PCA) shows that the distribution of Zn, Cd, Pb, and As in soils of the study area is mainly affected by anthropogenic factors such as local mining activities; the distribution of Cr and Ni is primarily controlled by the local geological background; the distribution of Hg is mainly affected by local vehicle exhaust emissions, and the distribution of Cu is influenced by both human activities and the geological background. Different cereal crops in the study area are polluted with heavy metals dominated by Cd and Ni to varying degrees, especially wheat. As indicated by the health risk assessment results, the intake of maize in the study area does not pose significant human health risks; however, Cu has high risks to human health, and the compound heavy metal pollution caused by the intake of wheat in the study area poses risks to the health of both adults and children. Overall, the soils and crops in the study area suffer a high degree of heavy metal pollution, for which mining activities may be the main reason.

Ultrasonic shot peening (USP) uses the high-frequency motion of the shots with a random distribution of direction and velocity driven through ultrasonic resonance, thus obtaining a deeper gradient structural layer and a larger residual compressive stress. The variation rule of different parameters in the USP process is studied in detail through finite element simulation. To be closer to the experiment, the motion pattern of the shot is observed and repeatedly verified with the help of high-speed camera. After establishing the finite element model (FEM) of the impact of the random distribution of the shots, the motion characteristics are assigned to the shot in the form of probability density function (PDF). The evolution of shot peening coverage is calculated from the experimental and simulation results, and the patterns of residual stress, roughness, and plastic strain during the USP simulation are calculated. The results show that with the increase of shot peening coverage, the maximum residual stress (RS) and plastic strain increase gradually, and the variation rules of each roughness parameter with the coverage range are different, but the values are all within 10 μm. The modeling method used in this paper provides a new idea for the fine modeling of USP.

Maize tassel detection is essential for future agronomic management in maize planting and breeding, with application in yield estimation, growth monitoring, intelligent picking, and disease detection. However, detecting maize tassels in the field poses prominent challenges as they are often obscured by widespread occlusions and differ in size and morphological color at different growth stages. This study proposes the SEYOLOX-tiny Model that more accurately and robustly detects maize tassels in the field. Firstly, the data acquisition method ensures the balance between the image quality and image acquisition efficiency and obtains maize tassel images from different periods to enrich the dataset by unmanned aerial vehicle (UAV). Moreover, the robust detection network extends YOLOX by embedding an attention mechanism to realize the extraction of critical features and suppressing the noise caused by adverse factors (e.g., occlusions and overlaps), which could be more suitable and robust for operation in complex natural environments. Experimental results verify the research hypothesis and show a mean average precision (mAP@0.5) of 95.0%. The mAP@0.5, mAP@0.5–0.95, mAP@0.5–0.95 (area=small), and mAP@0.5–0.95 (area=medium)average values increased by 1.5, 1.8, 5.3, and 1.7%, respectively, compared to the original model. The proposed method can effectively meet the precision and robustness requirements of the vision system in maize tassel detection.

Through inverted-design rather than modifying the generally-assumed S active sites in popular MoS2, we unlock the potential of Mo sites and successfully prepared novel MoS2@Ni3S2/NF core-shell nanospheres as a catalyst for the high-performance hydrogen evolution reaction (HER). The ΔGHat the Mo site is optimized via Ni3S2to achieve excellent HER activity. At low current densities, it has similar activity to the Pt/C. However, its performance is better than Pt/C at high density. Moreover, our catalyst shows a considerable stability at a variety of current densities for 50 h, promising to substitute noble metal catalysts in application of commercial alkaline electrocatalysts.

In view of the system uncertainty factors and bilateral drive synchronization problems of XY platform driven by stepping motor, on the basis of the mathematical model of single axis servo system, in order to improve traceability and disturbance rejection in X,Y direction, a double loop structure with PID controller as speed loop and active disturbance rejection as position loop is proposed to solve the contradiction between rapidity and overshoot, moreover, an ESO performance analysis method is proposed, and the parameter tuning rules of ADRC is summarized. On this basis, a deviation self-coupling compensation control strategy is proposed to solve the problem of bilateral drive synchronization in axis X direction, and the mechanism and parameter adjusting method of the compensator are studied. The comparative experiment between ADRC+PID+deviation self-coupling compensation and PID+PID+deviation self-coupling compensation, the comparative experiment between ADRC+PID+deviation self-coupling compensation and ADRC+PID are conducted, respectively. Finally, the experiment of synthetic trajectory of X and Y is carried out, the simulation and experimental results demonstrate that the response time of the control strategy proposed in this paper is 0.5 s, which is 0.2 s faster than PID’s, and the disturbance recovery time is 50% of PID control. Therefore, the proposed ADRC+PID+deviation self-coupling compensation control strategy can not only effectively improve the tracking performance of the XY platform system, but also enhance the robustness of the system.

Convolutional neural networks (CNNs) have been demonstrated to be powerful tools to automatically learn effective features from large datasets. Though features learned in CNNs are approximately scale-, translation-, and position-invariant, and their capacity in dealing with image rotations remains limited. In this article, a novel cyclic polar coordinate convolutional layer (CPCCL) is proposed for CNNs to handle the problem of rotation invariance for feature learning. First, the proposed CPCCL converts rotation variation into translation variation using polar coordinates transformation, which can easily be handled by CNNs. Moreover, cyclic convolution is designed to completely handle the translation variation converted from rotation variation by conducting convolution in a cyclic shift mode. Note that the proposed CPCCL is capable of generalization and can be used as a preprocessing layer for classification CNNs to learn the rotation-invariant feature. Extensive experiments over three benchmark datasets demonstrate that the proposed CPCCL can clearly handle the rotation-sensitive problem in traditional CNNs and outperforms several state-of-the-art rotation-invariant feature learning algorithms.

Photolysis is considered to be an important elimination process for antibiotics in nature. Humic acids (HA) are ubiquitous in aquatic environments and inevitably encounter antibiotics, which may interfere with their photolysis and thus alter their environmental fate. In this study, we chose sulfamethoxazole (SMX) as a typical antibiotic to assess the effect of HA on photolysis. HA at 1 mg L−1greatly promoted the photolysis of SMX by 98.8%. However, high-concentration HA inhibited the photolysis, and the photolysis rate decreased by 92.9% in the presence of 100 mg L−1HA. Furthermore, HA exhibited different effects on direct and indirect photolysis. Low-concentration HA induced the production of OH to promote indirect photolysis, whereas high-concentration HA could compete with SMX for photons and decrease light penetration vialight shielding to inhibit direct photolysis greatly. Based on density functional theory (DFT) calculation, the Na site in HA could bind with the N–O and –SO2–NH– on the isoxazole ring of SMX to form complexes. The active sites of SMX and SMX–HA complexes were also identified, and the active sites increased after complexation. Moreover, low-concentration HA exhibited little influence on the degradation pathway of SMX due to little complexation degree while SMX was degraded viadifferent paths in the presence of high-concentration HA, which was mainly attributed to the complexation of SMX and HA. Besides, the toxicity of most intermediates were lower than that of SMX, but some intermediates in new degradation pathways tended to accumulate during photolysis, resulting in higher potential environmental risk.

Transformer-based networks, which can well model the global characteristics of inputted data using the attention mechanism, have been widely applied to hyperspectral image (HSI) classification and achieved promising results. However, the existing networks fail to explore complex local land cover structures in different scales of shapes in hyperspectral remote sensing images. Therefore, a novel network named multiscale and cross-level attention learning (MCAL) network is proposed to fully explore both the global and local multiscale features of pixels for classification. To encounter local spatial context of pixels in the transformer, a multiscale feature extraction (MSFE) module is constructed and implemented into the transformer-based networks. Moreover, a cross-level feature fusion (CLFF) module is proposed to adaptively fuse features from the hierarchical structure of MSFEs using the attention mechanism. Finally, the spectral attention module (SAM) is implemented prior to the hierarchical structure of MSFEs, by which both the spatial context and spectral information are jointly emphasized for hyperspectral classification. Experiments over several benchmark datasets demonstrate that the proposed MCAL obviously outperforms both the convolutional neural network (CNN)-based and transformer-based state-of-the-art networks for hyperspectral classification.

Quantum many-body scarring (QMBS) is a recently discovered form of weak ergodicity breaking in strongly interacting quantum systems, which presents opportunities for mitigating thermalization-induced decoherence in quantum information processing applications. However, the existing experimental realizations of QMBS are based on systems with specific kinetic constrains. Here we experimentally realize a distinct kind of QMBS by approximately decoupling a part of the many-body Hilbert space in the computational basis. Utilizing a programmable superconducting processor with 30 qubits and tunable couplings, we realize Hilbert space scarring in a non-constrained model in different geometries, including a linear chain and quasi-one-dimensional comb geometry. By reconstructing the full quantum state through quantum state tomography on four-qubit subsystems, we provide strong evidence for QMBS states by measuring qubit population dynamics, quantum fidelity and entanglement entropy after a quench from initial unentangled states. Our experimental findings broaden the realm of scarring mechanisms and identify correlations in QMBS states for quantum technology applications.

Full-waveform inversion (FWI) is a popularly used high-resolution seismic inversion method. It relies on the measure of the misfit between observed data and predicted data. Due to the sinusoidal nature of seismic waves, a direct comparison of observed data and predicted data using the $l_{2}$ norm may cause cycle skipping. A variety of objective functions for FWI have been proposed to resolve this issue over the years. Based on the gradient optimization method, an explicit expression of the model gradient of the defined objective function is needed to be derived and calculated. This complicated step can be circumvented by using an automatic gradient calculation technique, called automatic differentiation (AD). AD allows calculation of the gradients of the model parameters, as well as those of the inputs using the chain rule. Taking advantage of the deep-learning framework, FWI with different objective functions can be automatically optimized using AD. To improve the accuracy and applicability of FWI on real data, we propose a new objective function that we refer to as the weighted envelope-correlation inversion (WECI), which combines two correlation-based waveform inversions. The weights imposed on these two terms in this new objective function can be dynamically adjusted by the sigmoid function during the optimization process. We show the versatility and effectiveness of AD-based waveform inversions using different objective functions through numerical tests. We also demonstrate the superiority of the proposed WECI method on synthetic data and real data.

Synthetic ester and blended insulating oils have attracted widespread attention owing to their great potential on performance enhancement because of the high capacity with tailorable molecular structure and mixable components. In this work, natural ester insulating oil (FR3) is blended into the synthetic tetra-ester insulating oil (KA) via mixing and oscillating. The physicochemical and electrical property measurements of the ester blends indicate that the flash point, fire point, and ac breakdown voltage of KA are capable to be increased, and the dissipation factor of KA is reduceable by the addition of FR3. As expected, a series of physicochemical properties of the ester blends follow a quasi-linear relationship with the variation (in vol%) of FR3. However, the flash point (closed cup) is verified with high nonlinearity according to the results of thermogravimetric-derivative and thermogravimetric-differential scanning calorimetry (TG-DSC) tests in nitrogen. The optimized ester blend [85 vol% (KA):15 vol% (FR3)] is typically manifested by high flash point (260 °C), low pour point (−51 °C), low dissipation factor (1.44%), high ac breakdown voltage (76.5 kV), and high onset temperature of DSC either in nitrogen (229.99 °C) or in the air (272.05 °C). The activation energy is analyzed based on the Arrhenius equation, dissipation factor, and resistivity values to recognize the optimum ratio of the ester blends.

Soft and intelligent bioelectronics have achieved unprecedented development in both academics and industries over the last few decades, especially as ideal body-worn detectors for continuous human health status monitoring. However, the longstanding functional stability of bioelectronics in multiple environmental conditions of variant temperatures, humidities, and mechanical stimuli or even in some extremes, such as ultraviolet radiation and X-ray radiation, has confined the application of these electronics. Herein, a self-sustainable, multifunctional, robust sensor for biomechanical monitoring is prepared by hybridizing a parallel-tunnel fluorinated poly(ethylene propylene) (FEP) ferroelectret film (sensing layer) and poly(dimethylsiloxane) (PDMS, protection layer). A fast response (80 ms) and a low pressure detection limit (10 Pa) were achieved. Notably, the self-powered sensor can not only sensitively detect the loading of solid objects but also percept liquid water droplets and airflow, which satisfies the diverse needs of wearable devices. Meanwhile, the capability of stable and repeatable operation under a wide temperature range (−26–70 °C), extreme moisture, continuous mechanical stimulus (∼1.08 million cycles), and long-time ultraviolet radiation enabled the extensive and long-term application of such sensors in multiple scenarios. Moreover, the reproducibility of sensing performance after X-ray radiation can be realized through second contact polarization even after encapsulation. Due to the inherent mechanical flexibility, the fabricated sensor was conformally attached to rough and deformed skin and verified the feasibility of wearable biomechanical sensing with high sensitivity from facial smiling to plantar movement. This work provides an efficient strategy for multifunctional sensing, holding great promise for advanced soft bioelectronics in the next generation of wearable intelligent electronic systems.

Growing concerns regarding the presence of pharmaceuticals in wastewater necessitate their removal. Membrane filtration offers a promising approach. This study explores the development of biochar incorporated mixed matrix membranes (MMMs) for ciprofloxacin removal from water. Biochar, derived from the pyrolysis of agricultural waste, was blended with polyether sulfone (PES) and polyvinylpyrrolidone in varying ratios. The resulting MMMs exhibited progressively improved properties with increasing biochar content. Notably, membrane M11, comprising equal parts PES and biochar, displayed the highest porosity, lowest surface roughness (12.0), and lowest contact angle (31.05°), indicating enhanced hydrophilicity (increased by 58.19% compared to the biochar-free membrane). M11effectively removed ciprofloxacin along with three additional antibiotics from different classes. Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy analyses corroborated the removal of ciprofloxacin. Furthermore, M11demonstrated excellent regenerability, retaining over 57% removal efficiency after four cycles. These findings highlight the potential of M11as a sustainable and cost-effective membrane for pharmaceutical removal from wastewater.