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The pre-pressure device of the ultrasonic motor plays a vital role in the design of the entire motor structure, the contact state of the stator and rotor of the motor, dynamic properties of the stator, friction and wear characteristics of the rotor; even the mechanical behaviors of the entire electric machinery have a profound impact. Appropriate pre-pressure is conducive to the smooth operation of the ultrasonic motor, so that the output performance remains excellent, reducing wear and effectively extend the service life of the motor. Therefore, the research on pre-stress is of great significance, as it can better optimize the structure of the three-stator ultrasonic motor and lay the foundation for the stable operation of the motor. First, this paper introduces the construction of the motor as a whole and the pre-pressure device briefly described the working mechanism of the motor, and then introduces the influence of the pre-pressure on the stator and rotor contact models, the position of the constant velocity point, and the modal frequency. Finally, the motor output under different pre-pressures is discussed. The performance experiment has determined the optimal pre-pressure interval, which provides help for its subsequent optimization.

Adolescents are especially vulnerable to experiencing depression and anxiety. This longitudinal study, from within- and between-person perspectives, explores how self-esteem relates to depressive and anxiety symptoms in Chinese adolescents and identifies the mediating factors impacting this relationship. Data were collected from 1025 junior and high school students in Southwestern China at three points over an 18-month period. This study utilized both traditional and random-intercept cross-lagged panel models to understand the dynamic developmental relationships. The general occurrence of depressive and anxiety symptoms increased longitudinally, with a more pronounced upward trend among female students. Between-person level analyses indicated bidirectional associations among self-esteem, life satisfaction, and depressive and anxiety symptoms. Moreover, life satisfaction emerged as a significant mediator. At the within-person level, self-esteem uniquely predicted both life satisfaction and subsequent depressive and anxiety symptoms. This study clarifies the longitudinal interplay between these constructs. Self-esteem, which denotes internal self-assessments, and life satisfaction, which denotes external evaluations of life, both significantly buffer the emergence of depressive and anxiety symptoms.

Hydrogen peroxide (H2O2), a common oxidant present in the environment, food, and biological systems, has wide-ranging applications. While H2O2 is generally considered non-toxic, prolonged or repeated exposure to high concentrations can be harmful, making its accurate detection crucial in environmental monitoring, food safety, healthcare, and other fields. This review delves into the recent advancements in H2O2 detection methods, with a particular focus on chemosensors. We comprehensively summarize the fundamental principles of various chemosensor principles (e.g., colorimetric, fluorescence, chemiluminescence, electrochemical, and chemiresistive approaches), active materials, and diverse applications. Additionally, we discuss the current challenges and future prospects in this field, emphasizing the need for innovative materials and advanced sensing technologies to meet the growing demand for highly sensitive, accurate, reliable, real-time, and cost-effective H2O2 detection solutions.

With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation.

Leopard coral grouper (Plectropomus leopardus), possessing a distinct red body color, is an important species in commercial markets; however, the high ratio of black individuals under intensive cultivation has limited the commercial value of the species. To dissect the regulatory mechanisms underlying the red skin trait in P. leopardus, gene expression and DNA methylation modifications were compared between red and black skin tissues after astaxanthin treatment. Astaxanthin effectively increased the redness value a* and body weight. Multi-omics analyses revealed the crucial roles of pathways related to antioxidants and lipid metabolism, particularly “Tyrosine metabolism”, “Melanogenesis”, “Fatty acid metabolism”, “Fatty acid elongation”, and “Biosynthesis of unsaturated acids”, in red skin coloration. A molecular network for the regulation of red skin coloration in P. leopardus was constructed, and pmel, tyr, tyrp1a, tyrp1b, dct, slc24a5, wnt1, acsl4, elovl1, elovl6l.1, elovl6l.2, and elovl7 were identified as key genes. Notably, pmel, acsl4, and elovl7 were negatively regulated by differential DNA methylation. Our results provide new insight into the molecular and epigenetic mechanisms of body color variation, representing a significant step towards breeding for the red skin trait in P. leopardus.

Conducting polymers represent a crucial class of functional materials with widespread applications in diverse fields. Among these, poly(3,4-ethylenedioxythiophene) (PEDOT) and its derivatives have garnered significant attention due to their distinctive optical, electronic, and magnetic properties, as well as their exceptional tunability. These properties often exhibit intricate interdependencies, manifesting as synergistic, concomitant, or antagonistic relationships. In optics, PEDOTs are renowned for their high transparency and unique photoelectric responses. From an electrical perspective, they display exceptional conductivity, thermoelectric, and piezoelectric performance, along with notable electrochemical activity and stability, enabling a wide array of electronic applications. In terms of magnetic properties, PEDOTs demonstrate outstanding electromagnetic shielding efficiency and microwave absorption capabilities. Moreover, these properties can be precisely tailored through molecular structure modifications, chemical doping, and composite formation to suit various application requirements. This review systematically examines the mechanisms underlying the optoelectromagnetic properties of PEDOTs, highlights their tunability, and outlines prospective research directions. By providing critical theoretical insights and technical references, this review aims to advance the application landscape of PEDOTs.

Suicidal ideation and behavior are critical psychological crises among children and adolescents, posing significant concerns for their mental health and safety. This cross-sectional study investigated the factors and underlying psychological mechanisms of suicidal ideation in adolescents. A total of 6474 middle school students from Sichuan and Hebei provinces, China, participated in the study. Data were collected using the Self-Assessment of Suicidal Ideation Scale, the Parental Psychological Control Scale, the Meaning in Life Scale, and the Basic Psychological Needs Scale. Statistical analyses, including mediation and chain mediation analyses, were conducted to examine the relationships between parental psychological control, basic psychological needs, meaning in life, and suicidal ideation. The results revealed the following: (1) Parental psychological control positively predicted suicidal ideation in middle school students; (2) basic psychological needs mediated the relationship between parental psychological control and suicidal ideation; (3) meaning in life mediated the relationship between parental psychological control and suicidal ideation; (4) basic psychological needs and meaning in life together played a chain-mediating role in this relationship. These findings highlight the importance of addressing parental psychological control and fostering a supportive family environment to meet adolescents’ psychological needs, enhance their sense of life meaning, and reduce suicidal ideation. Insights from this study provide valuable guidance for prevention and intervention strategies aimed at safeguarding adolescents’ mental health and well-being.

Pandan, a tropical crop, is rich in squalene (SQ), known for its antioxidant and hypoglycemic properties, and 2-acetyl-1-pyrroline (2-AP), which imparts a characteristic aroma. This study focuses on the extraction of the two bioactive compounds from Pandan leaves and investigates the effects of drying methods, extraction solvents, and conditions on the yield of SQ and 2-AP. Results show that hot air-dried Pandan leaves when extracted using the binary solvent system of ethanol and n-hexane (EH), yield higher SQ content while maintaining an adequate content of 2-AP. To further optimize the extraction process, a single-factor experiment was followed by optimization using Box–Behnken design (BBD) and response surface methodology (RSM). The optimal extraction conditions were determined as follows: ultrasound time of 60 min, a temperature of 50 °C, power of 300 W, and a solid-to-liquid ratio of 1:5 g/mL. Under these conditions, an SQ yield of 1229.98 ± 13.09 μg/DW 1 g Pandan leaves and a 2-AP yield of 80.72 ± 0.88 μg/DW 1 g Pandan leaves were achieved, representing increases of 3.30% and 9.82% compared to pre-optimization values. Additionally, the antioxidant activities of EH extracts were evaluated through various in vitro assays. The extracts demonstrated significant DPPH and ABTS free radical scavenging activity (12.46 μmol TE/g DW and 22.14 μmol TE/g DW, respectively), along with ferric and cupric ion reducing power (10.629 μmol TE/g DW and 14.275 μmol TE/g DW, respectively). The extracts also exhibited notable inhibitory effects on α-amylase and α-glucosidase. The findings suggest that these extracts are a promising natural source of antioxidants with potential applications in health and nutrition.

Specific emitter identification (SEI) is a promising physical-layer authentication technique that serves as a crucial complement to upper-layer authentication mechanisms. SEI capitalizes on the inherent radio frequency fingerprints stemming from circuit discrepancies, which are intrinsic hardware properties and challenging to counterfeit. Recently, various deep learning (DL)-based SEI methods have been proposed, achieving outstanding performance. However, collecting and annotating substantial data for novel or unknown radiation sources is not only time-consuming but also cost-intensive. To address this issue, this paper proposes a few-shot (FS) metric learning-based time-frequency fusion network. To enhance the discriminative capability for radiation source signals, the model employs a convolutional block attention module (CBAM) and feature transformation to effectively fuse the raw signal’s time domain and time-frequency domain representations. Furthermore, to improve the extraction of discriminative features under FS scenarios, the proxy-anchor loss and center loss are introduced to reinforce intra-class compactness and inter-class separability. Experiments on the ADS-B and Wi-Fi datasets demonstrate that the proposed TFAF-Net consistently outperforms existing models in FS-SEI tasks. On the ADS-B dataset, TFAF-Net achieves a 9.59% higher accuracy in 30-way 1-shot classification compared to the second-best model, and reaches an accuracy of 85.02% in 10-way classification. On the Wi-Fi dataset, TFAF-Net attains 90.39% accuracy in 5-way 1-shot classification, outperforming the next best model by 6.28%, and shows a 13.18% improvement in 6-way classification.

This paper develops a wave-to-wire model of a vibro-impact wave energy converter array for stand-alone offshore applications. Nonlinear model predictive control is proposed for maximising the wave power capture of the array, and implemented by AC/DC converters and the space vector pulse width modulation technique. A hybrid energy storage system, consisting of batteries and supercapacitors, is placed parallel to the DC bus via buck-boost DC/DC converters to smooth the array power output, and a Lyapunov-based power management strategy is utilised to control the DC/DC converters for stabilising the DC bus voltage. Intensive numerical simulations are conducted; the results show that the proposed wave-to-wire model is capable to evaluate the performance of the vibro-impact wave energy converter array in various scenarios, and the proposed energy maximisation control and power management strategy can enhance wave power capture and stabilise the power output simultaneously.

In order to improve the wear resistance of copper and enhance the surface properties of copper parts, this article uses BN nanoparticles as a reinforcing phase and the laser remelting method to prepare a Cu/BN remelted layer on the copper surface. The surface morphology, crystal structure, microhardness, and wear resistance of the samples were tested and characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), a microhardness tester, and a friction and wear tester. The effects of laser frequency, pulse width, and energy density on the surface morphology and wear resistance of the samples were analyzed and studied, and the effects of the laser parameters on the properties of the Cu/BN remelted layer were discussed. The research results indicate that laser frequency, pulse width, and energy density have a direct impact on the surface morphology and properties of the Cu/BN remelted layer, but the impact mechanism by the above parameters on the remelted layer is different. The effects of laser frequency on the remelted layer are caused by changes in the overlap mode of the remelting points, while laser pulse width and energy density are achieved through changes in remelting intensity. When the laser frequency is 10 Hz, the pulse width is 10 ms, and the energy density is 165.8 J/mm2, the Cu/BN remelted layer has better surface properties.

In order to improve the surface quality of copper after laser remelting, this article took laser frequency, pulse width, and energy density as the research objects and used scanning electron microscopy (SEM), a laser confocal three-dimensional measurement instrument, hardness tester, and friction and wear measurement instrument to study the surface morphology, surface roughness, microhardness, and wear resistance of copper, respectively. The results indicate that the frequency, pulse width, and energy density of laser remelting could directly affect the surface quality of the sample, but the influence of frequency and pulse width was more significant. When the laser remelting frequency was 10 Hz, the pulse width was 10 ms, and the energy density was 132.69 J/mm2, the sample exhibited good surface morphology, roughness, and wear resistance. The relevant research in this article can provide a good reference for the laser surface treatment of copper-based materials.

Meta-path-based heterogeneous graph neural networks have received widespread attention for better mining the similarities between heterogeneous nodes and for discovering new recommendation rules. Most existing models depend solely on node IDs for learning node embeddings, failing to leverage attribute information fully and to clarify the reasons behind a user’s interest in specific items. A heterogeneous graph neural network for recommendation named MIMA (multi-feature interaction meta-path aggregation) is proposed to address these issues. Firstly, heterogeneous graphs consisting of user nodes, item nodes, and their feature nodes are constructed, and the meta-path containing users, items, and their attribute information is used to capture the correlations among different types of nodes. Secondly, MIMA integrates attention-based feature interaction and meta-path information aggregation to uncover structural and semantic information. Then, the constructed meta-path information is subjected to neighborhood aggregation through graph convolution to acquire the correlations between different types of nodes and to further facilitate high-order feature fusion. Furthermore, user and item embedding vector representations are obtained through multiple iterations. Finally, the effectiveness and interpretability of the proposed approach are validated on three publicly available datasets in terms of NDCG, precision, and recall and are compared to all baselines.

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals.

White pepper, used both as a seasoning in people’s daily diets and as a medicinal herb, is typically produced by removing the pericarp of green pepper through the retting process. However, the mechanism of the retting process for peeling remains unclear. Therefore, this study aimed to investigate the changes in physicochemical factors, microbial community succession effects, and metabolites of the pepper pericarp during the pepper peeling process. The findings indicated that pre-treatment involving physical friction before the retting process effectively reduced the production time of white pepper. During the retting process, the pectinase activity increased, leading to a decrease in the pectin content in the pepper pericarp. There was a significant correlation observed between the changes in pH, pectin content, and peeling rate and the Shannon diversity index of bacteria and fungi. Prevotella, Lactococcus, and Candida were the dominant microbial genera during the retting. The functional predictions suggested that the monosaccharides degraded from the pepper pericarp could have been utilized by microbes through sugar metabolism pathways. Metabolomic analysis showed that the metabolic pathways of carbohydrates and amino acids were the main pathways altered during the pepper peeling process. The verification experiment demonstrated that the degradation of pectin into galacturonic acid by polygalacturonase was identified as the key enzyme in shortening the pepper peeling time. The structure of the pepper pericarp collapsed after losing the support of pectin, as revealed by scanning electron microscopy. These results suggest that the decomposition of the pepper pericarp was driven by key microbiota. The succession of microbial communities was influenced by the metabolites of the pepper pericarp during retting. These findings provide new insights into the retting process and serve as an important reference for the industrial production of white pepper.

In space laser communication, the wide divergence angle of beacon light leads to substantial spatial losses, compounded by background light and detector noise; this results in compromised precision in the detection of the beacon light position. To solve this problem, a high-precision detection technique and communication composite technology employing a four-quadrant detector (QD) with beacon spread-spectrum modulation are proposed. Pseudo-random sequences (PRNs) are employed to spread the beacon communication spectrum, with the spread-spectrum signal utilized to modulate the intensity of the transmitted beacon light at the transmitter end. At the receiver, QD photocurrent signals are cross-correlated with an identical PRN that is used for modulation. The strong auto-correlation properties of PRNs, which are uncorrelated with noise, enhance the output signal-to-noise ratio (SNR), enabling precise position detection and beacon communication under high-SNR conditions. Theoretical analysis is used to explore the effects of spreading gain on the sensitivity of system detection and the precision of position detection. The experimental results demonstrate that the beacon spread-spectrum modulation scheme effectively detects the position of the light spot. At a received optical power of −37 dBm and spreading sequence PRN depths of 1023, 127, and 31, the root-mean-square error (RMSE) values are 0.983 μm, 2.876 μm, and 7.275 μm, respectively. This corresponds to improvements of 14.96 dB, 10.29, dB, and 6.26 dB compared to direct detection precision (30.811 μm). Additionally, under an identical signal bandwidth, the sensitivity improves by 14.6 dB, 10.1 dB, and 6.4 dB, respectively. The proposed beacon spread-spectrum scheme mitigates the limitations of hardware reception sensitivity and position-detection precision, demonstrating its potential application in high-precision detection in long-distance interstellar laser communication.

Optimal energy transmission dispatching of microgrid systems involves complicated transmission energy allocation and battery charging/discharging management and remains a difficult and challenging research problem subject to complex operation conditions and action constraints due to the randomness and volatility of new energy. Traditional microgrid transmission dispatching mainly considers the matching of the demand side and the supply side from a macro perspective, without considering the impact of line loss. Therefore, a Hierarchical Deep Q-network (HDQN) approach for microgrid energy dispatching is proposed to address this issue. The approach takes the power flow of each line and the battery charging/discharging behavior as decision variables to minimize the system operation cost. The proposed approach employs a two-layer agent optimization architecture for simultaneously processing the discrete and continuous variables, with one agent making upper layer decisions on the charging and discharging behavior of the batteries, and the other agent making lower layer decisions on the transmission energy allocation for the line. The experimental results indicate that the proposed approach achieves better performance than existing approaches.

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

This study explores the relative contributions and psychological mechanisms of the effects of family (father–child and mother–child) and school (teacher–student and student–student) interpersonal relationships on depression in elementary school children. The participants (n = 20,629) were elementary school children (48.19% male) aged nine to 13 years from Southwest China during the COVID-19 pandemic. They voluntarily completed questionnaires regarding parent–child, teacher–student, and student–student relationships, as well as academic stress and self-esteem. The results indicate that the effect of family interpersonal relationships on children’s depression was stronger than that of school interpersonal relationships. The predictive effects of father–child and mother–child relationships on children’s depression did not significantly differ; however, the effect of student–student relationships was significantly stronger than that of teacher–student relationships. Academic stress partially mediated the effect of interpersonal relationships on depression in elementary school children. The effects of family interpersonal relationships and academic stress on depression were moderated by self-esteem. These findings underscore the disparities and mechanisms pertaining to the impacts of diverse interpersonal associations on children’s depression, thus signifying significant implications for the advancement of research and intervention strategies aimed at addressing this issue.

Compared with electromagnetic compatibility (EMC) testing in anechoic rooms, open-area EMC testing takes advantage of in situ and engine running status measurement but suffers from non-negligible external electromagnetic interference. This paper proposes a novel environmental interference suppression method (named the EMC environmental interference suppression algorithm (E2ISA)) that separates signals from backgrounds via image segmentation and recognizes the near–far site signal via a group of time-varying features based on the difference in the near-site EM radiative characteristic. We find that the proposed E2ISA method, which combines the deep learning segmentation network with the classical recognition methods, is able to suppress environmental interference signals accurately. The experiment results show that the accuracy of E2ISA reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks.

Chlorhexidine (CHX) is widely considered to be the gold standard for preventing dental caries. However, it is possible to induce resistance to CHX. The LiaSR two-component system has been identified that contributed to CHX resistance in Streptococcus mutans, which is one of the major pathogens in dental caries. However, the underlying mechanisms remain unclear. In this study, an MIC assay and a viability assessment demonstrated that after deleting the liaS and liaR genes, the sensitivity of mutants could increase. The Nile Red efflux assay exhibited that the efflux rates of mutants were significantly decreased. The RT-qPCR results indicated that the LiaSR two-component system-mediating influence on the expression of lmrB in S. mutans contributed to the efflux rate. The hydrophobicity assay and membrane potential assay showed that the mutants had higher levels of hydrophobicity and depolarization, suggesting that their membranes were more easily disturbed. The TEM graphs revealed that the border of the cell membrane was unclear in mutants compared with the wild-type strain, indicating that the cell envelope’s stress response may have been inhibited. While the surface charge of mutants showed no significant difference in the wild-type strain according to the result of cytochrome c-based charged determination. This study provides valuable novel insights into the mechanisms of the LiaSR two-component system in the CHX resistance of S. mutans.

Transition metal phosphides (TMPs) have been widely studied for water decomposition for their monocatalytic property for anodic or cathodic reactions. However, their bifunctional catalytic activity still remains a major challenge. Herein, hexagonal nickel-cobalt bimetallic phosphide nanoneedles with 1–3 μm length and 15–30 nm diameter supported on NF (NixCo2−xP NDs/NF) with adjusted electron structure have been successfully prepared. The overall alkaline water electrolyzer composed of the optimal anode (Ni0.67Co1.33P NDs/NF) and cathode (Ni1.01Co0.99P NDs/NF) provide 100 mA cm−2 at 1.62 V. Gibbs Free Energy for reaction paths proves that the active site in the hydrogen evolution reaction (HER) is Ni and the oxygen evolution reaction (OER) is Co in NixCo2−xP, respectively. In the HER process, Co-doping can result in an apparent accumulation of charge around Ni active sites in favor of promoting HER activity of Ni sites, and ΔGH* of 0.19 eV is achieved. In the OER process, the abundant electron transfer around Co-active sites results in the excellent ability to adsorb and desorb *O and *OOH intermediates and an effectively reduced ∆GRDS of 0.37 eV. This research explains the regulation of electronic structure change on the active sites of bimetallic materials and provides an effective way to design a stable and effective electrocatalytic decomposition of alkaline water.

Free space optical communication is developing towards laser communication networking. A novel method of quadrant detector (QD) multi-spot position detection based on orthogonal frequency division multiplexing (OFDM) is proposed for multi-spot laser communication systems. The mathematical model is constructed, and the Monte Carlo method is used to validate it. The position calculation of three beams incident on QD is simulated. The influence of key parameters on the accuracy of position detection is analyzed when the spots are at the same position and a different position. The results show that when the SNR of the system is 31.74 dB, the radius of the Gauss spot is 2 mm, the number of FFT (fast Fourier transform) points is 1024, and the center of the multi-spot is at the same position of the detector target; the accuracy of signal position detection calculated by the equation is 1.433 μm, and the simulation results are 1.351 μm, 1.354 μm, and 1.389 μm, respectively. When the center position of the multi-spot is at different positions of the detector target, the detection accuracy calculated by the formula is 1.438 μm, 1.433 μm, and 1.434 μm, respectively, and the simulation results are 1.419 μm, 1.387 μm, and 1.346 μm, respectively. The experimental results verify the effectiveness of the proposed multi-target simultaneous detection method. This article proposes a new multi-spot position detection method which can not only achieve one-to-multiple node laser communication but also improve the accuracy of point position detection.

In the dynamic and evolving landscape of food science and technology, the quest to develop innovative and effective delivery systems for bioactive compounds remains a focal point of research and development [...]

Microseismic monitoring systems (MMS) have become increasingly crucial in detecting tremors in coal mining. Microseismic sensors (MS), integral components of MMS, profoundly influence positioning accuracy and energy calculations. Hence, calibrating these sensors holds immense importance. To bridge the research gap in MS calibration, this study conducted a systematic investigation. The main conclusions are as follows: based on calibration tests on 102 old MS using the CS18VLF vibration table, it became evident that certain long-used MS in coal mines exhibited significant deviations in frequency and amplitude measurements, indicating sensor failure. Three important calibration indexes, frequency deviation, amplitude deviation, and amplitude linearity are proposed to assess the performance of MS. By comparing the index of old and new MS, critical threshold values were established to evaluate sensor effectiveness. A well-functioning MS exhibits an absolute frequency deviation below 5%, an absolute amplitude deviation within 55%, and amplitude linearity surpassing 0.95. In normal operations, the frequency deviation of MS is significantly smaller than the amplitude deviation. Simplified waveform analysis has unveiled a linear connection between amplitude deviation and localization results. An analysis of the Gutenberg–Richter microseismic energy calculation formula found that the microseismic energy calculation is influenced by both the localization result and amplitude deviation, making it challenging to pinpoint the exact impact of amplitude deviation on microseismic energy. Reliable MS, as well as a robust MS, serve as the fundamental cornerstone for acquiring dependable microseismic data and are essential prerequisites for subsequent microseismic data mining. The insights and findings presented here provide valuable guidance for future MS calibration endeavors and ultimately can guarantee the dependability of microseismic data.

Hydrogen peroxide (H2O2) is an aqueous solution that is widely used for oxidation, disinfection and sterilization, and its detection is very important in the fields of biological health and environment. The main detection methods of H2O2 include colorimetric, electrochemical, enzymatic and fluorescence analysis. However, due to the influence of moisture and oxidation, it is very difficult to realize simple, convenient, real-time and efficient detection technology for hydrogen peroxide vapor (HPV). Recently, our group proposed adding ammonium titanyl oxalate (ATO) to the sensing film composite system to prepare a chemosensor based on PEDOT:PSS-ATO/PEDOT composite film. The limit of detection (LOD) of the film was 1.0 ppm, and the linear trend was in the range of 1.0 ppm to 10.5 ppm. We then explored the influence of various material systems on its HPV sensing performance, which exhibited both electrical and colorimetric responses. This study was expected to realize a practical HPV sensor as well as promote the further application of PEDOT-based composites in the field of chemosensors.

Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen. In particular, research on temporal knowledge graph reasoning holds great significance, as it can provide abundant knowledge for downstream tasks such as question answering and recommendation systems. Current reasoning research focuses primarily on interpolation and extrapolation. Extrapolation research aims to predict the likelihood of events occurring in future timestamps. Historical events are crucial for predicting future events. However, existing models struggle to fully capture the evolutionary characteristics of historical knowledge graphs. This paper proposes a multi-scale evolutionary network (MSEN) model that leverages Hierarchical Transfer aware Graph Neural Network (HT-GNN) in a local memory encoder to aggregate rich structural semantics from each timestamp’s knowledge graph. It also utilizes Time Related Graph Neural Network (TR-GNN) in a global memory encoder to model temporal-semantic dependencies of entities across the global knowledge graph, mining global evolutionary patterns. The model integrates information from both encoders to generate entity embeddings for predicting future events. The proposed MSEN model demonstrates strong performance compared to several baselines on typical benchmark datasets. Results show MSEN achieves the highest prediction accuracy.

Compared with other gas-phase analytes, the coexistence and competing effect of the moisture component has long created great difficulty and technical challenges for the reliable detection of chemically oxidizing active H2O2 vapor (HPV). Recently, our group designed and prepared a chemiresistive sensor based on conductive films of poly(3,4-ethylenedioxythiophene) (PEDOT), poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), and PEDOT:PSS/PEDOT to achieve direct detection of HPV at the ppm level. Specially, the hydrophobic PEDOT layer was polymerized on the surface of the PEDOT:PSS film using the electrochemical method to attenuate the adverse effect of moisture within HPV on the stability and detection performance of the PEDOT:PSS/PEDOT sensing film.

Through the selection of N-type organic semiconductor molecules and the method of supramolecular self-assembly at the solvent phase interface, a perylene tetracarboxylic diimide (PTCDIs) nanofiber film with loose and porous morphology was constructed via in situ deposition on the surface of ITO conductive glass. Then, the P-type organic semiconducting polymer poly(3,4−ethylenedioxythiophene) (PEDOT) was grown in the fiber interweaving network of this film via quantitative electrochemical polymerization, thus preparing a PTCDIs@PEDOT composite film with N−P heterojunction architecture. The composite film has a nanometer-sized N−P heterojunction interpenetrating network structure, which is beneficial for full exposure to and contact of hydrogen peroxide vapor (HPV). The response time is 5.76 min, the recovery time is 5.53 min, and the response to 1.0 ppm concentration of HPV is 1.76. The PTCDIs@PEDOT film has good moisture resistance and improved sensitivity and signal response for gas-phase H2O2 detection.

Conjugated polymers (CPs) are an intriguing material with which to build fluorescent Cr2O72− sensors with excellent sensitivity, but they often lack specific recognition groups. In this study, several typical amino acids with N and O atom-identifying groups were incorporated into fluorene, and then six polyfluorene derivatives were synthesized using electrochemical polymerization. Compared to other cations and anions, all of these amino acid-functionalized polyfluorenes have good selectivity towards Cr2O72− and enable ultra-trace responses with detection thresholds at pM or even fM level.

Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WRSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where MC is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches.

In this study, grand canonical Monte Carlo simulations (GCMC) and molecular dynamics simulations (MD) were used to construct models of activated carbon with hydroxyl-modified hexachlorobenzene basic unit contents of 0%, 12.5%, 25%, 35% and 50%. The mechanism of adsorption of carbon disulfide (CS2) by hydroxyl-modified activated carbon was then studied. It is found that the introduction of hydroxyl functional groups will improve the adsorption capacity of activated carbon for carbon disulfide. As far as the simulation results are concerned, the activated carbon model containing 25% hydroxyl modified activated carbon basic units has the best adsorption performance for carbon disulfide molecules at 318 K and atmospheric pressure. At the same time, the changes in the porosity, accessible surface area of the solvent, ultimate diameter and maximum pore diameter of the activated carbon model also led to great differences in the diffusion coefficient of carbon disulfide molecules in different hydroxyl-modified activated carbons. However, the same adsorption heat and temperature had little effect on the adsorption of carbon disulfide molecules.

The brown planthopper (BPH) is a major pest in rice cultivation, significantly affecting both yield and quality; accordingly, exploring and utilizing anti-herbivory genes to enhance rice’s inherent resistance to BPH can be an effective strategy for mitigating infestation. The effects of transposon insertion polymorphisms (TIPs) on rice’s resistance to insect pests have not been reported. In this study, through the identification of transposon insertion sites in susceptible and resistant rice varieties, a total of six possible candidate insect resistance genes were potentially located. Among them, a segment of the LTR/Copia transposon insertion was verified in the promoter of LOC_Os04g02720, which carries a cis-acting element binding site in rice involved in the abscisic acid reaction. Quantitative analysis showed a significant difference of the gene expression between insect-resistant and insect-susceptible varieties (p < 0.05). This study provides insights into the functional analysis of transposons and population transposon polymorphisms, whereas the identification of candidate insect resistance genes offers a theoretical foundation for the development of insect-resistant rice varieties.

BiGaO3 doped BiFeO3–BaTiO3 ceramics were prepared by the traditional solid-phase synthesis process. The phase analysis, microstructure, piezoelectric, ferroelectric, dielectric properties, and thermal stability of 0.7BiFeO3-(0.3 − x)BaTiO3-xBiGaO3 (Abbreviated as BF–BT-xBG) were investigated. The results show that the ceramics have rhombohedral (R) and tetragonal (T) structures. Particle dimensions gradually get bigger with the increase of BiGaO3 concentration, and dense ceramic grains were observed through SEM. Electrical properties of BF–BT-xBG are improved after adding a small amount of BiGaO3: piezoelectric constants d33 = 141 pC/N, electromechanical coupling coefficient kp = 0.314, mechanical Quality Factor Qm = 56.813, dielectric loss tanδ = 0.048, residual polarization intensity Pr = 18.3 µC/cm2, Curie temperature Tc = 485.2 °C, depolarization temperature Td = 465 °C for x = 0.003. The “temperature-piezoelectric performance” curve under in situ d33 indicates that piezoelectric properties d33 increase rapidly with increasing temperature. Remarkably, the piezoelectric response d33 reaches a maximum of 466 pC/N at a temperature T = 340 °C, and afterward, reduces gradually to zero with increasing temperature until 450 °C.

The precise control and monitoring of pH values remain critical for many chemical, physiological and biological processes. Perylene diimide (PDI)-based molecules and materials exhibit excellent thermal, chemical and photochemical stability, unique UV-vis absorption and fluorescent emission properties, low cytotoxicity, as well as intrinsic electron-withdrawing (n-type semiconductor) nature and impressive molecular assembly capability. These features combined enable promising applications of PDIs in chemosensors via optical signal modulations (e.g., fluorescent or colorimetric). One of the typical applications lies in the probing of pH under various conditions, which in turn helps monitor the extracellular (environmental) and intracellular pH change and pH-relying molecular recognition of inorganic or organic ions, as well as biological species, and so on. In this review, we give a special overview of the recent progress in PDI-based optical chemosensors for pH probing in various aqueous and binary water–organic media. Specific emphasis will be given to the key design roles of sensing materials regarding the architectures and the corresponding sensing mechanisms for a sensitive and selective pH response. The molecular design of PDIs and structural optimization of their assemblies in order to be suitable for sensing various pH ranges as applied in diverse scenarios will be discussed in detail. Moreover, the future perspective will be discussed, focusing on the current key challenges of PDI-based chemosensors in pH monitoring and the potential approach of new research, which may help address the challenges.

The policy guidance and financial support for industrial development from public finance provide an important guarantee for practicing green and circular development of agriculture. By sorting out the development context of fiscal support for agriculture in Henan province in different historical stages, this paper analyzes the development status of fiscal support for agriculture and the agricultural circular economy in the province. Relying on data envelopment analysis (DEA), it measures the efficiency of the agricultural circular economy in Henan province in 2013–2019 using the Charnes, Cooper, and Rhodes (CCR) model, the Banker, Charnes, and Cooper (BCC) model, and super-efficiency DEA models and empirically analyzes the effect of the fiscal expenditure for supporting agriculture on the efficiency of the agricultural circular economy using the fixed effect model. According to the results, (1) the expenditure on comprehensive agricultural development of Henan Province was mainly supported by the government’s fiscal funds to such a degree that the proportion of fiscal funds from the central government exceeded 40% throughout the year. Particular stress was laid on the land governance projects of comprehensive agricultural development in the province. (2) The overall development level of the agricultural circular economy in Henan Province was low, and there were considerable gaps between cities (counties). Under the super-efficiency DEA model, only Hebi City, which ranked first in terms of average efficiency, achieved relative efficiency. (3) The fiscal expenditure for supporting agriculture had a positive incentive effect on the efficiency of the agricultural circular economy in Henan province, and the incentive effect became more significant after the time effect was controlled. Accordingly, this paper puts forward some targeted policy suggestions, which enrich the existing research on fiscal support for agriculture and agricultural circular economy at both theoretical and practical levels.

Hydrogen peroxide (H2O2) is commonly used as an oxidizing, bleaching, or antiseptic agent. It is also hazardous at increased concentrations. It is therefore crucial to monitor the presence and concentration of H2O2, particularly in the vapor phase. However, it remains a challenge for many state-of-the-art chemical sensors (e.g., metal oxides) to detect hydrogen peroxide vapor (HPV) because of the interference of moisture in the form of humidity. Moisture, in the form of humidity, is guaranteed to be present in HPV to some extent. To meet this challenge, herein, we report a novel composite material based on poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) doped with ammonium titanyl oxalate (ATO). This material can be fabricated as a thin film on electrode substrates for use in chemiresistive sensing of HPV. The adsorbed H2O2 will react with ATO, causing a colorimetric response in the material body. Combining colorimetric and chemiresistive responses resulted in a more reliable dual-function sensing method that improved the selectivity and sensitivity. Moreover, the composite film of PEDOT:PSS-ATO could be coated with a layer of pure PEDOT via in situ electrochemical synthesis. The pure PEDOT layer was hydrophobic, shielding the sensor material underneath from coming into contact with moisture. This was shown to mitigate the interference of humidity when detecting H2O2. A combination of these material properties makes the double-layer composite film, namely PEDOT:PSS-ATO/PEDOT, an ideal sensor platform for the detection of HPV. For example, upon a 9 min exposure to HPV at a concentration of 1.9 ppm, the electrical resistance of the film increased threefold, surpassing the bounds of the safety threshold. Meanwhile, the colorimetric response observed was 2.55 (defined as the color change ratio), a ratio at which the color change could be easily seen by the naked eye and quantified. We expect that this reported dual-mode sensor will find extensive practical applications in the fields of health and security with real-time, onsite monitoring of HPV.

Physical Unclonable Functions (PUFs) are considered attractive low-cost security anchors in the key generation scheme. The helper data algorithm is usually used to transform the fuzzy responses extracted from PUF into a reproducible key. The generated key can be used to encrypt secret data in traditional security schemes. In contrast, this work shows that the fuzzy responses of both weak and strong PUFs can be used to secretly store the important data (e.g., the distributed keys) directly by an error-tolerant algorithm, Fuzzy Vault, without the traditional encryption algorithm and helper data scheme. The locking and unlocking methods of our proposal are designed to leverage the feature of weak and strong PUFs relatively. For the strong PUFs, our proposal is a new train of thought about how to leverage the advantage of strong PUFs (exponential number of challenge–response pairs) when used in the field. The evaluation was performed on existing weak PUF and strong PUF designs. The unlocking rate and runtime are tested under different parameters and environments. The test results demonstrate that our proposal can reach a 100% unlocking rate by parameter adjustment with less than 1 second of locking time and a few seconds of unlocking time. Finally, the tradeoff between security, reliability, and overhead of the new proposal is discussed.

Electronic Health Records (EHRs) must be encrypted for patient privacy; however, an encrypted EHR is a challenge for the administrator to categorize. In addition, EHRs are predictable and possible to be guessed, although they are in encryption style. In this work, we propose a secure scheme to support the categorization of encrypted EHRs, according to some keywords. In regard to the predictability of EHRs, we focused on guessing attacks from not only the storage server but also the group administrator. The experiment result shows that our scheme is efficient and practical.

Hydrogen peroxide (aqueous solution of H2O2) is one of the most used reagents i n medical sterilization, environmental disinfection, food storage, and other fields. However, hydrogen peroxide has the potential to cause serious harm to biological health and environmental safety. There are many methods (especially electrochemistry) for H2O2 detection in liquid phase systems, but a lack of methods for vapor detection. This is due to its colorless and tasteless nature, as well as the oxidative activity of the molecule and its coexistence with humidity. In this study, poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), one of the most commercially successful and widely used conductive polymers, was employed to fabricate an all-organic chemiresistive sensor for simple, real-time, and on-site sensing of hydrogen peroxide vapor (HPV) at room temperature. In comparison with pristine PEDOT:PSS film, the PEDOT:PSS/PEDOT film was prepared by in situ electrochemical polymerization. Upon exposure to different concentrations of HPV, it was found that the hydrophobic and porous PEDOT layer could weaken the interference of humidity in HPV sensing, resulting in a more sensitive and accurate response. At 1.0 ppm HPV concentration, the resistance signal response was increased by nearly 89% compared with the pristine PEDOT:PSS film. This PEDOT-film-based chemiresistive sensor showcases the possibility for further development of nonenzymatic HPV monitoring technology.

At present, marine autonomous surface ships (MASSs) play a huge role in marine shipping, surveying and mapping, safeguarding the rights and interests of marine space, and other maritime tasks. The cooperative operation of a swarm of MASSs can extend the scope of operation of the MASSs, and thus allow them to carry out more complex tasks. Path tracking is an important problem for the control of a swarm of MASSs. In this paper, the control of underactuated MASSs is decoupled, to control the heading and speed, respectively. First of all, in the path tracking, the improved arc LOS guidance law is introduced, and the heading torque controller is designed, so that the MASS can track the reference path efficiently and accurately. Then the single-path guided path tracking without formation of the swarm of MASSs is studied, the reference path of the swarm center tracking is defined, and the heave thrust controller of the swarm of MASSs is designed based on consensus theory, so that the surge velocity of the MASS can tend towards consistentcy, and finally converge to the desired speed. Finally, the effectiveness of the proposed control strategy is verified by two groups of simulation experiments.

Aiming at the existing problem of unmanned aerial vehicle (UAV) aerial photography for riders’ helmet wearing detection, a novel aerial remote sensing detection paradigm is proposed by combining super-resolution reconstruction, residual transformer-spatial attention, and you only look once version 5 (YOLOv5) image classifier. Due to its small target size, significant size change, and strong motion blur in UAV aerial images, the helmet detection model for riders has weak generalization ability and low accuracy. First, a ladder-type multi-attention network (LMNet) for target detection is designed to conquer these difficulties. The LMNet enables information interaction and fusion at each stage, fully extracts image features, and minimizes information loss. Second, the Residual Transformer 3D-spatial Attention Module (RT3DsAM) is proposed in this work, which digests information from global data that is important for feature representation and final classification detection. It also builds self-attention and enhances correlation between information. Third, the rider images detected by LMNet are cropped out and reconstructed by the enhanced super-resolution generative adversarial networks (ESRGAN) to restore more realistic texture information and sharp edges. Finally, the reconstructed images of riders are classified by the YOLOv5 classifier. The results of the experiment show that, when compared with the existing methods, our method improves the detection accuracy of riders’ helmets in aerial photography scenes, with the target detection mean average precision (mAP) evaluation indicator reaching 91.67%, and the image classification top1 accuracy (TOP1 ACC) gaining 94.23%.

Cataracts are an ailment representing the leading cause of blindness in the world. The pathogenesis of cataracts is not clear, and there is no effective treatment. An increasing amount of evidence shows that oxidative stress and autophagy in lens epithelial cells play a key role in the occurrence and development of cataracts. Buddleja officinalis Maxim flavonoids (BMF) are natural antioxidants and regulators that present anti-inflammatory and anti-tumor effects, among others. In this study, we optimized the extraction method of BMFs and detected three of their main active monomers (luteolin, apigenin, and acacetin). In addition, a model of oxidative damage model using rabbit lens epithelial cells induced by hydrogen peroxide (H2O2). By detecting the levels of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), malondialdehyde (MDA), and OH (OH), the expression of autophagosomes and autolysosomes were observed after MRFP-GFP-LC3 adenovirus was introduced into the cells. Western blotting was used to detect the expression of Beclin-1 and P62. Our research results showed that the optimal extraction parameters to obtain the highest yield of total flavonoids were a liquid–solid ratio of 1:31 g/mL, an ethanol volume fraction of 67%, an extraction time of 2.6 h, and an extraction temperature of 58 °C. Moreover, the content of luteolin was 690.85 ppb, that of apigenin was 114.91 ppb, and the content of acacetin was 5.617 ppb. After oxidative damage was induced by H2O2, the cell survival rate decreased significantly. BMFs could increase the levels of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) and decrease the levels of malondialdehyde (MDA) and OH (OH). After the MRFP-GFP-LC3 virus was introduced into rabbit lens epithelial cells and detecting the expression of P62 and Beclin-1, we found that the intervention of BMF could promote the binding of autophagosomes to lysosomes. Compared with the model group, the level of P62 in the low-, middle-, and high-dose groups of BMF was significantly down-regulated, the level of Beclin-1 was significantly increased, and the difference was statistically significant (p < 0.05). In other words, the optimized extraction method was better than others, and the purified BMF contained three main active monomers (luteolin, apigenin, and acacetin). In addition, BMFs could ameliorate the H2O2-induced oxidative damage to rabbit lens cells by promoting autophagy and regulating the level of antioxidation.

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera’s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions.

Nonalcoholic steatohepatitis (NASH) is one of the most common chronic liver diseases worldwide and no effective drugs or treatments have been approved for disease management. Recently, bariatric surgery (BS) is considered to be a novel disease-modifying therapy for NASH and liver metabolic diseases, according to clinical follow-up studies. Despite the revealment of physiopathological alterations, underlying mechanisms and key factors remain indeterminate. This study included multiple bulk RNA-sequencing datasets to investigate transcriptome variation in one-year follow-up BS and diet management (Diet) NASH patients’ liver biopsies. Liver functions, fibrosis, and carcinogenesis were predicted in liver samples via hallmark-based function enrichment analysis. Key factors generated from multi-dataset comparison were further assessed with hepatocellular carcinoma (HCC) progression and prognosis. BS leads to active gene expression alterations in NASH liver in comparison to diet management (Diet). Both approaches reduce cell stress and immune response, whereas BS contributes to higher metabolic levels and lower apoptosis levels. The macrophage infiltration, adipose accumulation, and fibroblast activation were revealed to be lower in post-BS NASH livers, further demonstrating positive correlations mutually. Seven key genes (MNDA, ALOX5AP, PECAM1, SPP1, CD86, FGF21, CSTA) were screened out as potential macrophage-associated and carcinogenetic factors suppressed by BS. SPP1 was identified as a crucial factor participating in BS intervened NASH-HCC progression. This study determined that BS exerts potentially superior protective functions in NASH livers compared to diet management. SPP1 may serve as a novel factor to study the functionalities of BS on NASH patients.

Amidinium salts have been utilized in perovskite precursor solutions as additives to improve the quality of perovskite films. The design of hydrophilic or hydrophobic groups in amidinium salts is of great importance to photovoltaic device performance and stability in particular. Here we report a contrast study of a guanidinium iodide (GUI) additive with a hydrophilic NH2 group, and a N,1–diiodoformamidine (DIFA) additive with a hydrophobic C–I group, to investigate the group effect. The addition of GUI or DIFA was beneficial to achieve high quality perovskite film and superior photovoltaic device performance. Compared with GUI, the addition of the DIFA in a perovskite precursor solution enhanced the crystal quality, reduced the defect density, and protected the water penetration into perovskite film. The perovskite solar cell (PSC) devices showed the best power conversion efficiency (PCE) of 21.19% for those modified with DIFA, as compared to 18.85% for the control, and 20.85% for those modified with GUI. In benefit to the hydrophobic C–I group, the DIFA–modified perovskite films and PSC exhibited the best light stability, thermal stability, and humidity stability in comparison to the control films and GUI–modified films. Overall, the introduction of a hydrophobic group in the amidinium salts additive was demonstrated to be an efficient approach to achieve high quality and stable perovskite film and PSC devices.

It is essential to promote the intelligence and autonomy of Maritime Autonomous Surface Ships (MASSs). This study proposed an automatic collision-avoidance method based on an improved Artificial Potential Field (APF) with the formation of MASSs (F-MASSs). Firstly, the navigation environment model was constructed by the S-57 Electronic Navigation Chart (ENC) data in Tianjin Port. The Formation Ship State Parameter (FSSP) definition was proposed for the port environment under multiple constraints that considered the navigation conditions of the MASSs. The formation pattern transformation was settled by changing the formation ship state parameter. Considering the constraints of an ‘unmanned–manned’ encounter situation, the static obstacles, and the design of the channel area improved artificial potential method for the formation. Finally, the simulation experiment was carried out in the sea near Tianjin Port to verify the effectiveness of the algorithm under multiple constraints. The results indicate that the method can satisfy the integrated operation of collision avoidance and path following in a constrained environment, and it can support the application of merchant F-MASS autonomous navigation in the future.

A FeNx-C-based catalyst is considered one of the most promising candidates for the highest oxygen reduction reaction (ORR) activities among nonprecious metal-based electrocatalysts. In this work, a unique catalyst of nitrogen-doped twisted macroscopic graphene tubes decorated with Fe-Nx and bamboo-like carbon nanotubes (CNT) was prepared by using twisted iron wire as a template and cyanamide as a carbon source. The microstructure and physicochemical natures of the samples were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopic (XPS), X-ray diffraction (XRD), and nitrogen adsorption/desorption measurements. Torsion can promote the dislocation of the iron wire lattice, and activate the surface Fe atoms, thus leading to the growth of bamboo-like carbon nanotubes and forming iron nitride. The product has a graphene-like macroscopic tube structure and exhibits excellent ORR activity. Such excellent ORR performance may be ascribed to the synergistic effect, including high ORR catalytic sites caused by the dislocation of the iron wire lattice, nitrogen heteroatoms doping, favorable reactant transport channels provided by macroscopic tube structure, and fast electron transfer rate induced by 3D continuous networks.

A novel nonlinear dynamic modeling approach is proposed for the T-shaped beam structures widely used in the field of aerospace. All of the geometrical nonlinearities including the terms in the deformation of the beams, the terms at the connections, and the free ends of beams are considered in the dynamic modeling process. The global mode method is employed to determine the natural frequencies and global mode shapes of the linearized system. The validity and accuracy of the derived model are verified by comparing the natural frequencies obtained with those calculated from FEM. Adopting the Galerkin truncation procedure, a set of reduced-order nonlinear ODEs is obtained for the structure. A study on the variation of dynamic responses taking the different numbers of global modes into account is performed to determine the number of modes taken in nonlinear vibration analysis. A comparison between the responses of the system with linear or nonlinear matching and boundary conditions is given to evaluate the importance of neglecting and reserving the nonlinear terms in matching and boundary conditions. It is shown that ignoring the nonlinear terms in both matching and boundary conditions may significantly alter the responses while developing the discretized governing ODEs of the structure.