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The grapevine, a globally significant fruit and an essential fruit tree species in China, is vulnerable to the adverse effects of high temperatures. Understanding the roles of microRNA and transcription factors in plant development and stress resistance is crucial for mitigating the impact of high temperature on grape growth and yield. This study investigates the response of miRNA to high-temperature stress in grape leaves. The expression level of Vvi-miR3633a was found to be inhibited under heat treatment in both Thompson seedless and Shen yue varieties, while its potential target genes (Vv-Atg36 and Vv-GA3ox2) were induced. Through transgenic overexpression experiments, it was demonstrated that Vvi-miR3633a plays a role in thermal response by affecting the expression of target genes. Furthermore, under heat stress conditions, overexpression of Vvi-miR3633a in grape callus decreased heat resistance compared to the control group (CK). The study also revealed that the target genes of Vvi-miR3633a regulate the expression of oxidase synthesis genes VvSOD and VvCAT, leading to reduced oxidase synthesis which may compromise the oxidation system. Additionally, the expression level of heat shock proteins in the transgenic lines was changed compared to the control (CK). Overall, this research provides valuable insights into understanding the molecular mechanisms involved in different crossing/breeding programs to produce heat-resistant grape varieties. Such varieties can be appropriate to propagate in warm climate areas with high temperature conditions.

This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors under various flight conditions. Utilizing a supersonic flat plate as the main structure, the research integrates various flight conditions into the aeroelastic equation. The resulting structural vibration data create a large-scale database for training the models. The dataset, divided into training, validation, and test sets, includes input features such as panel aspect ratio, Mach number, air density, and decay rate. The study highlights the importance of selecting appropriate hidden layers, epochs, and neurons to avoid overfitting. While DNN, LSTM, and LSTM-NN all showed improved training with more neurons and layers, excessive numbers beyond a certain point led to diminished accuracy and overfitting. Performance-wise, the LSTM-NN model achieved the highest accuracy in classification tasks, effectively capturing sequential features and enhancing classification precision. Conversely, LSTM excelled in regression tasks, adeptly handling long-term dependencies and complex non-linear relationships, making it ideal for predicting flutter Mach numbers. Despite LSTM’s higher accuracy, it required longer training times due to increased computational complexity, necessitating a balance between accuracy and training duration. The findings demonstrate that deep learning, particularly LSTM-NN, is highly effective in predicting panel flutter, showcasing its potential for broader aerospace engineering applications. By optimizing model architecture and training processes, deep learning models can achieve high accuracy in predicting critical aeroelastic phenomena, contributing to safer and more efficient aerospace designs.

This study introduces an innovative energy harvesting system designed for industrial applications such as fluid pipelines, air conditioning ducts, sewer systems, and subsea oil pipelines. The system integrates magneto-electric flow coupling and utilizes a dynamic vibration absorber (DVA) to mitigate the vibrations induced by fluid flow while simultaneously harvesting energy through magnetic dipole–dipole interactions in a vibration energy harvester (VEH). The theoretical models, based on Hamilton’s Principle and the Biot–Savart Law, were validated through comprehensive experiments. The results indicate the superior performance of the small-magnet system over the large-magnet system in both damping and power generation. The study analyzed the frequency response and energy conversion efficiency across different parameters, including the DVA mass, spring constant, and placement location. The experimental findings demonstrated significant vibration reduction and increased voltage output, validating the theoretical model. This research offers new avenues for energy harvesting systems in pipeline infrastructures, potentially enhancing energy efficiency and structural integrity.

Obesity is a multifactorial chronic inflammatory metabolic disorder, with pathogenesis influenced by genetic and non-genetic factors such as environment and diet. Intestinal microbes and their metabolites play significant roles in the occurrence and development of obesity by regulating energy metabolism, inducing chronic inflammation, and impacting intestinal hormone secretion. Epigenetics, which involves the regulation of host gene expression without changing the nucleotide sequence, provides an exact direction for us to understand how the environment, lifestyle factors, and other risk factors contribute to obesity. DNA methylation, as the most common epigenetic modification, is involved in the pathogenesis of various metabolic diseases. The epigenetic modification of the host is induced or regulated by the intestinal microbiota and their metabolites, linking the dynamic interaction between the microbiota and the host genome. In this review, we examined recent advancements in research, focusing on the involvement of intestinal microbiota and DNA methylation in the etiology and progression of obesity, as well as potential interactions between the two factors, providing novel perspectives and avenues for further elucidating the pathogenesis, prevention, and treatment of obesity.

Household energy consumption plays a critical role in the context of global climate change. Utilizing data from the 2018 China Social Survey (CGSS), this study empirically examined the impact of subjective socioeconomic status (SES) on household green energy consumption behaviors using probit and ordered probit models. The mechanism of influence was further analyzed through the mediated effect approach. The results found include the following: (1) Although the proportion of households participating in green energy policies is similar to those not participating, the proportion of households deeply participating in multiple policies is very low; (2) subjective SES significantly influences both the rate and depth of household participation in green energy policies; (3) internet usage and understanding of green energy policies serve as mediating mechanisms for the promotive effect of subjective SES; and (4) subjective SES showed significant heterogeneity in its effects on different gender and education level groups. These findings contribute to the understanding of the drivers of household green energy use decisions and provide an important reference for governmental policymaking to enhance participation rates and degrees in green energy participation. Implications of these findings highlight the potential for targeted policies that address internet accessibility and educational outreach, which could significantly enhance the effectiveness of green energy initiatives across diverse socioeconomic groups.

The development and deployment of diverse resistance sources in novel wheat cultivars underpin the durable control of stripe rust. The objectives of this study were to identify quantitative trait loci (QTL) associated with stripe rust resistance in the Chinese wheat breeding line YN1813 and to provide wheat breeders with original genes with potentially durable resistance. A total of 306 F7:8 recombinant inbred lines (RIL), derived from a cross between YN1813 (infection type 0–3 and disease severity 1–36%) and the moderately susceptible landrace Chinese Spring (IT 7–9 and DS 41–65%), were assessed for stripe rust disease severity in the field at Qingshui in Gansu and Pixian in Sichuan in 2020 and 2021 following inoculation with a mixture of the currently predominant Pst races. The parents and RIL were genotyped using the Wheat 55K single-nucleotide polymorphism (SNP) array. The total length of the constructed genetic linkage map was 3896.30 cm, with an average interval of 1.30 cm between adjacent markers. Two major QTL were identified on chromosome 7B and 7D across all tested environments. QYr.hau-7B was mapped to a 2.26 cm interval between the SNP markers AX-110908486–AX-89658728–AX-109489314 on chromosome 7B, explaining 0.9% to 16.9% of the phenotypic variation. QYr.hau-7D was positioned in a 0.67 cm interval flanked by the SNP markers AX-111654594 and AX-89378255, explaining 0.4% to 21.4% of the phenotypic variation. The QTL on 7D likely correspond to the previously known gene Yr18, whereas QYr.hau-7B was presumed to be a novel gene adjacent to YrZH84 or the core part of YrZH84. SNP markers closely linked with QYr.hau-7B were converted to allele-specific quantitative PCR-based genotyping assay (AQP) markers and validated in a panel of 712 wheat accessions. The group possessing a positive allele (TT) of AQP_AX-89658728 significantly (p < 0.05) decreased the IT by 45.8% and the DS by 63.2%. QYr.hau-7B and its markers could be useful in breeding programs to improve the level and durability of stripe rust resistance.

The alkali-activated blast furnace slag is attracting significant attention in replacing Portland cement due to several characteristics similar to cement hydration. However, there are a few practical problems with commercial alkali activators, such as the fast setting time, relatively high costs, and significant CO2 emissions during preparation. Thus, discovering industrial residues possessing inherent alkalinity are urgent. This study proposes the use of carbide slag at levels of 0%, 5%, 10%, 15%, 20%, and 30% and alkali at levels of 1%, 2%, 3%, 4%, 5%, 6%, 8%, and 10% activated blast furnace slag. The compressive strength and microstructure of carbide slag and alkali-activated blast furnace slag (CAB) pastes were examined using X-ray diffraction analysis (XRD), Differential Scanning Calorimetry/Thermogravimetric Analysis (DSC/TG), Fourier transform infrared spectroscopy (FTIR) and Scanning electron microscopy (SEM). The results revealed that the addition of carbide slag produced more hydrotalcite-like phase as well as decreased the content of ettringite (AFt) and the calcium–silicate–hydrate (C-S-H) gel, which decreased the compressive strength of the CAB pastes. At the age of 28 days, when the dosage was 5%, 10%, 15%, 20%, and 30%, the compressive strength of CAB mixes decreased by 2.1%, 7.1%, 9.2%, 9.8%, and 28.1%, respectively. The addition of NaOH promoted the formation of AFt, and there was an optimum level of NaOH corresponding to the high compressive strength of paste. At the age of 3 days and 7 days, the compressive strength reached its maximum at the dosage of 6% NaOH, which was 24.8 MPa and 36.3 MPa, respectively. However, at the ages of 14 days and 28 days, the compressive strength increased as the dosage of NaOH increased to 5%, which was 43.3 MPa and 44.5 MPa, respectively. The water curing could both enhance the early and later strength, the compressive strength of 23.3 MPa was gained at 3 days, and this increased by 16.3%, 24.0% and 36.9% at 7 days, 14 days and 28 days, respectively. Therefore, water curing was suitable for the strength development of CAB pastes.

In the field of perovskite optoelectronics, developing hole-transporting materials (HTMs) on the spiro[fluorene-9,9′-xanthene] (SFX) platform is one of the current research focuses. The SFX inherits the merits of spirobifluorene in terms of the configuration and property, but it is more easily derivatized and regulated by virtue of its binary structure. In this work, we design and synthesize four isomeric SFX-based HTMs, namely m-SFX-mF, p-SFX-mF, m-SFX-oF, and p-SFX-oF, through varying the positions of fluorination on the peripheral aniline units and their substitutions on the SFX core, and the optoelectronic performance of the resulting HTMs is evaluated in both perovskite solar cells (PSCs) and light-emitting diodes (PeLEDs) by the vacuum thermal evaporating hole-transporting layers (HTLs). The HTM p-SFX-oF exhibits an improved power conversion efficiency of 15.21% in an inverted PSC using CH3NH3PbI3 as an absorber, benefiting from the deep HOMO level and good HTL/perovskite interface contact. Meanwhile, the HTM m-SFX-mF provides a maximum external quantum efficiency of 3.15% in CsPb(Br/Cl)3-based PeLEDs, which is attributed to its perched HOMO level and shrunken band-gap for facilitating charge carrier injection and then exciton combination. Through elucidating the synergistic position effect of fluorination on aniline units and their substitutions on the SFX core, this work lays the foundation for developing low-cost and efficient HTMs in the future.

Nitrogen (N) application rates and planting density are key factors affecting grain yield and quality. In this experiment, four varieties, Huijing 753 (HJ 753), Huijing 855 (HJ 855), Huijing 866 (HJ 866), and Dangjing 8 (DJ 8), were used to investigate their effects on the yield and quality of japonica rice in Anhui, which has a lower soil N supply capacity. The current study set four levels of N application N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and three planting densities D1, D2, and D3 (high-density: 45 × 104; medium-density: 37.5 × 104; low-density: 32.2 × 104) hills ha−1 for a total of 12 treatments. The effects of N application and planting density on rice yield, composition, processing quality, appearance quality, and cooking quality were analyzed. The results showed that with the increase in N application rates and planting density, the effective panicle number per plant and grain number per panicle of each rice variety increased, while HJ 753, HJ 855, and HJ 866 reached the highest yield under the N3D1 treatment and then gradually declined, and DJ 8 had the highest yield in the N4D1 treatment. In addition, the processing quality of various varieties can be improved by increasing the N application rate and planting density, but the appearance quality will deteriorate, the amylose content will also decrease, and gel consistency will also be lower. Therefore, under medium-planting density conditions, N at a rate of 240 kg ha−1 was the best for HJ 753, HJ 855, and HJ 866, and using 300 kg ha−1 for DJ 8 resulted in high grain yields and superior rice grain quality. In the next step, physiological, biochemical, and genotypic analyses of these three japonica rice varieties were carried out to provide a scientific basis and technical support for accelerating the breeding of high-yield japonica rice varieties.

Magnesium matrix composites are essential lightweight metal matrix composites, following aluminum matrix composites, with outstanding application prospects in automotive, aerospace lightweight and biomedical materials because of their high specific strength, low density and specific stiffness, good casting performance and rich resources. However, the inherent low plasticity and poor fatigue resistance of magnesium hamper its further application to a certain extent. Many researchers have tried many strengthening methods to improve the properties of magnesium alloys, while the relationship between wear resistance and plasticity still needs to be further improved. The nanoparticles added exhibit a good strengthening effect, especially the ceramic nanoparticles. Nanoparticle-reinforced magnesium matrix composites not only exhibit a high impact toughness, but also maintain the high strength and wear resistance of ceramic materials, effectively balancing the restriction between the strength and toughness. Therefore, this work aims to provide a review of the state of the art of research on the matrix, reinforcement, design, properties and potential applications of nano-reinforced phase-reinforced magnesium matrix composites (especially ceramic nanoparticle-reinforced ones). The conventional and potential matrices for the fabrication of magnesium matrix composites are introduced. The classification and influence of ceramic reinforcements are assessed, and the factors influencing interface bonding strength between reinforcements and matrix, regulation and design, performance and application are analyzed. Finally, the scope of future research in this field is discussed.

Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (−1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s−1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modeling informed by biology can provide new pathophysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to the better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.

MiR399 plays an important role in plant growth and development. The objective of the present study was to elucidate the evolutionary characteristics of the MIR399 gene family in grapevine and investigate its role in stress response. To comprehensively investigate the functions of miR399 in grapevine, nine members of the Vvi-MIR399 family were identified based on the genome, using a miRBase database search, located on four chromosomes (Chr 2, Chr 10, Chr 15, and Chr 16). The lengths of the Vvi-miR399 precursor sequences ranged from 82 to 122 nt and they formed stable stem–loop structures, indicating that they could produce microRNAs (miRNAs). Furthermore, our results suggested that the 2 to 20 nt region of miR399 mature sequences were relatively conserved among family members. Phylogenetic analysis revealed that the Vvi-MIR399 members of dicots (Arabidopsis, tomato, and sweet orange) and monocots (rice and grapevine) could be divided into three clades, and most of the Vvi-MIR399s were closely related to sweet orange in dicots. Promoter analysis of Vvi-MIR399s showed that the majority of the predicted cis-elements were related to stress response. A total of 66.7% (6/9) of the Vvi-MIR399 promoters harbored drought, GA, and SA response elements, and 44.4% (4/9) of the Vvi-MIRR399 promoters also presented elements involved in ABA and MeJA response. The expression trend of Vvi-MIR399s was consistent in different tissues, with the lowest expression level in mature and young fruits and the highest expression level in stems and young leaves. However, nine Vvi-MIR399s and four target genes showed different expression patterns when exposed to low light, high light, heat, cold, drought, and salt stress. Interestingly, a putative target of Vvi-MIR399 targeted multiple genes; for example, seven Vvi-MIR399s simultaneously targeted VIT_213s0067g03280.1. Furthermore, overexpression of Vvi_MIR399e and Vvi_MIR399f in Arabidopsis enhanced tolerance to drought compared with wild-type (WT). In contrast, the survival rate of Vvi_MIR399d-overexpressed plants were zero after drought stress. In conclusion, Vvi-MIR399e and Vvi-MIR399f, which are related to drought tolerance in grapevine, provide candidate genes for future drought resistance breeding.

During the secondary thermoforming of carbon fiber-reinforced polyphenylene sulfide (CF/PPS) composites, a vital material for the aerospace field, varied thermal parameters profoundly influence the crystallization behavior of the PPS matrix. Notably, PPS exhibits a distinctive self-nucleation (SN) behavior during repeated thermal cycles. This behavior not only affects its crystallization but also impacts the processing and mechanical properties of PPS and CF/PPS composites. In this article, the effects of various parameters on the SN and non-isothermal crystallization behavior of PPS during two thermal cycles were systematically investigated by differential scanning calorimetry. It was found that the SN behavior was not affected by the cooling rate in the second thermal cycle. Furthermore, the lamellar annealing resulting from the heating process in both thermal cycles affected the temperature range for forming the special SN domain, because of the refined lamellar structure, and expelled various defects. Finally, this study indicated that to control the strong melt memory effect in the first thermal cycle, both the heating rate and processing melt temperature need to be controlled simultaneously. This work reveals that through collaborative control of these parameters, the crystalline morphology, crystallization temperature and crystallization rate in two thermal cycles are controlled. Furthermore, it presents a new perspective for controlling the crystallization behavior of the thermoplastic composite matrix during the secondary thermoforming process.

This study introduces a groundbreaking slap-type Vibration Energy Harvesting (VEH) system, leveraging a rotating shaft with magnets to induce vibrations in an adjacent elastic steel sheet through magnetic repulsion. This unique design causes the elastic sheet to vibrate, initiating the oscillation of a seesaw-type rigid plate lever. The lever then slaps a piezoelectric patch (PZT) at the elastic steel sheet’s root, converting vibrations into electrical energy. Notably, the design enables the PZT to withstand deformation and flapping forces simultaneously, enhancing power conversion efficiency. The driving force for the rotating shaft is harnessed from the downstream flow field generated by moving objects like rotorcraft, fixed-wing aircraft, motorcycles, and bicycles. Beyond conventional vibration energy harvesting, this design taps into additional electric energy generated by the PZT’s slapping force. This study includes mathematical modeling of nonlinear elastic beams, utilizing the Method of Multiple Scales (MOMS) for in-depth vibration mode analysis. Experimental validation ensures the convergence of theory and practice, confirming the feasibility and superior voltage generation efficiency of this slap-type VEH concept compared to traditional VEH systems.

Drought stress is one of the major abiotic stresses to wheat worldwide, with negative effects on wheat growth and yield. Assessing genetic variation and drought stress tolerance of key agronomic and physiological traits of spring wheat and screening germplasm resources for higher drought tolerance and yield stability are a prerequisite for developing new, better-adapted spring wheat varieties. This study evaluated nine important agronomic and physiological traits in 152 spring wheat cultivars under non-stress (NS) and drought-stress (DS) conditions. Under DS conditions, grain yield per plot (GYP) and grain weight per spike (GWE) were significantly reduced by 33.8% and 31.7%, and their drought-tolerance indexes (DIs) were only 0.66 and 0.69, respectively, indicating that GYP and GWE are the most susceptible traits to drought stress. The SPAD value of flag leave at flowering stage decreased by 13.9% under DS conditions, and the DI of SPAD was 0.86. In addition, DI-SPAD was significantly positively correlated with DIs of plant height (PH), grain number per spikelet (GPS), grain number per spike (GNS), GWE and GYP, indicating that the drought tolerance and yield of wheat are closely related to chlorophyll retention. Six wheat germplasm accessions were identified for their ability to sustain grain yield and improve drought tolerance simultaneously. These results provide insights into the genetic co-variation between grain yield and drought stress tolerance and provide a theoretical basis for the development of new wheat cultivars with excellent drought tolerance and high yields in the presence and absence of drought.

Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of vehicle destruction. This paper presents a multi-agent deep reinforcement learning-based DR strategy (DRS) that optimizes the multi-UAV group redeployment in terms of swarm performance. To generate a two-layer DRS between multiple groups and a single group, a multi-agent deep reinforcement learning framework is developed in which a QMIX network determines the swarm redeployment, and each deep Q-network determines the single-group redeployment. The proposed method is simulated using Python and a case study demonstrates its effectiveness as a high-quality DRS for large-scale scenarios.

This study describes a circular curve path-following controller for an underactuated unmanned surface vessel (USV) experiencing unmodeled dynamics and external disturbances. Initially, a three degrees of freedom kinematic model of the USV is proposed for marine environmental disturbances and internal model parameter deterrence. Then, the circular path guidance law and controller are designed to ensure that the USV can move along the desired path. During the design process, a proportional derivative (PD)-based sigmoid fuzzy function is applied to adjust the guidance law. To accommodate unknown system dynamics and perturbations, a radial basis function neural network and adaptive updating laws are adopted to design the surge motion and yaw motion controllers, estimating the unmodeled hydrodynamic coefficients and external disturbances. Theoretical analysis shows that tracking errors are uniformly ultimately bounded (UUB), and the closed-loop system is asymptotically stable. Finally, the simulation results show that the proposed controller can achieve good control effects while ensuring tracking accuracy and demonstrating satisfactory disturbance rejection capability.

Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE–YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE–YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.

Fruit ripening is a highly complicated process that is accompanied by the formation of fruit quality. In recent years, a series of studies have demonstrated post-transcriptional control play important roles in fruit ripening and fruit quality formation. Till now, the post-transcriptional mechanisms for watermelon fruit ripening have not been comprehensively studied. In this study, we conducted PacBio single-molecule long-read sequencing to identify genome-wide alternative splicing (AS), alternative polyadenylation (APA) and long non-coding RNAs (lncRNAs) in watermelon fruit. In total, 6,921,295 error-corrected and mapped full-length non-chimeric (FLNC) reads were obtained. Notably, more than 42,285 distinct splicing isoforms were derived from 5,891,183 intron-containing full-length FLNC reads, including a large number of AS events associated with fruit ripening. In addition, we characterized 21,506 polyadenylation sites from 11,611 genes, 8703 of which have APA sites. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that fructose and mannose metabolism, starch and sucrose metabolism and carotenoid biosynthesis were both enriched in genes undergoing AS and APA. These results suggest that post-transcriptional regulation might potentially have a key role in regulation of fruit ripening in watermelon. Taken together, our comprehensive PacBio long-read sequencing results offer a valuable resource for watermelon research, and provide new insights into the molecular mechanisms underlying the complex regulatory networks of watermelon fruit ripening.

This study addresses the problem of predicting convergence outcomes in the Duffing equation, a nonlinear second-order differential equation. The Duffing equation exhibits intriguing behavior in both undamped free vibration and forced vibration with damping, making it a subject of significant interest. In undamped free vibration, the convergence result oscillates randomly between 1 and −1, contingent upon initial conditions. For forced vibration with damping, multiple variables, including initial conditions and external forces, influence the vibration patterns, leading to diverse outcomes. To tackle this complex problem, we employ the fourth-order Runge–Kutta method to gather convergence results for both scenarios. Our approach leverages machine learning techniques, specifically the Long Short-Term Memory (LSTM) model and the LSTM-Neural Network (LSTM-NN) hybrid model. The LSTM-NN model, featuring additional hidden layers of neurons, offers enhanced predictive capabilities, achieving an impressive 98% accuracy on binary datasets. However, when predicting multiple solutions, the traditional LSTM method excels. The research encompasses three critical stages: data preprocessing, model training, and verification. Our findings demonstrate that while the LSTM-NN model performs exceptionally well in predicting binary outcomes, the LSTM model surpasses it in predicting multiple solutions.

This study aims to enhance conventional vibration energy harvesting systems (VEHs) by repositioning the piezoelectric patch (PZT) in the middle of a fixed–fixed elastic steel sheet instead of the root, as is commonly the case. The system is subjected to an axial simple harmonic force at one end to induce transversal vibration and deformation. To further improve power conversion, a baffle is strategically installed at the point of maximum deflection, introducing a slapping force to augment electrical energy harvesting. Employing the theory of nonlinear beams, the equation of motion for this nonlinear elastic beam is derived, and the method of multiple scales (MOMS) is used to analyze the phenomenon of parametric excitation. This study demonstrates through experiments and theoretical analysis that the second mode yields better power generation benefits than the first mode. Additionally, the voltage generation benefits of the enhanced system with the added baffle (slapping force) surpass those of traditional VEH systems. Overall, the proposed model proves feasible and holds promising potential for efficient vibration energy harvesting applications in various industrial sectors.

This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack of control in the sway channel. Secondly, nonlinear behavior stemming from saturation constraints is not always differentiable. Regarding this, a smooth dead-zone-based model was conducted to fit the behavior, leaving a relatively simple actuator model. Then, an improved prescribed time–prescribed performance function (PTPPF) and error transformation method were utilized to propose a model-parameter-free control algorithm that guarantees user-defined constrained boundaries while ensuring all tracking errors converge within small domains before a preassigned settling time. The theoretical analysis was conducted by the initial value theorem, Lyapunov’s second method, and proof by contradiction, followed by comparative simulation results that verified the effectiveness of the proposed control scheme.

The adverse effects of high temperatures on fruit quality and yield have been reported in many studies of grapevines. MiRNAs play crucial roles in plant growth and development and also fulfill functions in regulating the high-temperature response. In this research, miRNA-based SSR (simple sequence repeat) polymorphisms were analyzed according to the grape genome sequence and then combined with polymerase chain reaction (PCR) amplification and Sanger sequencing methods to analyze the miRNA-SSR diversity of different heat-resistant grape varieties. A statistical analysis of SSR sequences showed that 391 and 12 SSRs were specific to pri-miRNA and pre-miRNA, respectively. In pri-miRNA containing SSR, hexa-nucleotide repeats were the most abundant (52.69%), followed by tetra-nucleotide (13.04%) and minimum penta-nucleotide (4.09%), which were also observed in pre-miRNA sequences. On the other hand, differences in heat resistance among grape varieties were observed. Based on the results of leaf Fv/Fm images, values and phenotypic changes under high-temperature stress, 20 heat-resistant (e.g., Niagara Rosada and Grand Noir) and 20 heat-sensitive (e.g., Shine Muscat and Jumeigui) grape varieties were identified. Further, PCR-Sanger sequencing was used to screen SSR differences in four thermos-tolerant and four thermos-sensitive grape varieties, and finally, eight SSR differential primers were found to be able to distinguish these varieties. Of these, four pairs of primers were selected for validation in 40 grape germplasm resources (20 thermo-tolerance and 20 thermo-sensitivity). The VMIRSSR167c3 highlights that the ratio of SSR (CT) repeat values greater than 17 in heat-resistant varieties was 90%. In conclusion, the VMIRSSR167c3 marker can accurately distinguish between heat-resistant and heat-sensitive grape varieties. The results provide a novel molecular marker for the genetic improvement of grape germplasm resources and will be beneficial to the breeding of heat-resistant varieties in the future.

The present work demonstrated and compared the anti-inflammatory effects of celery leaf (CLE) and stem (CSE) extracts. LC-MS-based metabolomics were an effective approach to achieve the biomarker identification and pathway elucidation associated with the reduction in inflammatory responses. The celery extracts suppressed LPS-induced NO production in RAW 264.7 cells, and CLE was five times more effective than CSE. Distinct differences were revealed between the control and celery-treated samples among the 24 characteristic metabolites that were identified. In celery-treated LPS cells, reversals of intracellular (citrulline, proline, creatine) and extracellular (citrulline, lysine) metabolites revealed that the therapeutic outcomes were closely linked to arginine metabolism. Reversals of metabolites when treated with CLE (aspartate, proline) indicated targeted effects on the TCA and urea cycles, while, in the case of CSE (histidine, glucose), the glycolysis and the pentose phosphate pathways were implicated. Subsequently, apigenin and bergapten in CLE were identified as potential biomarkers mediating the anti-inflammatory response.

To explore the effects of competition, site, and climate on the growth of Chinese fir individual tree diameter at breast height (DBH) and tree height (H), a regionally compatible individual tree growth model under the combined influence of environment and competition was constructed. Using continuous forest inventory (CFI) sample plot data from Fujian Province between 1993 and 2018, we constructed an individual tree DBH model and an H model based on re-parameterization (RP), BP neural network (BP), and random forest (RF), which compared the accuracy of the different modeling methods. The results showed that the inclusion of competition and environmental factors could improve the prediction accuracy of the model. Among the site factors, slope position (PW) had the most significant effect, followed by elevation (HB) and slope aspect (PX). Among the climate factors, the highest contribution was made by degree-days above 18 °C (DD18), followed by mean annual precipitation (MAP) and Hargreaves reference evaporation (Eref). The comparison results of the three modeling methods show that the RF model has the best fitting effect. The R2 of the individual DBH model based on RF is 0.849, RMSE is 1.691 cm, and MAE is 1.267 cm. The R2 of the individual H model based on RF is 0.845, RMSE is 1.267 m, and MAE is 1.153 m. The model constructed in this study has the advantages of environmental sensitivity, statistical reliability, and prediction efficiency. The results can provide theoretical support for management decision-making and harvest prediction of mixed uneven-aged forest.

Airborne synthetic aperture radar (SAR) is susceptible to atmospheric disturbance and other factors that cause the position offset error of the antenna phase center and motion error. In close-range detection scenarios, the large elevation angle may make it impossible to directly observe areas near the underlying plane, resulting in observation blind spots. In cases where the illumination elevation angle is extremely large, the influence of range variant envelope error and phase modulations becomes more serious, and traditional two-step motion compensation (MOCO) methods may fail to provide accurate imaging. In addition, conventional phase gradient autofocus (PGA) algorithms suffer from reduced performance in scenes with few strong scattering points. To address these practical challenges, we propose an improved phase-weighted estimation PGA algorithm that analyzes the motion error of UAV SAR under a large elevation angle, providing a solution for high-order range variant motion error. Based on this algorithm, we introduce a combined focusing method that applies a threshold value for selection and optimization. Unlike traditional MOCO methods, our proposed method can more accurately compensate for spatially variant motion error in the case of scenes with few strong scattering points, indicating its wider applicability. The effectiveness of our proposed approach is verified by simulation and real data experimental results.

Circular RNAs (circRNAs) serve as covalently closed single-stranded RNAs and have been proposed to influence plant development and stress resistance. Grapevine is one of the most economically valuable fruit crops cultivated worldwide and is threatened by various abiotic stresses. Herein, we reported that a circRNA (Vv-circPTCD1) processed from the second exon of the pentatricopeptide repeat family gene PTCD1 was preferentially expressed in leaves and responded to salt and drought but not heat stress in grapevine. Additionally, the second exon sequence of PTCD1 was highly conserved, but the biogenesis of Vv-circPTCD1 is species-dependent in plants. It was further found that the overexpressed Vv-circPTCD1 can slightly decrease the abundance of the cognate host gene, and the neighboring genes are barely affected in the grapevine callus. Furthermore, we also successfully overexpressed the Vv-circPTCD1 and found that the Vv-circPTCD1 deteriorated the growth during heat, salt, and drought stresses in Arabidopsis. However, the biological effects on grapevine callus were not always consistent with those of Arabidopsis. Interestingly, we found that the transgenic plants of linear counterpart sequence also conferred the same phenotypes as those of circRNA during the three stress conditions, no matter what species it is. Those results imply that although the sequences are conserved, the biogenesis and functions of Vv-circPTCD1 are species-dependent. Our results indicate that the plant circRNA function investigation should be conducted in homologous species, which supports a valuable reference for further plant circRNA studies.

The Chinese language has witnessed remarkable changes in the past several decades, marked by a rapid rise of new words, frequent innovation of pseudo-affixes, and a notable increase in word length. By analyzing the creation, spread, and expansion of the new word 健康码 Jiankang-ma “health QR code”, this study sheds light on how language change takes place and how nonce formation is brought into being. Following the explosion of COVID-19 in China, 健康码 Jiankang-ma “health QR code” was created and promoted by the local and central governments and subsequently generated a large XX-码 XX-ma “XX-code” word family through various morphological processes, such as abbreviation, clipping, derivation, and analogy, where -码 -ma “-code” has acquired some new meaning distinct from its original form as a bound root. Linguistically, 健康码 Jiankang-ma “health QR code” is a three-morpheme word in a 2 + 1 length pattern, which phonologically consists of a single super foot that makes the expression catchy and appealing. The highly productive AB-C internal structure makes -码 -ma “-code”, although not yet a fully grammaticalized affix, a strong pseudo-suffix that has high morphological productivity with a fixed suffix positioning. Given the high frequency of the lexical 码 ma “code” in contemporary Chinese language use, the pseudo-suffix -码 -ma “-code” may, however, not completely replace it in the long run, as principles of grammaticalization theories predict. Instead, it is likely that both the lexical 码ma “code” and the pseudo-suffix -码 -ma “-code” will coexist side by side along split pathways.

The bis-benzimidazole derivative (BBM) molecule, consisting of two 2-(2′-hydroxyphenyl) benzimidazole (HBI) halves, has been synthesized and successfully utilized as a ratiometric fluorescence sensor for the sensitive detection of Cu2+ based on enol–keto excited-state intramolecular proton transfer (ESIPT). In this study, we strategically implement femtosecond stimulated Raman spectroscopy and several time-resolved electronic spectroscopies, aided by quantum chemical calculations to investigate the detailed primary photodynamics of the BBM molecule. The results demonstrate that the ESIPT from BBM-enol* to BBM-keto* was observed in only one of the HBI halves with a time constant of 300 fs; after that, the rotation of the dihedral angle between the two HBI halves generated a planarized BBM-keto* isomer in 3 ps, leading to a dynamic redshift of BBM-keto* emission.

The key component of electrochemical advanced oxidation technology are high-efficiency anodes, and highly efficient and simple-to-prepare materials have generated a lot of interest. In this study, novel self-supported Ti3+-doped titanium dioxide nanotube arrays (R-TNTs) anodes were successfully prepared by a two-step anodic oxidation and straightforward electrochemical reduction technique. The electrochemical reduction self-doping treatment produced more Ti3+ sites with stronger absorption in the UV-vis region, a band gap reduction from 2.86 to 2.48 ev, and a significant increase in electron transport rate. The electrochemical degradation effect of R-TNTs electrode on chloramphenicol (CAP) simulated wastewater was investigated. At pH = 5, current density of 8 mA cm−2, electrolyte concentration of 0.1 M sodium sulfate (Na2SO4), initial CAP concentration of 10 mg L−1, CAP degradation efficiency exceeded 95% after 40 min. In addition, molecular probe experiments and electron paramagnetic resonance (EPR) tests revealed that the active species were mainly •OH and SO4−, among which •OH played a major role. The CAP degradation intermediates were discovered using high-performance liquid chromatography-mass spectrometry (HPLC-MS), and three possible degradation mechanisms were postulated. In cycling experiments, the R-TNTs anode demonstrated good stability. The R-TNTs prepared in this paper were an anode electrocatalytic material with high catalytic activity and stability, which could provide a new approach for the preparation of electrochemical anode materials for difficult-to-degrade organic compounds.

Recently, hydrogels have attracted great attention because of their unique properties, including stretchability, self-adhesion, transparency, and biocompatibility. They can transmit electrical signals for potential applications in flexible electronics, human–machine interfaces, sensors, actuators, et al. MXene, a newly emerged two-dimensional (2D) nanomaterial, is an ideal candidate for wearable sensors, benefitting from its surface’s negatively charged hydrophilic nature, biocompatibility, high specific surface area, facile functionalization, and high metallic conductivity. However, stability has been a limiting factor for MXene-based applications, and fabricating MXene into hydrogels has been proven to significantly improve their stability. The unique and complex gel structure and gelation mechanism of MXene hydrogels require intensive research and engineering at nanoscale. Although the application of MXene-based composites in sensors has been widely studied, the preparation methods and applications of MXene-based hydrogels in wearable electronics is relatively rare. Thus, in order to facilitate the effective evolution of MXene hydrogel sensors, the design strategies, preparation methods, and applications of MXene hydrogels for flexible and wearable electronics are comprehensively discussed and summarized in this work.

Understanding the transformation of accumulated phosphorus (P) is vital for P management. However, previous studies are limited to a few sites in Chinese agroecosystems. In this study, to investigate the temporal-spatial differences of transformation from accumulated P to available P (determined by the Olsen method), a dataset was assembled based on 91 national long-term experimental sites across China in the recent 31 years (1988–2018). A boosted regression tree (BRT) and a structural equation model (SEM) were used to analyze the factors influencing the transformation. The results showed that the transformation from accumulated P to available P in South China (1.97 mg kg−1) was significantly higher than that in other regions (0.69–1.22 mg kg−1). Soil properties were the main driving factors with a relative contribution of 81.8%, while climate and management practices explained 7.8% and 10.4% of the variations, respectively. Furthermore, SEM analysis revealed that the soil organic matter (SOM) could positively and directly affect the transformation, whereas the soil pH, soil silt content, and P fertilizer had negative and direct effects on it. For the first time, this study analyzed the transformation from soil accumulated P to available P at a national scale and at multiple sites and quantified the contribution of the main influencing factors. These results help to predict the soil available P content across different agroecosystems based on the input amount of P fertilizer, contributing to the regional precise management of P fertilizer application.

Macrophages can be characterized as a very multifunctional cell type with a spectrum of phenotypes and functions being observed spatially and temporally in various disease states. Ample studies have now demonstrated a possible causal link between macrophage activation and the development of autoimmune disorders. How these cells may be contributing to the adaptive immune response and potentially perpetuating the progression of neurodegenerative diseases and neural injuries is not fully understood. Within this review, we hope to illustrate the role that macrophages and microglia play as initiators of adaptive immune response in various CNS diseases by offering evidence of: (1) the types of immune responses and the processes of antigen presentation in each disease, (2) receptors involved in macrophage/microglial phagocytosis of disease-related cell debris or molecules, and, finally, (3) the implications of macrophages/microglia on the pathogenesis of the diseases.

Metamaterials with actively tunable functionalities are highly desirable for applications of advanced optoelectronic devices. In this paper, we theoretically present a metamaterial with diversified functionalities by availing of the phase transition characteristics of vanadium dioxide (VO2) in terahertz frequency regions. The research results demonstrate that the function of the designed metamaterial can be switched from giant circular dichroism (CD) to a reflecting broadband half-wave plate (HWP) and a quarter-wave plate (QWP). When VO2 is in the isolating state, the metamaterial exhibits a quite distinct transmission efficiency for circularly polarized lights, thus resulting in a maximum CD value ~0.97 at the resonant frequency. When VO2 is operating in the metallic state, the metamaterial performs like a broadband HWP, in which the nearly perfect linear polarization conversion can be achieved at the frequency range from 3 to 7 THz. Moreover, the structure can play a role of a high-efficiency QWP that can simultaneously convert the incident linear polarized light to left-handed and right-handed circularly polarized light. The calculated ellipticity indicates a good polarization conversion at the frequency of 2.4 THz and 7.4 THz, respectively. The physical mechanism of the discussed features and effects can be explained by exploring the electric field distributions. Furthermore, the structural parameters also exert great influences for achieving giant CD and HWP as well as QWP. The proposed metamaterial may offer a new approach for designing metamaterial devices with multi-functions in THz regions.

The current literature on Christian mission universities in Modern China (1840–1949) pays specific attention to their efforts to adapt to the intellectual and political context of their time. Through extensive archival works, we contribute to this research orientation by documenting the academic activities of the first Catholic university in China, Université l’Aurore (1903–1952) in Shanghai. Established and managed by the French Jesuits, Université l’Aurore exemplified the mission’s tradition of evangelization through science education. Its pedagogical arrangements, selection of teachers, and moral education showed high levels of professionalism and almost no religious influence. The Jesuits, who took as their motto, “to find God in all things”, believed that their scientific excellence could indirectly promote the Catholic spirit among future Chinese intellectuals and elites. Thanks to their strategy of indirect evangelization, not only did Université l’Aurore survive in a period when the government imposed draconic restrictions on mission entities in the name of “educational sovereignty”, it also contributed to the modernization of China’s education and society.

High-purity titanium has been subjected to dynamic compression with a strain rate of 103 s−1 to activate {112-2} and {112-4} contraction twins. Electron backscatter diffraction (EBSD), transmission electron microscopy (TEM), and high-resolution TEM (HRTEM) were performed to observe the morphologies and twin boundaries of the contraction twins. The results show that {112-2} twins are the predominant twinning mode, as well as the formation of {112-4} twins due to the change in local stress state at the intersection region of {112-2} twin variants or {112-2} twin and grain boundary. The TEM and HRTEM observations reveal that (0001)‖(1122-) facets and (0001)‖(1121-) facets formed along the {112-2} and {112-4} twin boundaries, respectively. According to the theory of interfacial defects, the propagation of the {112-2} twin boundary was discussed with (b3, 3h{112-2}) and (b1, h{112-2}) twinning disconnections, as well as the growth process of the {112-4} twin boundary.

In this study, we investigated the phonon blockade effect in a parametrically driven and dissipative acoustic cavity at finite temperature. With the approximated analytical results based on the steady-state density-matrix master equation, we found that a quantum-interference-induced phonon blockade exists at finite temperature. We found a crossover between the quantum and thermal regimes on the curve of the second-order correlation function of the acoustic mode as the temperature increases. This phenomenon implies an asymmetry about the quantum and classic regimes. We also numerically simulated the single-phonon emission using the Monte Carlo wave function method. The results showed that a wide and deep dip around the zero time delay exists on the curve of the time-delayed second-order correlation function, which implies the possibility of observing a strong phonon blockade with pulse driving. Our study outlines a potential candidate for a efficient single-phonon source and applications in quantum information and phononic quantum networks.

Aiming at the enhancement of the lightweight formability potential of aluminum alloy, the bulging and tensile properties of a 5052 Aluminum alloy sheet were tested on a microcomputer controlled sheet metal forming tester and tensile testing machine. The effects of different blank holder force, punch velocity and lubrication conditions were investigated on bulging properties by the experimental analysis. The cupping values (Erichsen Cupping Index: IE) of sheets with a thickness of 1.2 mm at room temperature were obtained under different process parameters. Meanwhile, the anisotropic property of the material was analyzed in different rolling directions. The results show that the sheet cupping values increase with the increase of blank holder force and punch velocity, and the stress state was changed due to the changing of the blank holder force and strain rate. Moreover, the use of lubricating conditions with a lower coefficient of friction allows the sheet to exhibit a larger cupping value. The effect of rolling direction on the anisotropy of 5052 aluminum alloy sheet is distinct, which means in the aluminum alloy sheet forming process the anisotropy factor should be carefully considered.

In this study, a beta-barium borate sensing head (BBO-SH) was fabricated and evaluated for the measurements of fluidic concentration variations by using a non-invasive technique. The BBO-SH could be coupled to a fluidic container through thin interlayer water in a heterodyne interferometer based on the phase interrogation. To ensure the sensing head’s stability, the package of BBO-SH uses the prism and the coverslip bounded with UV glue, which can resist environmental damage due to moisture. After each use, the sensing head could be easily cleaned. The sensitivity of the BBO-SH remained stable after repeated measurements over a period of 139 days. Finally, the achievable measurement resolutions of the concentration and refractive index are 52 ppm and 1 × 10−6 RIU, respectively, for the sodium chloride solution. The achievable measurement resolutions of the concentration and refractive index were 55 ppm and 8.8 × 10−7 RIU, respectively, for the hydrochloric acid solution.

The ability of spoof surface plasmon polaritons (SSPPs) to confine electromagnetic fields in a subwavelength regime enables the design of miniaturized antennas. However, the impedance matching scheme for miniaturized spoof plasmonic antennas has not been studied systematically. In this paper, we propose a general method in the antenna design based on SSPPs, providing a feasible solution to impedance matching at the feeding point of miniaturized spoof plasmonic antennas. To verify the method, a prototype of a planar spoof plasmonic dipole antenna is simulated, fabricated and measured, of which the dipole arm length is reduced by 35.2% as compared with the traditional dipole antenna. A peak gain level of 4.29 dBi and the radiation efficiency of about 94.5% were measured at 6 GHz. This general method can be extended to solve the impedance matching problem in the design of other spoof plasmonic devices.

Background: Breast cancer is the most common cancer in women and the leading cause of female cancer deaths worldwide. Obesity causes chronic inflammation and is a risk factor for post-menopausal breast cancer and poor prognosis. Obesity triggers increased infiltration of macrophages into adipose tissue, yet little research has focused on the effects of macrophages in early stages of breast tumor development in obese patients. In this study, the effects of pro-inflammatory macrophages on breast cancer–adipocyte crosstalk were investigated. Methods: An innovative human cell co-culture system was built and used to model the paracrine interactions among adipocytes, macrophages, and breast cancer cells and how they facilitate tumor progression. The effects on cancer cells were examined using cell counts and migration assays. Quantitative reverse-transcription polymerase chain reaction was used to measure the expression levels of several cytokines and proteases to analyze adipocyte cancer association. Results: Macrophage-conditioned media intensified the effects of breast cancer–adipocyte crosstalk. Adipocytes became delipidated and increased production of pro-inflammatory cytokines, even in the absence of cancer cells, although the expression levels were highest with all three cell components. As a result, co-cultured breast cancer cells became more aggressive, with increased proliferation and migration compared to adipocyte–breast cancer co-cultures treated with unconditioned media. Conclusions: A novel co-culture model was built to evaluate the crosstalk among human macrophages, adipocytes, and breast cancer cells. We found that macrophages may contribute to adipocyte inflammation and cancer association and thus promote breast cancer progression.

For direct formic acid fuel cells (DFAFC), palladium (Pd)-based alloy catalysts with competitive morphology and elemental composition are essential to boost the performance of the formic acid oxidation reaction (FAOR) in the anode zone. Herein, we design and synthesize RuPdx alloy nano-network structures (ANs) via the facile wet-chemical reduction of Pd-Ru cyanogel (Pdx [Ru(CN)6]y·aH2O) as an effective electrocatalyst for the FAOR. The formation of Pd-Ru cyanogel depends on the facile coordination of K2PdCl4 and K3 [Ru(CN)6]. The unique structure of cyanogel ensures the presentation of a three-dimensional mesoporous morphology and the homogeneity of the elemental components. The as-prepared RuPd3 ANs exhibit good electrocatalytic activity and stability for the FAOR. Notably, the RuPd3 ANs achieve a mass-specific activity of 2068.4 mA mg−1 in FAOR, which shows an improvement of approximately 16.9 times compared to Pd black. Such a competitive FAOR performance of RuPd3 ANs can be attributed to the advantages of structure and composition, which facilitate the exposure of more active sites, accelerate mass/electron transfer rates, and promote gas escape from the catalyst layer, as well as enhance chemical stability.

Soft materials such as biological tissues are prone to deformation and generate different stable structures under external stimulation. This property is widely used to create tunable patterns, and the tuning of the wrinkling patterns can be applied to the control of elastic waves. In this paper, the wrinkling modes of film/substrate systems with different geometric dimensions and material parameters were studied. It is verified by numerical simulation that the elastic wave band gaps corresponding to the two wrinkling modes can be effectively superposed in one system, and the experimental samples with two wrinkling modes coexisting in one system were prepared by parameter optimization and a moisture-curing process. A vibration test showed that the hybrid system could effectively suppress the propagation of elastic waves. Combined with engineering needs, the wrinkling system under different loading conditions was studied, which provides a design guide for widening and regulating the elastic wave band gap.

This technical note proposes a clapping vibration energy harvesting system (CVEH system) installed in a rotating system. This device includes a rotating wheel, a drive shaft that rotates the wheel, and a double elastic steel sheet fixed on the drive shaft. One of the free ends of the steel is fixed with a magnet, and the free end of the other elastic steel is fixed with a PZT patch. We also install an array of magnets on the periphery (rim) of the wheel. The rim magnets repulse the magnet on the elastic steel sheet of the transmission shaft, causing the elastic steel to oscillate periodically, and slap the piezoelectric patch installed on the other elastic steel sheet to generate electricity. In this study, the authors’ previous study on the voltage output was improved, and the accurate nonlinear natural frequency of the elastic steel was obtained by the dimensional analysis method. By adjusting the rotation speed of the wheel, the precise frequency was controlled to accurately excite the energy harvesting system and obtain the best output voltage. A simple experiment was also performed to correlate with the theoretical model. The voltage and power output efficiencies of the nonlinear frequency to linear frequency excitation of the CVEH system can reach 15.7% and 33.5%, respectively. This study confirms that the clapping VEH system has practical power generation benefits, and verifies that nonlinear frequencies are more effective than linear frequencies to excite the CVEH system to generate electricity.

The number ratio of carbon-rich to oxygen-rich asymptotic giant branch (AGB) stars (the so-called C/M ratio) is closely related to the evolution environment of the host galaxy. This work studies the C/M ratio in 14 galaxies within the Local Group with the most complete and clean sample of member stars identified in our previous works. The borderlines between carbon-rich AGB and oxygen-rich AGB stars as well as red supergiants are defined by Gaussian mixture model fitting to the number density in the (J−K)/K diagram for the member stars of the LMC and M33, and then applied to the other galaxies by shifting the difference in the position of tip red giant branch (TRGB). The C/M ratios are obtained after precise and consistent categorization. Although for galaxies with larger distance modulo there is greater uncertainty, the C/M ratio is clearly found to decrease with the color index (J−K)0 of TRGB as the indicator of metallicity, which agrees with previous studies and can be explained by the fact that carbon stars are more easily formed in a metal-poor environment. Furthermore, the C/M ratio within M33 is found to increase with galactocentric distance, which coincides with this scenario and the galactic chemical evolution model. On the other hand, the C/M ratio within M31 is found to decrease with galactocentric radius, which deserves further study.

Central nervous system (CNS) trauma activates a persistent repair response that leads to fibrotic scar formation within the lesion. This scarring is similar to other organ fibrosis in many ways; however, the unique features of the CNS differentiate it from other organs. In this review, we discuss fibrotic scar formation in CNS trauma, including the cellular origins of fibroblasts, the mechanism of fibrotic scar formation following an injury, as well as the implication of the fibrotic scar in CNS tissue remodeling and regeneration. While discussing the shared features of CNS fibrotic scar and fibrosis outside the CNS, we highlight their differences and discuss therapeutic targets that may enhance regeneration in the CNS.

With the rapid development of high-resolution synthetic aperture radar (SAR) systems, the technique that utilizes multiple two-dimensional (2-D) SAR images with different view angles to extract three-dimensional (3-D) coordinates of targets has gained wide concern in recent years. Unlike the traditional multi-channel SAR utilized for 3-D coordinate extraction, the single-channel curvilinear SAR (CLSAR) has the advantages of large variation of view angle, requiring fewer acquisition data, and lower device cost. However, due to the complex aerodynamic configuration and flight characteristics, important issues should be considered, including the mathematical model establishment, imaging geometry analysis, and high-precision extraction model design. In this paper, to address these challenges, a 3-D vector model of CLSAR was presented and the imaging geometries under different view angles were analyzed. Then, a novel 3-D coordinate extraction approach based on radargrammetry was proposed, in which the unique property of the SAR system, called cylindrical symmetry, was utilized to establish a novel extraction model. Compared with the conventional approach, the proposed one has fewer constraints on the trajectory of radar platform, requires fewer model parameters, and can obtain higher extraction accuracy without the assistance of extra ground control points (GCPs). Numerical results using simulated data demonstrated the effectiveness of the proposed approach.

Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO2 batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs.