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It is a consensus that Earth’s climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period.

To reinforce natural rubber latex foam, fumed silica and precipitated silica are introduced into latex foam prepared using the Dunlop process as fillers. Four types of silica, including Aerosil 200 (hydrophilic fumed silica), Reolosil DM30, Aerosil R972 (hydrophobic fumed silica), and Sipernat 22S (precipitated silica), are investigated. The latex foam with added silica presents better mechanical and physical properties compared with the non-silica foam. The hydrophobic nature of the fumed silica has better dispersion in natural rubber compared to hydrophilic silica. The specific surface area of silica particles (BET) also significantly influences the properties of the latex foam, with larger specific surface areas resulting in better dispersity in the rubber matrix. It was observed that exceeding 2 phr led to difficulties in the foaming process (bulking). Furthermore, higher loading of silica also affected the rubber foam, resulting in an increased shrinkage percentage, hardness, compression set, and crosslink density. The crosslink density increased from 11.0 ± 0.2 mol/cm3 for non-silica rubber to 11.6 ± 0.6 mol/cm3 for Reolosil DM30. Reolosil DM30 also had the highest hardness, with a hardness value of 52.0 ± 2.1 IRHD, compared to 45.0 ± 1.3 IRHD for non-silica foam rubber and 48 ± 2.4 IRHD for hydrophilic fumed silica Aerosil 200. Hydrophobic fumed silica also had the highest ability to return to its original shape, with a recovery percentage of 88.0% ± 3.5% compared to the other fumed silica. Overall, hydrophobic fumed silica had better results than hydrophilic silica in both fumed and precipitated silica.

In order to improve the walking stability of a biped robot in multiple scenarios and reduce the complexity of the Central Pattern Generator (CPG) model, a new CPG walking controller based on multivariate linear mapping was proposed. At first, in order to establish a dynamics model, the lower limb mechanical structure of the biped robot was designed. According to the Lagrange and angular momentum conservation method, the hybrid dynamic model of the biped robot was established. The initial value of the robot’s passive walking was found by means of Poincaré mapping and cell mapping methods. Then, a multivariate linear mapping model was established to form a new lightweight CPG model based on a Hopf oscillator. According to the parameter distribution of the new CPG model, a preliminary parameter-tuning idea was proposed. At last, the joint simulation of MATLAB and V-REP shows that the biped robot based on the new CPG control has a stable periodic gait in flat and uphill scenes. The proposed method could improve the stability and versatility of bipedal walking in various environments and can provide general CPG generation and a tuning method reference for robotics scholars.

Synthetic microwave focusing methods have been widely adopted in qualitative medical imaging to detect and localize anomalies based on their electromagnetic scattering signatures. This paper discusses the principles, challenges, and limitations of synthetic microwave-focusing techniques in medical applications. It is shown that the various focusing techniques, including time reversal, confocal imaging, and delay-and-sum, are all based on the scalar solution of the electromagnetic scattering problem, assuming the imaged object, i.e., the tissue or object, is linear, reciprocal, and time-invariant. They all aim to generate a qualitative image, revealing any strong scatterer within the imaged domain. The differences among these techniques lie only in the assumptions made to derive the solution and create an image of the relevant tissue or object. To get a fast solution using limited computational resources, those methods assume the tissue is homogeneous and non-dispersive, and thus, a simplified far-field Green’s function is used. Some focusing methods compensate for dispersive effects and attenuation in lossy tissues. Other approaches replace the simplified Green’s function with more representative functions. While these focusing techniques offer benefits like speed and low computational requirements, they face significant ongoing challenges in real-life applications due to their oversimplified linear solutions to the complex problem of non-linear medical microwave imaging. This paper discusses these challenges and potential solutions.

Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.

A precise Johnson-Cook (J–C) constitutive model is the foundation for precise calculation of finite-element simulation. In order to obtain the J–C constitutive model accurately for a new cast and forged alloy GH4198, an inverse identification of J–C constitutive model was proposed based on a genetic–particle swarm algorithm. Firstly, a quasi-static tensile test at different strain rates was conducted to determine the initial yield strength A, strain hardening coefficient B, and work hardening exponent n for the material’s J–C model. Secondly, a new method for orthogonal cutting model was constructed based on the unequal division shear theory and considering the influence of tool edge radius. In order to obtain the strain-rate strengthening coefficient C and thermal softening coefficient m, an orthogonal cutting experiment was conducted. Finally, in order to validate the precision of the constitutive model, an orthogonal cutting thermo-mechanical coupling simulation model was established. Meanwhile, the sensitivity of J–C constitutive model parameters on simulation results was analyzed. The results indicate that the parameter m significantly affects chip morphology, and that the parameter C has a notable impact on the cutting force. This study addressed the issue of missing constitutive parameters for GH4198 and provided a theoretical reference for the optimization and identification of constitutive models for other aerospace materials.

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

With the rapid development of modern industry, it is urgently needed to measure the biotoxicity of complex chemicals. Microbial electrochemical biotoxicity sensors are an attractive technology; however, their application is usually limited by their stability and reusability after measurements. Here, we improve their performance by encapsulating the electroactive biofilm with polydopamine (PDA), and we evaluate the improvement by different concentrations of heavy metal ions (Cu2+, Ag+, and Fe3+) in terms of inhibition ratio (IR) and durability. Results indicate that the PDA-encapsulated sensor exhibits a more significant detection concentration than the control group, with a 3-fold increase for Cu2+ and a 1.5-fold increase for Ag+. Moreover, it achieves 15 more continuous toxicity tests than the control group, maintaining high electrochemical activity even after continuous toxicity impacts. Images from a confocal laser scanning microscope reveal that the PDA encapsulation protects the activity of the electroactive biofilm. The study, thus, demonstrates that PDA encapsulation is efficacious in improving the performance of microbial electrochemical biotoxicity sensors, which can extend its application to more complex media.

Climate change restricts and alters the distribution range of plant species. Predicting potential distribution and population dynamics is crucial to understanding species’ geographical distribution characteristics to harness their economic and ecological benefits. This study uses Dendrocalamus brandisii as the research subject, aiming to accurately reveal the impact of climate change on this plant. The findings offer important insights for developing practical conservation and utilization strategies, and guidance for future introduction and cultivation. The MaxEnt model was optimized using regularization multiplier (RM) and feature combination (FC) from the ‘Kuenm’ package in R language, coupled with ArcGIS for modeling 142 distribution points and 29 environmental factors of D. brandisii. This article explored the key environmental factors influencing the potential suitable regions for D. brandisii, and predicted trends in habitat changes under SSPs2.6 and SSPs8.5 climate scenarios for the current era, the 2050s, 2070s, and 2090s. (1) The results show that when FC = QPH and RM = 1, the AUC = 0.989, indicating that the model prediction is accurate with the lowest complexity and overfitting. The key environmental factors affecting its primary suitable distribution, determined by jackknife training gain and single-factor response curve, are the precipitation of warmest quarter (bio18), the temperature seasonality (bio4), the minimum average monthly radiation (uvb-4), and elevation (Elev), contributing 93.6% collectively. It was established that the optimal range for D. brandisii is precipitation of warmest quarter of between 657 and 999 mm, temperature seasonality from 351% to 442%, minimum average monthly radiation from 2420 to 2786 J/m2/day, at elevation from 1099 to 2217 m. (2) The current potential habitat distribution is somewhat fragmented, covering an area of 92.17 × 104 km2, mainly located in southwest, south, and southeast China, central Nepal, southern Bhutan, eastern India, northwestern Myanmar, northern Laos, and northern Vietnam. (3) In future periods, under different climate scenario models, the potential habitat of D. brandisii will change in varying degrees to become more fragmented, with its distribution center generally shifting westward. The SSP8.5 scenario is not as favorable for the growth of D. brandisii as the SSPs2.6. Central Nepal, southern Bhutan, and the southeastern coastal areas of China have the potential to become another significant cultivation region for D. brandisii. The results provide a scientific basis for the planning of priority planting locations for potential introduction of D. brandisii in consideration of its cultivation ranges.

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates trend towards secondary generation during extraction and transportation, leading to safety issues such as pipeline blockages. Consequently, developing new and efficient natural gas hydrate inhibitors has become a focal point in hydrate research. Kinetic hydrate inhibitors (KHIs) offer an effective solution by disrupting the nucleation and growth processes of hydrates without altering their thermodynamic equilibrium conditions. This paper systematically reviews the latest research progress and development trends in KHIs for natural gas hydrates, covering their development history, classification, and inhibition mechanisms. It particularly focuses on the chemical properties, inhibition effects, and mechanisms of polymer inhibitors such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap). Studies indicate that these polymer inhibitors provide an economical and efficient solution due to their low dosage and environmental friendliness. Additionally, this paper explores the environmental impact and biodegradability of these inhibitors, offering guidance for future research, including the development, optimization, and environmental assessment of new inhibitors. Through a comprehensive analysis of existing research, this work aims to provide a theoretical foundation and technical reference for the commercial development of natural gas hydrates, promoting their safe and efficient use as a clean energy resource.

Abrin and ricin, both type II ribosome-inactivating proteins, are toxins of significant concern and are under international restriction by the Chemical Weapons Convention and the Biological and Toxin Weapons Convention. The development of a rapid and sensitive detection method for these toxins is of the utmost importance for the first emergency response. Emerging rapid detection techniques, such as surface-enhanced Raman spectroscopy (SERS) and lateral flow assay (LFA), have garnered attention due to their high sensitivity, good selectivity, ease of operation, low cost, and disposability. In this work, we generated stable and high-affinity nanotags, via an efficient freezing method, to serve as the capture module for SERS-LFA. We then constructed a sandwich-style lateral flow test strip using a pair of glycoproteins, asialofetuin and concanavalin A, as the core affinity recognition molecules, capable of trace measurement for both abrin and ricin. The limit of detection for abrin and ricin was 0.1 and 0.3 ng/mL, respectively. This method was applied to analyze eight spiked white powder samples, one juice sample, and three actual botanic samples, aligning well with cytotoxicity assay outcomes. It demonstrated good inter-batch and intra-batch reproducibility among the test strips, and the detection could be completed within 15 min, indicating the suitability of this SERS-LFA method for the on-site rapid detection of abrin and ricin toxins.

Boron-doped diamond thin films exhibit extensive applications in chemical sensing, in which the performance could be further enhanced by nano-structuring of the surfaces. In order to discover the relationship between diamond nanostructures and properties, this paper is dedicated to deep learning target detection methods. However, great challenges, such as noise, unclear target boundaries, and mutual occlusion between targets, are inevitable during the target detection of nanostructures. To tackle these challenges, DWS-YOLOv8 (DCN + WIoU + SA + YOLOv8n) is introduced to optimize the YOLOv8n model for the detection of diamond nanostructures. A deformable convolutional C2f (DCN_C2f) module is integrated into the backbone network, as is a shuffling attention (SA) mechanism, for adaptively tuning the perceptual field of the network and reducing the effect of noise. Finally, Wise-IoU (WIoU)v3 is utilized as a bounding box regression loss to enhance the model’s ability to localize diamond nanostructures. Compared to YOLOv8n, a 9.4% higher detection accuracy is achieved for the present model with reduced computational complexity. Additionally, the enhancement of precision (P), recall (R), mAP@0.5, and mAP@0.5:0.95 is demonstrated, which validates the effectiveness of the present DWS-YOLOv8 method. These methods provide effective support for the subsequent understanding and customization of the properties of surface nanostructures.

Recycling flexible polyurethane foam (F-PUF) scraps is difficult due to the material’s high cross-linking structure. In this work, a wedge-block-reinforced extruder with a considerable enhanced shear extrusion and stretching area between the rotating screw and the stationary wedge blocks was utilized to recycle F-PUF scraps into powder containing surface-active hydroxyl groups. The powder was then utilized for the quantitative replacement of polyol in the foaming process. Characterizations showed that the continuous shear extrusion and stretching during the extrusion process reduced the volume mean diameter (VMD) of the F-PUF powder obtained by extruding it three times at room temperature to reach 54 μm. The -OH number (OHN) of the powder prepared by extruding it three times reached 19.51 mgKOH/g due to the mechanochemical effect of the powdering method. The F-PUF containing recycled powder used to quantitively replace 10 wt.% polyol was similar in microstructure and chemical structure to the original F-PUF, with a compression set of 2%, indentation load deflection of 21.3 lbf, resilience of 43.4%, air permeability of 815.7 L/m2·s, tensile strength of 73.0 Kpa, and tear strength of 2.3 N/cm, indicating that the recycling method has potential for industrial applications.

Ricin and abrin are highly potent plant-derived toxins, categorized as type II ribosome-inactivating proteins. High toxicity, accessibility, and the lack of effective countermeasures make them potential agents in bioterrorism and biowarfare, posing significant threats to public safety. Despite the existence of many effective analytical strategies for detecting these two lethal toxins, current methods are often hindered by limitations such as insufficient sensitivity, complex sample preparation, and most importantly, the inability to distinguish between biologically active and inactive toxin. In this study, a cytotoxicity assay was developed to detect active ricin and abrin based on their potent cell-killing capability. Among nine human cell lines derived from various organs, HeLa cells exhibited exceptional sensitivity, with limits of detection reaching 0.3 ng/mL and 0.03 ng/mL for ricin and abrin, respectively. Subsequently, toxin-specific neutralizing monoclonal antibodies MIL50 and 10D8 were used to facilitate the precise identification and differentiation of ricin and abrin. The method provides straightforward and sensitive detection in complex matrices including milk, plasma, coffee, orange juice, and tea via a simple serial-dilution procedure without any complex purification and enrichment steps. Furthermore, this assay was successfully applied in the unambiguous identification of active ricin and abrin in samples from OPCW biotoxin exercises.

8-17 DNAzymes (8-17, 17E, Mg5, and 17EV1) are in vitro-selected catalytic DNA molecules that are capable of cleaving complementary RNAs. The conserved residues in their similar catalytic cores, together with the metal ions, were suggested to contribute to the catalytic reaction. Based on the contribution of the less conserved residues in the bulge loop residues (W12, A15, A15.0) and the internal stem, new catalytic cores of 8-17 DNAzymes were programmed. The internal stem CTC-GAG seems to be more favorable for the DNAzymes than CCG-GGC, while an extra W12.0 led to a significant loss of activity of DNAzymes, which is contrary to the positive effect of A15.0, by which a new active DNAzyme 17EM was derived. It conducts a faster reaction than 17E. It is most active in the presence of Pb2+, with the metal ion preference of Pb2+ >> Zn2+ > Mn2+ > Ca2+ ≈ Mg2+. In the Pb2+ and Zn2+-mediated reactions of 17EM and 17E, the same Na+- and pH dependence were also observed as what was observed for 17E and other 8-17 DNAzymes. Therefore, 17EM is another member of the 8-17 DNAzymes, and it could be applied as a potential biosensor for RNA and metal ions.

An abnormal level of dopamine (DA), a kind of neurotransmitter, correlates with a series of diseases, including Parkinson’s disease, Willis-Ekbom disease, attention deficit hyperactivity disorder, and schizophrenia. Hence, it is imperative to achieve a precise, rapid detection method in clinical medicine. In this study, we synthesized nanocomposite carbon aerogels (CAs) doped with iron and iron carbide, based on algae residue-derived biomass materials, using Fe(NO3)3 as the iron source. The modified glassy carbon electrode (GCE) for DA detection, denoted as CAs-Fe/GCE, was prepared through surface modification with this composite material. X-ray photoelectron spectroscopy and X-ray diffraction characterization confirmed the successful doping of iron into the as-prepared CAs. Additionally, the electrochemical behavior of DA on the modified electrode surface was investigated and the results demonstrate that the addition of the CAs-Fe promoted the electron transfer rate, thereby enhancing their sensing performance. The fabricated electrochemical DA biosensor exhibits an accurate detection of DA in the concentration within the range of 0.01~200 µM, with a detection limit of 0.0033 µM. Furthermore, the proposed biosensor is validated in real samples, showing its high applicability for the detection of DA in beverages.

The extended duration of mulching in Xinjiang cotton fields leads to a significant decline in the tensile strength of plastic film. When recycling is in operation, the soil and the spring teeth of the machinery used can easily cause secondary damage and fracture the residual film. Establishing appropriate working parameters for recycling is essential to enhance the overall quality of collection efforts. By analyzing the motion process of a chain-tooth residual film pickup device, we identified key working parameters that significantly impact the efficiency of recycling. Employing the finite element method (FEM) and a coupled algorithm incorporating smooth particle hydrodynamics (SPH), we developed a coupled finite element model representing the interaction among spring teeth, soil, and residual film. Through simulation and analysis of the process of inserting the spring teeth into the soil to collect film, we derived the governing rules for residual film stress and deformation changes. Utilizing forward speed, rotational angular velocity, and angle of entry into the soil of the spring teeth as test factors and selecting the residual film stress and the residual film deformation as test indices, we conducted a multi-factor simulation test. We established a mathematical model correlating test factors with test indices, and the influence of each factor on the test index was analyzed. Subsequently, we optimized the working parameters of the spring teeth. The results indicated that the optimal working parameters are forward speed of 1111.11 mm/s, rotational angular velocity of 25 rad/s, and angle of entry into the soil of 30°. At these values, the average peak stress of residual film was 4.51 MPa and the height of residual film pickup was 84.48 mm. To validate the optimized the spring teeth impact on performance, field experiments were conducted with recovery rate and winding rate as test indices. The results demonstrated a 92.1% recovery rate and a 1.1% winding rate under the optimal combination of working parameters. The finite element model presented in this paper serves as a reference for designing and analyzing key components of residual film recycling machines.

TiCx/Al composites were successfully prepared in this study by dissolving graphite particles in Al-Ti melt based on the principle of a solid–liquid in situ reaction. It was observed that the microstructure of the TiCx/Al composites changed with changes in the reaction temperature and graphite particle size. With an increase in reaction temperature, the TiCx particles in the TiCx/Al composites transitioned from a spider-like distribution to being evenly dispersed in the Al matrix. Additionally, the morphology of the TiCx particles changed from polygons of various sizes to quasi-spherical shapes with a uniform particle size, while the presence of Al4C3 and Al3Ti in the matrix diminished. The size variation of the graphite particles had minimal impact on the particle size and stoichiometric ratio of TiCx generated in the sample. Furthermore, an appropriate graphite particle size was found to mitigate the agglomeration and residue of graphite particles during the in situ reaction.

Mulching practices have been used to improve peach growth and production across the globe. However, the impact of mulching on the physiochemical properties and soil characteristics of orchards remains largely unknown. This study aimed to decipher the impacts of various mulching patterns on the soil environment and the quality of Prunus persica fruit in “Zijinhuangcui”. Three treatments were set up, which included black ground fabric mulch (BF) and two living grass mulch treatments (HV: hairy vetch and RG: ryegrass). The results showed that different mulching treatments have different effects on soil, plant growth, and fruit quality. Living grass mulch treatments, especially the HV treatment, significantly improved soil nutrients by enhancing nitrogen-related indicators. Of note, the BF treatment had higher total phosphorus and available phosphorus contents than the HV and RG treatments. The HV treatment had the highest relative abundance of Proteobacteria (33.49%), which is associated with symbiotic nitrogen fixation, followed by RG (25.62%), and BF (22.38%) at the young fruit stage. Similarly, the abundance of Terrimonas, which has a unique nitrogen fixation system at the genus level, was significantly higher in the living grass mulch (HV, 1.30–3.13% and RG, 2.27–4.24%) than in the BF treatment. Living grass mulch also promoted tree growth, increased fruit sugar content, sugar-related components, and sugar-acid ratio, and reduced the acid content. Collectively, the findings of this study show that living grass mulch can promote tree growth and improve fruit quality by improving soil fertility, bacterial diversity, and richness.

Low-temperature mechanical chemical devulcanization is a process that can produce reclaimed rubber with exceptional mechanical properties. However, the inadequacy and low efficiency of the devulcanization have significantly restricted its application. To address the issues, alcoholic amines, including hydroxyethyl ethylenediamine (AEEA), ethanolamine (ETA), and diethanol amine (DEA), are utilized as devulcanizing agents to promote the devulcanization process. Careful characterizations are conducted to reveal the devulcanizing mechanism and to depict the performances of reclaimed rubbers. Results show that the amine groups in the devulcanizing agents can react with sulfur after the crosslink bonds are broken by mechanical shear force, thus blocking the activity of sulfur and introducing hydroxyl groups into the rubber chains. The incorporation of alcoholic amines can enhance the devulcanizing degree and devulcanizing efficiency, reduce the Mooney viscosity, and improve the mechanical and anti-aging performance. When using DEA as the devulcanizing agent, the sol content of reclaimed rubber increases from 13.1% to 22.4%, the devulcanization ratio increases from 82.1% to 89.0%, the Mooney viscosity decreases from 135.5 to 83.6, the tensile strength improves from 14.7 MPa to 16.3 MPa, the retention rate of tensile strength raises from 55.2% to 82.6% after aging for 72 h, while the devulcanization time is shortened from 21 min to 9.5 min, compared with that without using alcoholic amines. Therefore, alcoholic amines exhibit remarkable advantages in the devulcanization of waste rubber, thus indicating a promising direction for the advancement of research in the area of waste rubber reclamation.

Lead (Pb) pollution, especially from the incineration of municipal solid waste (MSW), poses a significant threat to the environment. Among all the effective methods, activated carbon (AC) injection serves as an effective approach for lead removal from flue gas, while the modification of ACs emerges as a crucial pathway for enhancing Pb adsorption capacities. Density functional theory (DFT) is employed in this study to investigate the mechanisms underlying the enhanced adsorption of Pb species (Pb0, PbO, and PbCl2) on nitrogen-functionalized carbonaceous surfaces. The results show that nitrogen-containing groups substantially enhance lead adsorption capacity, with adsorption energies ranging from −526.18 to −288.31 kJ/mol on nitrogen-decorated carbonaceous surfaces, much higher than those on unmodified surfaces (−310.35 to −260.96 kJ/mol). Additionally, electrostatic potential and density-of-states analyses evidence that pyridinic nitrogen atoms remarkably expand charge distribution and strengthen orbital hybridization, thereby augmenting lead capture. This research elucidates the role of nitrogen-containing functional groups in lead adsorption, offering valuable insights for the development of highly efficient biomass-derived activated carbon sorbents for lead removal.

Fu brick tea (FBT), a product of microbial fermentation from primary dark tea, also known as raw material tea (RMT), has been extensively studied for its functional properties. However, its potential as a staple food supplement for weight loss remains poorly understood. This study compared the weight loss effects of orlistat, traditional plain noodles (NN), and noodles supplemented with varying amounts of RMT (RMTN) and FBT (FBTN), with the aim to elucidate their lipid-reducing effects and underlying mechanisms. Experimental trials on high fat diet fed mice revealed significant weight loss, lipid-lowering, and hypoglycemic effects upon supplementation with orlistat, RMTN, and FBTN. Moreover, supplementation with orlistat, RMTN, and FBTN effectively restored serum and liver-related index levels, mitigating high-fat diet-induced dyslipidemia. Additionally, these supplements ameliorated liver and kidney damage by inhibiting oxidative stress and inflammatory responses. Furthermore, orlistat, RMTN, and FBTN exert their anti-obesity effects primarily by modulating genes associated with lipid metabolism and inflammatory responses and through regulation of the composition and structure of the gut microbiota. Importantly, FBTN demonstrated a significantly stronger lipid-lowering effect compared to RMTN, particularly at higher tea addition ratios. In contrast, NN supplementation exhibited minimal to no weight loss effects. Based on these findings, it could be inferred that FBT holds promise as a staple food supplement to ameliorate high-fat diet-induced obesity and its associated health conditions.

Oil-based drilling fluids are widely used in challenging wells such as those with large displacements, deepwater and ultra-deepwater wells, deep wells, and ultra-deep wells due to their excellent temperature resistance, inhibition properties, and lubrication. However, there is a challenging issue of rheological deterioration of drilling fluids under high-temperature conditions. In this study, a dual-amphiphilic segmented high-temperature-resistant gelling agent (HTR-GA) was synthesized using poly fatty acids and polyether amines as raw materials. Experimental results showed that the initial decomposition temperature of HTR-GA was 374 °C, indicating good thermal stability. After adding HTR-GA, the emulsion coalescence voltage increased for emulsions with different oil-to-water ratios. HTR-GA could construct a weak gel structure in oil-based drilling fluids, significantly enhancing the shear-thinning and thixotropic properties of oil-based drilling fluids under high-temperature conditions. Using HTR-GA as the core, a set of oil-based drilling fluid systems with good rheological properties, a density of 2.2 g/cm3, and temperature resistance up to 220 °C were constructed. After aging for 24 h at 220 °C, the dynamic shear force exceeded 10 Pa, and G′ exceeded 7 Pa, while after aging for 96 h at 220 °C, the dynamic shear force exceeded 4 Pa, and G″ reached 7 Pa. The synthesized compound HTR-GA has been empirically validated to significantly augment the rheological properties of oil-based drilling fluids, particularly under high-temperature conditions, showcasing impressive thermal stability with a resistance threshold of up to 220 °C. This notable enhancement provides critical technical reinforcement for progressive exploration endeavors in deep and ultra-deep well formations, specifically employing oil-based drilling fluids.

Arctic sea ice prediction is of great practical significance in facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily-scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the models’ capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the prediction accuracy of the four models significantly surpasses the CMIP6 model in three prospective climate scenarios (SSP126, SSP245, and SSP585). Of the four models, the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance than the PredRNN-multi model in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction, and meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin-ice region at the edge of the sea ice.

Chitosan nanoparticles (NPs) serve as useful multidrug delivery carriers in cancer chemotherapy. Chitosan has considerable potential in drug delivery systems (DDSs) for targeting tumor cells. Doxorubicin (DOX) has limited application due to its resistance and lack of specificity. Chitosan NPs have been used for DOX delivery because of their biocompatibility, biodegradability, drug encapsulation efficiency, and target specificity. In this review, various types of chitosan derivatives are discussed in DDSs to enhance the effectiveness of cancer treatments. Modified chitosan–DOX NP drug deliveries with other compounds also increase the penetration and efficiency of DOX against tumor cells. We also highlight the endogenous stimuli (pH, redox, enzyme) and exogenous stimuli (light, magnetic, ultrasound), and their positive effect on DOX drug delivery via chitosan NPs. Our study sheds light on the importance of chitosan NPs for DOX drug delivery in cancer treatment and may inspire the development of more effective approaches for cancer chemotherapy.

Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based approaches are susceptible to inaccuracies stemming from inadequate training datasets, primarily consisting of signals generated from simplified and homogeneous imaging scenarios. This paper introduces a novel methodology to construct an expansive and diverse database encompassing domains featuring randomly shaped structures with electrical properties representative of healthy and abnormal tissues. The core objective of this database is to enable the training of universal deep-learning techniques for permittivity profile reconstruction in complex electromagnetic medical imaging domains. The constructed database contains 25,000 unique objects created by superimposing from 6 to 24 randomly sized ellipses and polygons with varying electrical attributes. Introducing randomness in the database enhances training, allowing the neural network to achieve universality while reducing the risk of overfitting. The representative signals in the database are generated using an array of antennas that irradiate the imaging domain and capture scattered signals. A custom-designed U-net is trained by using those signals to generate the permittivity profile of the defined imaging domain. To assess the database and confirm the universality of the trained network, three distinct testing datasets with diverse objects are imaged using the designed U-net. Quantitative assessments of the generated images show promising results, with structural similarity scores consistently exceeding 0.84, normalized root mean square errors remaining below 14%, and peak signal-to-noise ratios exceeding 33 dB. These results demonstrate the practicality of the constructed database for training deep learning networks that have generalization capabilities in solving inverse problems in medical imaging without the need for additional physical assistant algorithms.

Sediment transport serves as a link for material exchange between land and sea. Using sediment traps, we can observe the capture and transport processes of sediments. Based on the sediment particle size distribution characteristics in Jiaozhou Bay, this paper analyzes the influence of a newly designed 3D sediment trap on the water–sand two-phase flow process inside and outside a trap device during its operation. Meanwhile, under a certain concentration condition, a numerical formula model is researched and proposed to evaluate the impact of the device’s structure, the environmental flow speed, and the particle size on particle capture efficiency. This model is based on the CFD-DPM coupling in Fluent 2021R1 software, and the particle filtration process is solved using a combination of porous media and UDF functions. Finally, by analyzing the distribution of sediment movement in the fluid domain, two concepts, namely the percentage of particles entering the tube and the effective capture rate, are proposed. Suggestions for optimizing the structure of the trap are put forward to achieve optimal capture effects.

Typical edge defects in the edge region of a new cemented carbide insert without edge preparation include burrs, poor surface quality, micro-breakages, and irregularities along the edge. To address the problems in new cemented carbide inserts without edge preparations, a chemical–mechanical synergistic preparation (CMSP) method for the cemented carbide insert cutting edge was proposed. Firstly, the CMSP device for the insert cutting edge was constructed. Then, the polishing slurry of the CMSP for the insert cutting edge was optimized using the Taguchi method combined with a grey relation analysis and fuzzy inference. Finally, orthogonal experiments, the Taguchi method, and analysis of variance (ANOVA) were used to investigate the effect of the polishing plate’s rotational speed, swing angle, and input frequency of the controller on the edge preparation process, and the parameters were optimized. The results showed that the best parameter combination for the polishing slurry for the cemented carbide inserts was the mass concentration of the abrasive particle of 10 wt%, the mass concentration of the oxidant of 10 wt%, the mass concentration of the dispersant of 2 wt%, and the pH of 8. The CMSP process parameter combination for the linear edge had the polishing plate’s rotational speed of 90 rpm, the swing angle of 6°, and the input frequency of the controller of 5000 Hz. The optimum CMSP process parameter combination for the circular edge had the polishing plate’s rotational speed of 90 rpm, the swing angle of 6°, and the input frequency of the controller of 7000 Hz. The polishing plate’s rotational speed had the most significant impact on the edge preparation process, followed by the swing angle, and the effect of the input frequency of the controller was the smallest. This study demonstrated that CMSP is a potential way to treat the cemented carbide insert cutting edge in a tool enterprise.

Mycoplasma synoviae infection rates in chickens are increasing worldwide. Genomic studies have considerably improved our understanding of M. synoviae biology and virulence. However, approximately 20% of the predicted proteins have unknown functions. In particular, the M. synoviae ATCC 25204 genome has 663 encoding DNA sequences, among which 155 are considered encoding hypothetical proteins (HPs). Several of these genes may encode unknown virulence factors. This study aims to reannotate all 155 proteins in M. synoviae ATCC 25204 to predict new potential virulence factors using currently available databases and bioinformatics tools. Finally, 125 proteins were reannotated, including enzymes (39%), lipoproteins (10%), DNA-binding proteins (6%), phase-variable hemagglutinin (19%), and other protein types (26%). Among 155 proteins, 28 proteins associated with virulence were detected, five of which were reannotated. Furthermore, HP expression was compared before and after the M. synoviae infection of cells to identify potential virulence-related proteins. The expression of 14 HP genes was upregulated, including that of five virulence-related genes. Our study improved the functional annotation of M. synoviae ATCC 25204 from 76% to 95% and enabled the discovery of potential virulence factors in the genome. Moreover, 14 proteins that may be involved in M. synoviae infection were identified, providing candidate proteins and facilitating the exploration of the infection mechanism of M. synoviae.

Triphenylphosphine acetylacetone carbonyl rhodium (ROPAC) is an important catalyst in the petrochemical industry, and its deactivated waste catalyst holds significant value for recovery. This study focuses on the existing forms of rhodium (Rh) in waste catalysts and the current status of traditional processes. A green, efficient, and continuous recovery technique was developed using a sealed stainless steel microchannel reactor. The influence of reaction temperature, reaction time, and phase ratio on the Rh recovery rate was investigated, and the process parameters were optimized using response surface methodology (RSM). The results indicate that the magnitude of the impact on the Rh recovery rate follows the order: reaction temperature > reaction time > phase ratio. The optimized process parameters were determined as follows: a reaction time of 29 min, a reaction temperature of 110 °C, and a phase ratio of 1:1, with a corresponding maximum recovery rate of Rh of 66.06%. Furthermore, secondary treatment was performed on the organic phase after primary recovery using the same process conditions, resulting in an overall Rh recovery rate of 95.6%, indicating satisfactory recovery efficiency. Moreover, the application of FTIR and ICP-OES analysis provided definitive evidence that the oxidative dissociation of the rhodium-phosphine chemical bond by H2O2 within ROPAC leads to the conversion of Rh+ into Rh3+. Subsequently, Rh forms chloroaquorhodium (III) complexes that enter the aqueous phase, enabling effective recovery of Rh.

Shale in the third member of the Jiufotang Formation at the Lujiapu Rift Basin is a new potential target for shale oil exploration and has rarely been studied before. In order to study pore structure and its controlling factors, shale compositions are mainly analyzed by X-ray Diffraction (XRD), and the characterization of full-scale pore structures is studied by the field-emission scanning electron microscope (FE-SEM), low-temperature N2 adsorption, and high-pressure mercury intrusion porosimetry (high-pressure MIP). According to composition and micro-texture, shale samples in the third member of the Jiufotang Formation are classified into three types: laminated organic matter-lean shale (TOC < 2%), unlaminated organic matter-intermediate shale (2% < TOC < 4%) and laminated organic matter-rich shale (TOC > 4%). Most shale samples are dominated by interparticle pores, with many of them filled by diagenetic minerals. All the shale samples are most developed in mesopores, whose development is mainly controlled by quartz content. And macropores with a diameter of 10,000 nm~100,000 nm are the secondary developed pores, which are influenced by both the paleoenvironment and diagenesis (especially clay transformation). Full-scale pore variations in laminated organic matter-lean shale, unlaminated organic matter-intermediate shale, and laminated organic matter-rich shale are ultimately related to their paleoenvironments.

The Long Short-Term Memory (LSTM) neural network model is an effective deep learning approach for predicting streamflow, and the investigation of the interpretability of deep learning models in streamflow prediction is of great significance for model transfer and improvement. In this study, four key hydrological stations in the Xijiang River Basin (XJB) in South China are taken as examples, and the performance of the LSTM model and its variant models in runoff prediction were evaluated under the same foresight period, and the impacts of different foresight periods on the prediction results were investigated based on the SHapley Additive exPlanations (SHAP) method to explore the interpretability of the LSTM model in runoff prediction. The results showed that (1) LSTM was the optimal model among the four models in the XJB; (2) the predicted results of the LSTM model decreased with the increase in foresight period, with the Nash–Sutcliffe efficiency coefficient (NSE) decreasing by 4.7% when the foresight period increased from one month to two months, and decreasing by 3.9% when the foresight period increased from two months to three months; (3) historical runoff had the greatest impact on streamflow prediction, followed by precipitation, evaporation, and the North Pacific Index (NPI); except evaporation, all the others were positively correlated. The results can provide a reference for monthly runoff prediction in the XJB.

Following an in-depth analysis of one-dimensional chaos, a randomized selective autoencoder neural network (AENN), and coupled chaotic mapping are proposed to address the short period and low complexity of one-dimensional chaos. An improved method is proposed for synchronizing keys during the transmission of one-time pad encryption, which can greatly reduce the usage of channel resources. Then, a joint encryption model based on randomized AENN and a new chaotic coupling mapping is proposed. The performance analysis concludes that the encryption model possesses a huge key space and high sensitivity, and achieves the effect of one-time pad encryption. Experimental results show that this model is a high-security joint encryption model that saves secure channel resources and has the ability to resist common attacks, such as exhaustive attacks, selective plaintext attacks, and statistical attacks.

Aluminum alloy adhesive bonding joint widely appears in many industrial products. Improving the mechanical performances of aluminum alloy bonding joints has been attracting much effort. To acquire more excellent bonding strength, this paper focused on the effects of different surface treatments, including laser ablation and milling superposed by phosphoric acid anodizing (PAA). The treated surfaces were characterized by roughness and contact angle, and the effects of the geometric parameters of microstructures on wettability, failure mode, and shear strength were examined. The results indicate that those surfaces where the spacing is smaller than the diameter present a hydrophilic property and the corresponding specimens are mainly subject to cohesive failure, and vice versa. Additionally, laser ablation with a properly designed dimple pattern can greatly improve the bonding strength, and the maximum average shear strength of specimens with a thickness of 50 μm reaches 32.82 MPa, which is an increase of 28.15% compared with the original milling specimen. Moreover, fabricating groove or grid patterns on the surfaces and applying PAA treatment can also significantly enhance the bonding strength, reaching up to 36.28 MPa.

The highly toxic plant toxin ricin is one of the most known threatening toxins. Accurate and sensitive biosensing methods for the first emergency response and intoxication treatment, are always pursued in the biodefense field. Screening affinity molecules is the fundamental mainstream approach for developing biosensing methods. Compared with common affinity molecules such as antibodies and oligonucleotide aptamers, peptides have great potential as biosensing modules with more accessible chemical synthesis capability and better batch-to-batch stability than antibodies, more abundant interaction sites, and robust sensing performance towards complex environments. However, anti-ricin peptides are so scant to be screened and discovered, and an advanced screening strategy is the utmost to tackle this issue. Here, we present a new in silico-in vitro iteration-assisted affinity maturation strategy of anti-ricin peptides. We first obtained affinity peptides targeting ricin through phage display with five panning rounds of “coating-elution-amplification-enrichment” procedures. The binding affinity and kinetic parameters characterized by surface plasmon resonance (SPR) showed that we had obtained four peptides owning dissociation constants (KD) around 2~35 μM, in which peptide PD-2-R5 has the lower KD of 4.7 μM and higher stable posture to interact with ricin. We then constructed a new strategy for affinity maturity, composing two rounds of in silico-in vitro iterations. Firstly, towards the single-site alanine scanning mutation peptide library, the molecular docking predictions match the SPR evaluation results well, laying a solid foundation for designing a full saturation mutated peptide library. Secondly, plenty of in silico saturation mutation prediction results guided the discovery of peptides PD2-R5-T3 and PD-2-R5-T4 with higher affinity from only a limited number of SPR evaluation experiments. Both evolved peptides had increased affinity by about 5~20 times, i.e., KD of 230 nM and 900 nM. A primary cellular toxicity assay indicated that both peptides could protect cells against ricin damage. We further established an SPR assay based on PD-2-R5-T3 and PD-2-R5-T4 elongated with an antifouling peptide linkage and achieved good linearity with a sensitivity of 1 nM and 0.5 nM, respectively. We hope this new affinity-mature strategy will find its favorable position in relevant peptide evolution, biosensing, and medical countermeasures for biotoxins to protect society’s security and human life better.

Preformed fragments can deform or even fracture when subjected to contact blasts, which might lead to a reduction of the terminal effect. Therefore, to solve this problem, the effect of surface electroplating on the fragment deformation behavior under contact blasts was analyzed. Firstly, blast recovery tests were carried out on uncoated and coated fragments. After the contact blast, the two samples produced different deformation behaviors: the uncoated fragments were fractured, while the coated fragments maintained integrity. The tests were simulated by finite element simulation, and the deformation behavior of the different samples matched well with the test results, which can explain the protective effect of the coating after quantification. In order to further reveal the dynamic behavior involved, detonation wave theory and shock wave transmission theory in solids were used to calculate the pressure amplitude variation at the far-exploding surface of the fragments. The theoretical results showed that the pressure amplitude of the uncoated samples instantly dropped to zero after the shock wave passed through the far-exploding surface, which resulted in the formation of a tensile zone. But the pressure amplitude of the coated samples increased, transforming the tensile zone into the compression zone, thereby preventing the fracture of the fragment near the far-exploding surface, which was consistent with the test and simulated results. The test results, finite element simulations, and theories show that the coating can change the deformation behavior of the fragment and prevent the fracture phenomenon of the fragment. It also prevents the material from missing and a molten state of the fragment in the radial direction by microscopic observation and weight statistics.

We report on a high-power acoustic-optical (AO) Q-switched Tm:YLF laser operating at ~1.83 μm by controlling the transmittance of the output coupler. Under the continuous-wave (CW) operation, the maximum output power of 13 W is achieved, and the slope efficiency is up to 32.7%. With a YAG etalon inserted into the cavity, the linewidth is compressed below 0.5 nm with a maximum output power of 12.2 W. In the Q-switched state, the maximum pulsed output power of 10.32 W is achieved with a pulse duration of 150 ns when the repetition rate is 15 kHz. And the maximum pulsed energy of 1.13 mJ is generated with a duration of 131 ns at 5 kHz. As far as we know, this is the highest output power reported for the CW and pulsed 1.83 μm laser. In addition, the relationship between the output wavelength and crystal length is theoretically analyzed, which shows that increasing the loss of 1880 nm is the key to high-power 1.83 μm laser output.

Visible-light-based transmission application plays an important role in various types of sensor services for the Internet of Things (IoTs). However, in big data scenarios, current visible-light-based systems cannot achieve concurrent high-speed communication, low-speed communication, and positioning. Therefore, in this article, we propose a smart visible-light-based fusion applications system, named Fasys, to solve the above problem for the big data traffic with heterogeneity. Specifically, for low-speed data services, we propose a novel linear block coding and bit interleaving mechanism, which enhances the LED positioning accuracy and recovers the lost data bits in the interframe gap (IFG). For high-speed data services with traffic possessing burstiness, an elegant statistical reliability analysis framework in regard to latency is proposed based on martingale theory. The backlog martingale process is constructed. Leveraging stopping time theory, a tight upper bound of unreliability is obtained. An arrival abstraction and traffic allocation scheme is designed, which contributes to decouple the reliability requirement as the maximum supportable arrival load. Finally, we implement our Fasys system, and extensive experimental results show that our system can achieve consistent high-precision positioning and low-BER data communication for low-speed data services. And the proposed martingale-based traffic allocation scheme can achieve the provisioning of reliability in regard to the latency for high-speed data services.

With the extensive use of plastic film mulching in planting, the amount of residual plastic film in soil has been increasing, causing soil and water pollution, harming crop growth, and reducing agricultural product quality and yield. In response to this issue, this study proposes a roll-type residual film recovery mechanism using the tillage layer method. The structure and operation principles of this system are discussed, and a detailed analysis of its key components and working process is provided. The lifting cutter operates at a speed of 22.37 rad/s, the pick-up drum at 4.58 rad/s, the roll forward rotation picks up the film at 13.74 rad/s, and the roll reverse film rotation unloads the film at 17.57 rad/s, with the unloading wheel rotating at a speed of 4.5 rad/s. ADAMS (software of automatic dynamic analysis of mechanical systems) Version No.2019 is used for modeling and analysis, and the displacement and velocity change patterns of MARKER_499, MARKER_500, and MARKER_505, which are marked points of the spring-tooth tips and are found to be the same. The maximum resultant displacement of MARKER_499 and MARKER_500 is 22.146 mm when picking up plastic film and 17.047 mm when unloading plastic film. Meanwhile, the maximum resultant displacement of MARKER_500 and MARKER_505 is 231.715 mm in the film-picking area and 234.028 mm in the film-unloading area. After analyzing the velocity of MARKER_499 during picking and unloading of the film, it was determined that the absolute velocity for the picking direction was 79,809.407 mm/s, and for the unloading direction it was 10,2266.168 mm/s. Bench tests show a tillage gathering rate of 71.6% and a surface gathering rate of 83.4%, meeting the performance requirements of the roller-type residual film recovery mechanism. These findings provide a theoretical basis for the design of the structure and operational parameters for the roll-type residual film recovery mechanism using the tillage layer method.

Scrophularia ningpoensis, a perennial medicinal plant from the Scrophulariaceae family, is the original species of Scrophulariae Radix (SR) in the Chinese Pharmacopoeia. This medicine is usually deliberately substituted or accidentally contaminated with other closely related species including S. kakudensis, S. buergeriana, and S. yoshimurae. Given the ambiguous identification of germplasm and complex evolutionary relationships within the genus, the complete chloroplast genomes of the four mentioned Scrophularia species were sequenced and characterized. Comparative genomic studies revealed a high degree of conservation in genomic structure, gene arrangement, and content within the species, with the entire chloroplast genome spanning 153,016–153,631 bp in full length, encoding 132 genes, including 80 protein-coding genes, 4 rRNA genes, 30 tRNA genes, and 18 duplicated genes. We identified 8 highly variable plastid regions and 39–44 SSRs as potential molecular markers for further species identification in the genus. The consistent and robust phylogenetic relationships of S. ningpoensis and its common adulterants were firstly established using a total of 28 plastid genomes from the Scrophulariaceae family. In the monophyletic group, S. kakudensis was determined to be the earliest diverging species, succeeded by S. ningpoensis. Meanwhile, S. yoshimurae and S. buergeriana were clustered together as sister clades. Our research manifestly illustrates the efficacy of plastid genomes in identifying S. ningpoensis and its counterfeits and will also contribute to a deeper understanding of the evolutionary processes within Scrophularia.

For fresh table grapes, flavor is one of the most important components of their overall quality. The flavor of table grapes includes both their taste and aroma, involving multiple physical and chemical properties, such as soluble solids. In this paper, we investigate six factors, divide flavor ratings into a range of five grades based on the results of trained food tasters, and propose a deep-neural-network-based flavor evaluation model that integrates an attention mechanism. After training, the proposed model achieved a prediction accuracy of 94.8% with an average difference of 2.657 points between the predicted score and the actual score. This work provides a promising solution to the evaluation of table grapes and has the potential to improve product quality for future breeding in agricultural engineering.

The axial distribution of initial velocity and direction angle of double-layer prefabricated fragments after an explosion were investigated via an explosion detonation test. A three-stage detonation driving model of double-layer prefabricated fragments was proposed. In the three-stage driving model, the acceleration process of double-layer prefabricated fragments is divided into three stages: “detonation wave acceleration stage”, “metal–medium interaction stage” and “detonation products acceleration stage”. The initial parameters of each layer of prefabricated fragments calculated by the three-stage detonation driving model of double-layer prefabricated fragments fit well with the test results. It was shown that the energy utilization rate of detonation products acting on the inner-layer and outer-layer fragments were 69% and 56%, respectively. The deceleration effect of sparse waves on the outer layer of fragments was weaker than that on the inner layer. The maximum initial velocity of fragments was located near the center of the warhead where the sparse waves intersected, located at around 0.66 times of the full length of warhead. This model can provide theoretical support and a design scheme for the initial parameter design of double-layer prefabricated fragment warheads.

Low biomass yield and nutrient removal efficiency are problems challenging the employment of microorganisms for wastewater remediation. Starch processing effluent (SPE) was used as a fermentation substrate to co-culture Chlorella vulgaris and Rhodotorula glutinis for biofuel feedstock production. Co-culture options were compared, and the optimal conditions were identified. The result shows that microalgae and yeast should be inoculated simultaneously at the beginning of SPE-based fermentation to achieve high biomass yield and the optimal inoculation ratio, light intensity, and temperature should be 2:1, 150 μmol/m2/s, and 25 °C, respectively. Under the optimal conditions, the lipid yield of microorganisms was 1.81 g/L and the carbon–conversion ratio reached 82.53% while lipid yield and the carbon–conversion ratio in a monoculture fell in the range of 0.79–0.81 g/L and 55.93–62.61%, respectively. Therefore, compared to the monoculture model, the co-fermentation of Chlorella vulgaris and Rhodotorula glutinis in starch processing effluent could convert nutrients to single-cell oil in a more efficient way. It should be noted that with the reduced concentration of residual organic carbon in effluent and the increased carbon–conversion ratio, co-fermentation of microalgae and yeast can be regarded as a promising and applicable strategy for starch processing effluent remediation and low-cost biofuel feedstock production.

Al–air battery has been regarded as a promising new energy source. However, the self-corrosion of aluminum anode leads to a loss of battery capacity and a decrease in battery longevity, limiting its commercial applications. Herein, indole-2-carboxylic acid (ICA) has been added to 4 M NaOH as a corrosion inhibitor. Its impact on the self-corrosion of aluminum alloy and the enhancement of the functionality of Al–air batteries at various concentrations have been investigated. X-ray photoelectron spectroscopy (XPS), attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM), and scanning electron microscopy (SEM) techniques have been used to examine the compositional and morphological alterations of aluminum alloy surfaces. Electrochemical and hydrogen evolution tests showed that indole-2-carboxylic acid is an efficient corrosion inhibitor in alkaline solutions, and its impact grows with concentration. Our findings demonstrated that when the inhibitor concentration is 0.07 M, the inhibition efficiency is 54.0%, the anode utilization rises from 40.2% to 79.9%, the capacity density increases from 1197.6 to 2380.9 mAh g−1, and the energy density increases from 1469.9 to 2951.8 Wh kg−1. In addition, theoretical calculations have been performed to support the experimental results.

Cold stress impairs plant growth and development, resulting in crop failure. Cultivated potato (Solanum tuberosum L.) is sensitive to freezing, while its wild relative, S. commersonii, has a strong freezing tolerance. To decipher the anti-freezing mechanism of CM, we carried out a transcriptomic and metabolomic analysis of an anti-freezing variety of CM (a type of S. commersonii) and a freeze-sensitive variety of DM (a type of Solanum tuberosum L.). A total of 49,232 high-quality transcripts from 12,811 gene loci, including 46,772 coding sequences and 2018 non-coding RNAs, were identified. KEEG enrichment analysis of differentially expressed genes (DEGs) between the two varieties showed that the flavonoid biosynthesis pathway was strongly induced by freezing stress, which was proven by flavonoid metabolome analysis. Consistent with the accumulation of more flavonoids, nearly all the pathway genes were significantly upregulated in CM than those in DM. The transcript levels of two chalcone synthase (CHS-1) isoforms and four isoforms of flavonoid 3′-hydroxylase (F3′H-1) were confirmed by qRT-PCR. Co-expression analysis identified one Myb-related and three UGTs (UDP-glycosyltransferase) that were significantly upregulated in CM during freezing stress. Our findings support that the flavonoid pathway was significantly enhanced by freezing stress and the greater accumulation ofglycosylatedflavonoids in resistant types than that of sensitive types, maybe accounting for the increased freezing tolerance of freeze-resistant potato varieties.

The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.

In recent years, millimeter-wave communication has played a crucial role in satellite communication, 5G, and even 6G applications. The millimeter-wave electro-optic modulator is capable of receiving and processing millimeter-wave signals effectively. However, the large attenuation of millimeter waves in the air remains a primary limiting factor for their future applications. Therefore, finding a waveguide structure with a high quality factor (Q-factor) is critical for millimeter-wave electro-optic modulators. This manuscript presents the demonstration of a double ring modulator made of lithium niobate with the specific goal of modulating an RF signal at approximately 35 GHz. By optimizing the microring structure, the double ring resonator with high Q-factor is studied to obtain high sensitivity modulation of the RF signal. This manuscript employs the transfer matrix method to investigate the operational principles of the double ring structure and conducts simulations to explore the influence of structural parameters on its performance. Through a comparison with the traditional single ring structure, it is observed that the Q-factor of the double ring modulator can reach 7.05 × 108, which is two orders of magnitude greater than that of the single ring structure. Meanwhile, the electro-optical tunability of the double ring modulator is 6 pm/V with a bandwidth of 2.4 pm, which only needs 0.4 V driving voltage. The high Q double ring structure proposed in this study has potential applications not only in the field of communication but also as a promising candidate for a variety of chemical and biomedical sensing applications.

Designing functional membranes through the collaboration of multi-dimensional nanomaterials is of particular interest in environmental and biomedical applications. Herein, we propose a facile and green synthetic strategy by collaborating with graphene oxide (GO), peptides, and silver nanoparticles (AgNPs) to synthesize functional hybrid membranes with favourable antibacterial effects. GO nanosheets are functionalized with self-assembled peptide nanofibers (PNFs) to form GO/PNFs nanohybrids, in which the PNFs not only improve the biocompatibility and dispersity of GO, but also provide more active sites for growing and anchoring AgNPs. As a result, multifunctional GO/PNFs/AgNP hybrid membranes with adjustable thickness and AgNP density are prepared via the solvent evaporation technique. The structural morphology of the as-prepared membranes is characterized using scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy, and their properties are analyzed by spectral methods. The hybrid membranes are then subjected to antibacterial experiments and their excellent antibacterial performances are demonstrated.

The pathogenicity of foodborne Vibrio parahaemolyticus is a major concern for global public health. This study aimed to optimize the liquid–solid extraction of Wu Wei Zi extracts (WWZE) against Vibrio parahaemolyticus, identify its main components, and investigate the anti-biofilm action. The extraction conditions optimized by the single-factor test and response surface methodology were ethanol concentration of 69%, temperature at 91 °C, time of 143 min, and liquid–solid ratio of 20:1 mL/g. After high performance liquid chromatography (HPLC) analysis, it was found that the main active ingredients of WWZE were schisandrol A, schisandrol B, schisantherin A, schisanhenol, and schisandrin A–C. The minimum inhibitory concentration (MIC) of WWZE, schisantherin A, and schisandrol B measured by broth microdilution assay was 1.25, 0.625, and 1.25 mg/mL, respectively, while the MIC of the other five compounds was higher than 2.5 mg/mL, indicating that schisantherin A and schizandrol B were the main antibacterial components of WWZE. Crystal violet, Coomassie brilliant blue, Congo red plate, spectrophotometry, and Cell Counting Kit-8 (CCK-8) assays were used to evaluate the effect of WWZE on the biofilm of V. parahaemolyticus. The results showed that WWZE could exert its dose-dependent potential to effectively inhibit the formation of V. parahaemolyticus biofilm and clear mature biofilm by significantly destroying the cell membrane integrity of V. parahaemolyticus, inhibiting the synthesis of intercellular polysaccharide adhesin (PIA), extracellular DNA secretion, and reducing the metabolic activity of biofilm. This study reported for the first time the favorable anti-biofilm effect of WWZE against V. parahaemolyticus, which provides a basis for deepening the application of WWZE in the preservation of aquatic products.

With the continuous expansion of industrial enterprises, a large amount of high-salt wastewater with complex components is produced. Direct discharge will cause great harm to the ecosystem and waste a large amount of potential salt resources. This paper summarizes the source, water quality characteristics, and environmental impact of high-salinity wastewater, and introduces the desalination and treatment technologies of high-salinity wastewater. The desalination technology of high-salinity wastewater mainly includes two processes: concentration and crystallization, obtaining concentrated solution through membrane concentration or thermal concentration and then carrying out crystallization treatment on the concentrated solution, thereby realizing the recovery of salt. The advanced treatment technologies of high-salinity wastewater were analyzed, including physicochemical treatment, biological treatment, and coupling treatment. Catalytic ozonation is one of the most widely used physicochemical technologies for the advanced treatment of high-salinity wastewater. Biological treatment processes operating in the presence of halotolerant bacteria show excellent performance at high salinity. High salinity has a negative impact on the performance of various physicochemical processes and biological treatment technologies. However, high salinity has little effect on the performance of a coupled system designed to treat high-salinity wastewater. In this review, the effect of salinity on the scaling and corrosion of equipment is also illustrated. It is suggested that the research direction of high-salinity wastewater should be to develop new membrane materials and catalysts, develop salt-tolerant microorganisms, explore high-efficiency and energy-saving physico–chemical–biochemical combination processes, improve the treatment efficiency of high-salinity organic wastewater, and reduce treatment costs.