Search

Clostridium perfringens (C. perfringens) is an important veterinary pathogen and a noteworthy threat to human and animal health. Recently, there has been a significant rise in the number of moose fatalities caused by this rare, endemic species in China. Currently, there is an increasing trend in conducting whole-genome analysis of C. perfringens strains originating from pigs and chickens, whereas fewer studies have been undertaken on Elaphurus davidianus-originating strains at the whole-genome level. Our laboratory has identified and isolated five C. perfringens type A from affected Elaphurus davidianus. The current study identified the most potent strain of C. perfringens, which originated from Elaphurus davidianus, and sequenced its genome to reveal virulence genes and pathogenicity. Our findings show that strain CX1-4 exhibits the highest levels of phospholipase activity, hemolytic activity, and mouse toxicity compared to the other four isolated C. perfringens type A strains. The chromosome sequence length of the CX1-4 strain was found to be 3,355,389 bp by complete genome sequencing. The current study unveils the genomic characteristics of C. perfringens type A originating from Elaphurus davidianus. It provides a core foundation for further investigation regarding the prevention and treatment of such infectious diseases in Elaphurus davidianus.

With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms often fail to recover fine details and do not adequately address the fundamental principles of the LDR imaging pipeline. To overcome these limitations, the Reversing Lossy UNet (RLUNet) has been proposed, aiming to effectively balance dynamic range expansion and recover overexposed areas through a deeper understanding of LDR image pipeline principles. The RLUNet model comprises the Reverse Lossy Network, which is designed according to the LDR–HDR framework and focuses on reconstructing HDR images by recovering overexposed regions, dequantizing, linearizing the mapping, and suppressing compression artifacts. This framework, grounded in the principles of the LDR imaging pipeline, is designed to reverse the operations involved in lossy image operations. Furthermore, the integration of the Texture Filling Module (TFM) block with the Recovery of Overexposed Regions (ROR) module in the RLUNet model enhances the visual performance and detail texture of the overexposed areas in the reconstructed HDR image. The experiments demonstrate that the proposed RLUNet model outperforms various state-of-the-art methods on different testsets.

China’s renewable energy sector is experiencing rapid growth, yet its inherent intermittency is creating significant challenges for balancing power supply and demand. Power-to-gas (PtG) technology, which converts surplus electricity into combustible gas, offers a promising solution. Salt caverns, due to their favorable geological properties, are among the best choices for large-scale underground energy storage in PtG systems. CO2 leakage along the interlayer and salt rock–interlayer interfaces is a key constraint on the CO2 tightness of subsurface salt caverns. This paper focuses on the critical issue of tightness within salt cavern CO2 storage, particularly in the Jintan region. A coupled hydro-mechanics mathematical model is developed to investigate CO2 transportation and leakage in bedded salt caverns, with key variables such as permeability range, pore pressure evolution, and permeability changes being analyzed. The results reveal that permeability plays a decisive role in determining the CO2 transportation rate and leakage extent within the salt rock layer. Notably, the CO2 transportation rate and influence range in the mudstone interlayer are significantly larger than those in the salt rock over the same period. However, with prolonged storage time, the CO2 transportation rate and pressure increase in both salt rock and mudstone interlayer exhibit a decreasing trend, eventually stabilizing as the CO2 pressure front reaches the boundary of the simulation domain. Furthermore, elevated operating pressure markedly expands the permeability range and results in higher cumulative leakage. For a specific salt cavern, the CO2 leakage range can reach up to 142 m, and the leakage volume can reach 522.5 tonnes with the increase in operating pressure during 35 years of operation. Therefore, the setting of operational pressure should fully consider the influence of permeability and mechanical strength of the salt rock and mudstone interlayer. These findings provide valuable insights into optimizing the sealing performance of salt cavern CO2 storage systems under varying conditions.

Gastrodiae Rhizoma (GE), a popular food in China, is stored and consumed after steaming, which can lead to the degradation of active substances and a decrease in its quality. Therefore, this study explored the potential application of ultra-high-pressure (UHP)-assisted hot air drying in improving the quality of GE. The results indicated that UHP pre-treatment could preserve the original cross-sectional color of GE and increase the degrees of freedom of water in GE samples. Compared with traditional steaming pre-treatment (18 h), UHP pre-treatment at 500 MPa significantly shorted the time (10 h) required for the GE samples to reach drying equilibrium. Meanwhile, the UHP-assisted hot air drying method (60 °C) could reduce the activity of β-D-glucosidase and avoid the degradation of active substances. Finally, UHP pre-treatment improved the neuroprotective activity in vivo. Overall, UHP-assisted hot air drying could improve the quality of GE samples. This study provides a simple method for improving the quality of GE samples and offers a reference for subsequent research on the influence of UHP on GE.

High temperatures and droughts are two natural disasters that cause forest fires. During climate change, the frequent occurrence of high temperatures, droughts, and their coupled effects significantly increase the forest fire risk. To reveal the seasonal and spatial differences in the coupled effects of high temperatures and droughts on forest fires, this study used the Copula method and proposed the compound extremely high-temperature and drought event index (CTDI). The results indicated that the study area was subject to frequent forest fires in spring (71.56%), and the burned areas were mainly located in forests (40.83%) and the transition zone between farmland and forests (36.91%). The probability of forest fires in summer increased with high temperatures and drought intensity, with high temperatures playing a dominant role. The highest forest fire hazard occurred in summer (>0.98). The probability of a forest fire occurring under extreme meteorological conditions in summer and fall was more than twice as high as that in the same zone under non-extreme conditions. Droughts play a significant role in the occurrence and spread of forest fires during fall. These results can provide decision-making support for forest fire warnings and fire fighting in the Northeast China forest zone.

Heterologous double-root grafting represents an effective strategy to mitigate challenges associated with continuous coffee cropping and reduce soil-borne diseases. However, its specific regulatory mechanism remains unclear. Therefore, a field experiment was conducted including six different grafting combinations for C. canephora cv. Robusta (Robusta) and Coffea Liberica (Liberica): Robusta scion with a homologous double root (R/RR), Liberica scion with a homologous double root (L/LL), Robusta scion with a heterologous double root (R/RL and L/RL), and Liberica scion with a heterologous double root (L/LR and R/LR); these combinations were conducted to clarify the effects of heterologous double-root grafting combinations on the root exudates and soil microbial diversity, structure, and function of Robusta and Liberica. The results demonstrated notable differences in root exudates, rhizosphere microbial structure, and function between Robusta and Liberica. Despite Liberica having lower diversity in its rhizosphere microbial communities and relatively higher levels of potential pathogenic bacteria, it showed stronger resistance to diseases. Roots of Robusta in heterologous double-root coffee seedlings significantly enhanced the secretion of resistance compounds, increased the relative abundance of potentially beneficial bacteria, and reduced the relative abundance of potential pathogenic fungi. This enhances the rhizosphere immunity of Robusta against soil-borne diseases. The results indicated that grafting onto Liberica roots can strengthen resistance mechanisms and enhance the rhizosphere immunity of Robusta, thereby mitigating challenges associated with continuous cropping.

The grinding process is one of the key factors affecting the quality of glutinous rice flour (GRF). As an emerging grinding method, semidry grinding aims to solve the problems of the high yield of wastewater in traditional wet grinding and the high content of damaged starch in dry grinding, in which the water content has a great influence on the quality of GRF. However, semidry grinding has not yet been formally put into production due to limitations such as the long time required to adjust the water content of rice grains. Therefore, this work was carried out to shorten the soaking time of glutinous rice (GR) by hot air pretreatment, and to conduct a systematic and in-depth study of the effect of water content on the quality of GRF, including water distribution, water hydration properties, thermal properties, rheological properties, and microstructure. The results showed that the GRF with higher water content had lower water solubility and higher enthalpy of pasting, which were due to the low content of damaged starch and the high degree of crystallization. The particle size of the GRF became smaller as the interaction between water and starch was enhanced and the GR was softened. In addition, the viscosity and elasticity of the GRF were also improved with an increase in water content. This work provides theoretical guidance for the improvement of semidry grinding to a certain extent.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector’s hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

Because of its low concentration and low energy in an organic Rankine cycle (ORC) system, more than 50% of low-grade thermal energy with a temperature below 300 °C is not taken seriously. The relationship between changeable working condition parameters and operating parameters is typically not taken into account in a model while studying ORC systems. It is insufficient to analyze the system performance changes solely on changes in the performance parameters of the heat exchangers. Furthermore, a model perspective of the system control solution is absent from the steady-state control of the system when the heat source varies. In this paper, we created a system model for the application scenario of a 100–200 kW ORC system with brazed plate heat exchangers in order to address the aforementioned issues using MATLAB R2016b. Additionally, a sensitivity analysis of the system was conducted based on heat exchangers of performance variations. In addition, a modeling computation and adjustment scheme were put forth to guarantee that the system could continue to produce steady power production even when the temperature of the heat source fluctuated. Preliminary results showed that the system’s internal parameters included its evaporation pressure and condensation temperature. An increase in evaporation pressure will reduce the mass flow rate and heat flux of an evaporator, thus reducing the heat transfer coefficient. The two-phase region of the evaporator accounted for the highest proportion, of 70.22%, and continued to increase to 75.83%, followed by the supercooling region, and gradually decreasing. The utilization rate of the evaporator decreased from 74.85% to 38.32%. The system output power increased first and then decreased, with a maximum value of 153.11 kW. The system efficiency increased from 5.74% to 11.04%. The increase in condensation temperature increased the proportion of the two-phase region and the superheating region, and the mass flow rate did not change much. The increase in heat flux increased the heat transfer coefficient from 1721.31 W/(m2·K) to 2374.77 W/(m2·K), and the utilization rate of the evaporator decreased from 70.56% to 51.91%. The aforementioned change rules were used in the building of the steady-state regulation model to balance out the changes in the generating power as the heat source temperature increased. The model output was cross-checked and validated against pertinent experimental test literature data. The results of this research can serve as a valid and useful guide for ORC system design and practical applications.

Curcumin is widely recognized for its diverse antitumor properties, ranging from breast cancer to many other types of cancers. However, its role in the tumor microenvironment remains to be elucidated. In this study, we established a 3D tumor spheroids model that can simulate the growth environment of tumor cells and visualized the antitumor metabolic alteration caused by curcumin using mass spectrometry imaging technology. Our results showed that curcumin not only exerts a profound impact on the growth and proliferation of breast cancer cells but in situ multivariate statistical analysis also reveals the significant effect on the overall metabolic profile of tumor spheroids. Meanwhile, our visualization map characterized curcumin metabolic processes of reduction and glucuronidation in tumor spheroids. More importantly, abnormal metabolic pathways related to lipid metabolism and polyamine metabolism were also remodeled at the metabolite and gene levels after curcumin intervention. These insights deepen our comprehension of the regulatory mechanism of curcumin on the tumor metabolic network, furnishing powerful references for antitumor treatment.

High-entropy alloys (HEAs) have emerged as a novel class of materials with exceptional mechanical and corrosion properties, offering promising applications in various engineering fields. However, optimizing their performance through advanced manufacturing techniques, like laser cladding, remains an area of active research. This study investigated the effects of laser energy density on the mechanical and electrochemical properties of CoCrFeMnNi HEA coatings applied to Q235 substrates. Utilizing X-ray diffraction (XRD), this study confirmed the formation of a single-phase face-centered cubic (FCC) structure in all coatings. The hardness of the coatings peaked at 210 HV with a laser energy density of 50 J/mm2. Friction and wear tests highlighted that a coating applied at 60 J/mm2 exhibited the lowest wear rate, primarily due to adhesive and oxidative wear mechanisms, while the 55 J/mm2 coating showed increased hardness but higher abrasive wear. Electrochemical testing revealed superior corrosion resistance for the 60 J/mm2 coating, with a slow corrosion rate and minimal passivation tendency in contrast to the 55 J/mm2 coating. The comprehensive evaluation indicates that the HEA coating with an energy density of 60 J/mm2 exhibits exceptional wear and corrosion resistance.

The rapidly growing field of cannabinoid research is gaining recognition for its impact in neuropsychopharmacology and mood regulation. However, prenyltransferase (NphB) (a key enzyme in cannabinoid precursor synthesis) still needs significant improvement in order to be usable in large-scale industrial applications due to low activity and limited product range. By rational design and high-throughput screening, NphB’s catalytic efficiency and product diversity have been markedly enhanced, enabling direct production of a range of cannabinoids, without the need for traditional enzymatic conversions, thus broadening the production scope of cannabinoids, including cannabigerol (CBG), cannabigerolic acid (CBGA), cannabigerovarin (CBGV), and cannabigerovarinic acid (CBGVA). Notably, the W3 mutant achieved a 10.6-fold increase in CBG yield and exhibited a 10.3- and 20.8-fold enhancement in catalytic efficiency for CBGA and CBGV production, respectively. The W4 mutant also displayed an 9.3-fold increase in CBGVA activity. Molecular dynamics simulations revealed that strategic reconfiguration of the active site’s hydrogen bonding network, disulfide bond formation, and enhanced hydrophobic interactions are pivotal for the improved synthetic efficiency of these NphB mutants. Our findings advance the understanding of enzyme optimization for cannabinoid synthesis and lay a foundation for the industrial-scale production of these valuable compounds.

Green process innovation is an important strategy in the high-quality development of enterprises. Digital technology is becoming a key factor in helping businesses address environmental issues and contributes to their green process innovation and sustainable growth. Nevertheless, there is a lack of studies on how particular digital technology categories affect corporate green process innovation. Artificial intelligence (AI) is an important part of digitalization as it can provide new technical means and guidance for enterprise’s innovation of green processes. This study aims to fills this research gap by revealing the logical relationship between digital technology and the green development of enterprises. Using China’s A-share-listed companies as the research object from 2013 to 2022, this study employed a two-way fixed-effects model and investigated the impact of artificial intelligence (AI) on corporate green process innovation and the moderating effect of multidimensional intellectual capital. The results revealed that AI positively impacts corporate green process innovation. Human capital, structural capital, employed capital, and relational capital strengthen this positive effect. Robustness tests validated these conclusions. This study expands the literature on digital technology and corporate green innovation and provides a reference for enterprises to implement green practices using digital technology.

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.

Rotor-type cross-media vehicles always induce considerably complex mixed air–water flows when approaching the water surface, resulting in relative thrust loss and structural damage on rotor. The interactions between a water surface and rotor wake bring potential risks to the cross-media process, which is known as the near-water effect of the rotor. In this paper, experimental investigations are used to explore the fluid dynamics of the near-water effect of the rotor. Qualitative droplet observation was carried out on the 0.25 m and 0.56 m diameter commercial rotor blades and the 0.07 m diameter ducted fan near the water surface first to gain a qualitative understanding of droplet characteristics. The results show that the rotor wake caused water surface deformation, droplet tearing off, splashing, and entrainment into the rotor disk. The depression formed by the rotor downwash flow impacting the water surface is named as three modes: dimpling, splashing, and penetrating, and the correlation between the depression modes and the aerodynamic characteristics of the rotor is primary analyzed. The flow mechanisms of dimpling mode were studied using the particle image velocimetry (PIV) technique. The results showed that the cavity and liquid crown obviously alter the flow direction of water surface jets, but not all rotors near water enter the vortex ring state. Two splashing mechanisms were revealed, including the direct ejection of droplets at the rim of depression and the tearing of liquid crown by the water surface jets. The blade tip vortex in the surface jet is a potential cause of entrainment into the rotor disk and secondary breakup of the droplet.

Our target USH reservoir in the D oilfield is characterized by “inverse rhythm” deposition with the noticeable features of “high porosity and low permeability”. The reservoir has been developed with waterflooding using horizontal wells. Due to the strong heterogeneity of the reservoir, water channeling is severe, and the water cut has reached 79%. Considering the high temperature and high salinity reservoir conditions, polymer microspheres (PMs) were selected to realize conformance control. In this study, characterization of the polymer microsphere suspension was achieved via morphology, size distribution, and viscosity measurement. Furthermore, a multi-scale visualization study of the reservoir development process, including waterflooding, polymer microsphere flooding, and subsequent waterflooding, was conducted using macro-scale coreflooding and calcite-etched micromodels. It was revealed that the polymer microspheres could swell in the high salinity brine (170,000 ppm) by 2.7 times if aged for 7 days, accompanied by a viscosity increase. This feature is beneficial for the injection at the wellbore while swelled to work as a profile control agent in the deep formation. The macro-scale coreflood with a 30 cm × 30 cm × 4.5 cm layer model with 108 electrodes installed enabled the oil distribution visualization from different perpendicular cross sections. In this way, the in situ conformance control ability of the polymer microsphere was revealed both qualitatively and quantitatively. Furthermore, building on the calcite-etched visible micro-model, the pore-scale variation of the residual oil when subjected to waterflooding, polymer microsphere waterflooding, and subsequent waterflooding was collected, which revealed the oil displacement efficiency increase by polymer microspheres directly. The pilot test in the field also proves the feasibility of conformance control by the polymer microspheres, i.e., more than 40,000 bbls of oil increase was observed in the produces, accompanied by an obvious water reduction.

To meet the demands for high-temperature performance and lightweight materials in aerospace engineering, the Au-Ni solder is often utilized for joining dissimilar materials, such as Ti3Al-based alloys and Ni-based high-temperature alloys. However, the interaction between Ti and Ni can lead to the formation of brittle phases, like Ti2Ni, TiNi, and TiNi3, which diminish the mechanical properties of the joint and increase the risk of crack formation during the welding process. Cu doping has been shown to enhance the mechanical properties and high-temperature stability of the Au-Ni brazed joint’s central area. Due to the difficulty in accurately controlling the solid solution content of Cu in the Au-Ni alloy, along with the high cost of Au, traditional experimental trial-and-error methods are insufficient for the development of Au-based solders. In this study, first principles calculations based on density functional theory were employed to analyze the effect of Cu content on the stability of the Au-2.0Ni-xCu (x = 0, 0.25, 0.5, 0.75, 1.0, 1.25 wt%) alloy phase structure. The thermal properties of the alloy were determined using Gibbs software fitting. The results indicate that the Au-2.0Ni-0.25Cu alloy exhibits the highest plastic toughness (B/G = 5.601, ν = 0.416, Cauchy pressure = 73.676 GPa) and a hardness of 1.17 GPa, which is 80% higher than that of Au-2.0Ni. This alloy balances excellent strength and plastic toughness, meeting the mechanical performance requirements of brazed joints. The constant pressure specific heat capacity (Cp) of the Au-2.0Ni-xCu alloy is higher than that of Au-2.0Ni and increases with Cu content. At 1000 K, the Cp of the Au-2.0Ni-0.25Cu alloy is 35.606 J·mol−1·K−1, which is 5.88% higher than that of Au-2.0Ni. The higher Cp contributes to enhanced high-temperature stability. Moreover, the linear expansion coefficient (CTE) of the Au-2.0Ni-0.25Cu alloy at 1000 K is 8.76 × 10−5·K−1, only 0.68% higher than Au-2.0Ni. The lower CTE helps to reduce the risk of solder damage caused by thermal stress. Therefore, the Au-2.0Ni-0.25Cu alloy is more suitable for brazing applications in high-temperature environments due to its excellent mechanical properties and thermal stability. This study provides a theoretical basis for the performance optimization and engineering application of the Au-2.0Ni-xCu alloy as a gold-based solder.

Numerical simulations are performed for flows over a circular cylinder at a Reynolds number ranging from 150 to 5000, and Mach number of 0.2, to assess the predictive capability of URANS and hybrid RANS/LES for acoustic waves generation and propagation. Complementary direct numerical simulations (DNS) are performed to generate validation datasets and to provide more insight into the problem. DNS predictions show that noise induced by the vortex shedding is radiated primarily at a 90-degree angle with respect to the wake direction and dictates the dominant frequency of the sound pressure waves. Turbulence dominates the noise in the near-field wake, resulting in a broadband pressure spectrum. URANS both underpredicts and overpredicts the noise levels in the wake region and in the direction normal to the freestream flow, respectively, which is attributed to its inability to accurately predict the turbulent kinetic energy content. Hybrid RANS/LES computations, using a second-order low-dissipation optimization-based gradient reconstruction scheme on a grid that is three times coarser than the DNS, provide an accurate prediction of the far-field noise levels, except in the wake region, where they are overpredictive.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

To address the limitations of the Deep Deterministic Policy Gradient (DDPG) in robot path planning, we propose an improved DDPG method that integrates kinematic analysis and D* algorithm, termed D*-KDDPG. Firstly, the current work promotes the reward function of DDPG to account for the robot’s kinematic characteristics and environment perception ability. Secondly, informed by the global path information provided by the D* algorithm, DDPG successfully avoids getting trapped in local optima within complex environments. Finally, a comprehensive set of simulation experiments is carried out to investigate the effectiveness of D*-KDDPG within various environments. Simulation results indicate that D*-KDDPG completes strategy learning within only 26.7% of the training steps required by the original DDPG, retrieving enhanced navigation performance and promoting safety. D*-KDDPG outperforms D*-DWA with better obstacle avoidance performance in dynamic environments. Despite a 1.8% longer path, D*-KDDPG reduces navigation time by 16.2%, increases safety distance by 72.1%, and produces smoother paths.

Hawthorn (Crataegus pinnatifida Bunge) has been highlighted as an excellent source of a variety of bioactive polymers, which has attracted increasing research interest. Pectin, as a kind of soluble dietary fiber in hawthorn, is mainly extracted by hot water extraction and ultrasonic or enzymatic hydrolysis and is then extensively used in food, pharmaceutical, and nutraceutical industries. Numerous studies have shown that hawthorn pectin and its derived oligosaccharides exhibit a wide range of biological activities, such as antioxidant activity, hypolipidemic and cholesterol-reducing effects, antimicrobial activity, and intestinal function modulatory activity. As discovered, the bioactivities of hawthorn pectin and its derived oligosaccharides were mainly contributed by structural features and chemical compositions and were highly associated with the extraction methods. Additionally, hawthorn pectin is a potential resource for the development of emulsifiers and gelling agents, food packaging films, novel foods, and traditional medicines. This review provides a comprehensive summary of current research for readers on the extraction techniques, functional characteristics, structure–activity relationship, and applications in order to provide ideas and references for the investigation and utilization of hawthorn pectin and its derived oligosaccharides. Further research and development efforts are imperative to fully explore and harness the potential of hawthorn pectin-derived oligosaccharides in the food and medicine fields.

Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya’an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models’ resilience in an open-source landslide database. The results show that in the Ya’an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.

The emerging upward shield method (USM) for constructing vertical shafts has been used in various projects, including the Midosuji utility tunnel in Japan. A scaled-down model testing system, featuring a geometric similarity ratio of 1:30, was developed specifically for studying the USM. This system incorporates sand inflow control, propulsion control, data acquisition, and water level control. It facilitates detailed observation and recording of parameters such as vertical displacement of surface soil layers, support force at the excavation face, and earth pressure within the model box. Consequently, it enables investigation into the excavation face instability process, modes, and the formation and evolution of the soil arch zone above the excavation face during upward shield tunneling. Additionally, through the application of particle image velocimetry (PIV) technology and GeoPIV-RG software v1.1, quantitative analysis of soil displacement fields during excavation face instability is conducted, capturing microscopic displacements and deformations of soil planes. This approach more accurately elucidates the accuracy of understanding the dynamic response of soil. Pre-test research using the model testing system explores the variation patterns of excavation face load displacement, vertical earth pressure within the failure zone, surface displacement, and internal soil displacement during the instability process. Analysis reveals that excavation face load variation typically progresses through three stages: rapid growth, slow growth, and descent. Moreover, vertical earth pressure shifts upward in tandem with excavation face displacement, while overall surface displacement initially shows slight settlement followed by accelerated uplift.

In response to the issue of weak laser connection strength between 304 stainless steel and the carbon fiber-reinforced polymer PCCF30, this study proposes the addition of a polyethylene terephthalate (PET) intermediate layer between 304 stainless steel and PCCF30, along with surface texture pretreatment of the stainless steel surface, to enhance the joint connection strength. First, three types of surface textures (vertical, wavy, and vertical plus wave) were investigated for their effect on joint quality. Once the optimal texture type was determined, the effect of texture scanning repetition on the surface morphology and groove cross-sectional geometry of 304 stainless steel was examined. The joint strengths achieved by adding intermediate layers and employing different surface texture treatment methods were compared, followed by a comparison of the joint strengths obtained from different pretreatment methods. Finally, the connection mechanism of the joint was analyzed. The results indicated that the maximum joint strength achieved through the vertical and wavy texture design was 4.39 MPa, which is 3.3 times greater than that achieved without reinforcement. Moreover, the maximum joint strength achieved with the addition of a PET intermediate layer and surface texture composite treatment was 11.85 MPa, which is approximately nine times greater than that achieved via direct connection. The inclusion of a PET intermediate layer enhances the fluidity of the molten polymer at the joint, facilitating better filling of the grooves with surface texture. This strengthens the mechanical anchoring effect of the joint and contributes to the improvement in the joint’s connection strength. An XPS analysis revealed that after multiple etchings of the joint interface with an added intermediate layer, the Cr in the stainless steel chemically bonded with the PET, resulting in the formation of Cr–O–C chemical bonds, which is advantageous for enhancing joint quality.

The ecological stoichiometric characteristics of soil elements have greatly enhanced our understanding of the circulation of soil nutrients. However, there is limited knowledge regarding the alteration of carbon, nitrogen, and phosphorus stoichiometric ratios in deep soil after afforestation. To examine the variations in stoichiometric ratios of soil elements with different vegetation types, restoration times, and soil depths, we collected soil samples from grassland, Caragana korshinskii shrubland, and Picea asperata forestland at different stand ages (10a, 25a, and 40a) in Xining City, which is located on the Loess Plateau. Our results showed that, at 25a, the carbon-to-nitrogen (C:N) and carbon-to-phosphorus (C:P) ratios were significantly higher in the grassland soil than under other vegetation types, whereas the nitrogen-to-phosphorus (N:P) ratio had no significant difference among the three vegetation types. At 40a, the ratios of soil C:N, C:P, and N:P in the shrubland were the highest. With the increasing of the restoration time, the ratios of soil C:N, C:P, and N:P in grassland with 25a became higher than for 40a or 10a. The ratios in the shrubland were highest at 40a, followed by 25a and then 10a, while the ratios in the forestland showed no significant difference. At 40a, the soil C:N, C:P, and N:P ratios of shrubland were highest at the soil depth of 40–100 cm. The soil C:N, C:P, and N:P ratios showed positive correlations with soil ammonium nitrogen and nitrate nitrogen, and the soil N:P ratios showed a negative correlation with soil available phosphorus. Plant diversity significantly influenced the soil stoichiometric ratio of the upper soil layer. In the upper soil layer (0–40 cm), species richness showed a positive correlation with soil C:N, C:P, and N:P ratios, and the Margalef index exhibited a positive correlation with soil C:N and C:P ratios. The results of this study indicate that the stoichiometric ratio and nutrient availability of Caragana korshinskii shrubland were the highest over time. Therefore, these findings can be served as a valuable reference for local revegetation and ecological restoration.

Sixteen novel antioxidant peptides from Asian swamp eel (ASE) were identified in previous studies. However, their chemical and cellular antioxidant mechanisms remain unclear. Molecular docking of these peptides with ABTS and DPPH radicals revealed the critical role of hydrogen bonding and Pi–Pi stacking hydrophobic interactions between hydrophobic amino acid residues and free radicals. Residues, such as tryptophan, proline, leucine, and valine, played significant roles in these interactions. All these peptides exhibited notable erythrocyte morphoprotective effects in a model of AAPH-induced oxidative damage of human erythrocytes. Erythrocyte hemolysis was reduced primarily through the modulation of both non-enzymatic (GSH/GSSG) and enzymatic antioxidant systems (SOD, CAT, and GSH-Px) by these peptides. A decrease in levels of MDA, LDH release, and hemoglobin oxidation was observed. Among the peptides, VLYPW demonstrated superior chemical and cellular antioxidant activities, which may be attributed to its higher levels of tyrosine and tryptophan, as well as to its increased hydrophobic amino acid content.

To refine the grain size and improve the mechanical properties of ultrahigh-strength aluminum alloy (Al-10Zn-1.9Mg-1.6Cu-0.12Zr), the Al-Ti-B-Er grain refiner was prepared by the melt reaction method using the aluminum melt and Al + Ti + B precursor. The results exhibit that the Al-Ti-B-Er grain refiner is mainly composed of a block TiAl3 phase, and loose agglomerated nano-sized TiB2 and Al3Er phases. The microstructure of ultrahigh-strength aluminum is significantly affected by the Al-Ti-B-Er refiner, which changes from dendrite to equiaxial grain with increasing Al-Ti-B-Er content, and the size of the eutectic phase is significantly refined. The high-efficiency refinement of Al-Ti-B-Er is due to Er promoting the uniform distribution of TiAl3 particles and the formation of loose agglomerated nano-sized TiB2 particles. The optimal addition content of Al-Ti-B-Er into ultrahigh-strength aluminum alloys is 1 wt%, whose grain size is approximately 40 µm. Additionally, the strength and ductility of ultrahigh-strength aluminum alloys are simultaneously improved by adding 1wt% Al-Ti-B-Er after the T6 treatment, reaching 756 MPa and 20%, respectively. This enhancement in strength and ductility is mainly attributed to grain refinement and the eutectic phase refinement.

Ore is a crucial component in the process of industrialization, and its crushing is a practice that is inextricably linked to our society. This study aims to simulate the crushing process of minerals in a comminution device using the Discrete Element Method (DEM) by characterizing the bonding parameters of mineral particles. Utilizing the EDEM software (2018) for discrete element simulations, the study investigated the influence of bonding parameters on the compressive strength and other performance indicators of the particle bonding model. The study was executed through the application of a Box–Behnken Design (BBD) and Response Surface Methodology (RSM), which facilitated the construction of a second-order response surface regression model. The optimal values for normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress were meticulously determined. The subsequent simulation experiments strongly verify the feasibility of the proposed characterization method for key parameters.

The accurate evaluation of aerodynamic characteristics is a prerequisite and foundation for the design of high-performance aerodynamic shapes, navigation guidance, and strength of projectiles. The nonlinearity of aerodynamic calculations for a projectile is high, and the modeling and simulation are difficult, especially under the high-angle of attack flight conditions. Small variations in flight conditions, and structural parameters, etc., may cause large deviations in aerodynamic responses. Taking a small cylindrical projectile as an example, and to realize its attitude control, it is necessary to conduct aerodynamic characteristics analysis on it and analyze the main influencing factors of its aerodynamic characteristics parameters. In this paper, the finite volume method is used to solve the three-dimensional unsteady N-S equation, combined with the SST k-ω turbulence model, the overlapping grid technology, and the forced pitching vibration method, and the aerodynamic characteristics analysis model of the projectile is established, which realizes the accurate simulation of the surrounding flow field, aerodynamic coefficients, and dynamic derivative of the projectile under different flight conditions. On this basis, the Sobol global sensitivity analysis method based on the augmented radial basis function surrogate model of aerodynamics characteristics and Latin hypercube sampling is used to efficiently analyze and obtain the main influence parameters of cylindrical projectile aerodynamic characteristics. This paper provides a basic theory and fast algorithm for subsequent engineering system design, which has important theoretical and engineering value.

This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.

The rational design of magnetic carbon composites, encompassing both their composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave-absorbing materials (EAMs). In this study, FeCo@CM composites were efficiently fabricated through an advanced microwave plasma-assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of −58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs.

Background: Eurotium cristatum (E. cristatum) is the probiotic fungus in Fu-brick tea, with which fermentation brings a unique flavor and taste and health-promoting effects. Tartary buckwheat leaves are rich in functional active substances such as flavonoids and phenolic compounds, yet are not effectively utilized. Methods: Tartary buckwheat leaves were processed into raw green tea first and subsequently fermented with E. cristatum to develop a novel fermented leaf tea. The tea quality was evaluated by the aspects of the sensory scores by E-tongue, the volatile compounds by HS-SPME-GC-MS, the non-volatile profile by biochemical and UPLC-MS/MS methods and the antioxidant activity by the colorimetric assay. Results: Fermented leaf tea displayed a golden yellow color, a unique “flower” aroma and a dark-tea taste, with an improved sensory acceptability. Fermentation raised the content of volatile heterocyclic and aromatic compounds, alkenes and other aromatic components, which produced a unique floral flavor. The proportion of sour, bitter and astringency accounting non-volatile compounds such as phenolic acids and amino acids decreased, while the proportion of umami and sweet accounting substances such as responsible amino acids increased. Fermented leaf tea displayed a relative stronger total antioxidant activity against ABTS. Conclusion: E. cristatum fermentation exerted positive effects on Tartary buckwheat leaf tea quality.

For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the pressure-resistant cylindrical shell arise from the need to ensure that the detector packaging structure can withstand the immense water pressure at tens of thousands of meters in the underwater environment, while simultaneously reducing the detector packaging structure’s self-weight. This article examines the detection system’s deep-sea pressure-resistant cylindrical shell. To address the issue of the pressure-resistant shell’s insufficient ability to counteract the overall instability caused by the inability to form unstable half-waves in the radial direction when the ring rib pressure-resistant shell experiences it, a design method for the ribs inside the unequal-stiffness pressure-resistant cylindrical shell is suggested. The shell’s instability pressure increases by 9.65 MPa following the stiffness adjustment. Simultaneously, in order to attain even more lightweight optimization, the optimal inner rib section was obtained by applying the orthogonal topology optimization method, which also reduced the weight by 106.8 g and effectively improved the compression stability of the high-pressure cylindrical shell structure. Based on this, key optimization variables were found by performing sensitivity analysis on the cylindrical shell structure’s parameters. Then, with lightweighting as the primary objective, the high-pressure-resistant cylindrical shell’s optimal structural parameters were found using a multi-objective optimization process using the second-generation fast non-dominated genetic algorithm (NSGA-2). This resulted in a weight reduction of 1.2492 kg, or 17.26% of the original pressure-resistant shell. This has led to the development of a lightweight, highly pressure-resistant method for packaging marine exploration equipment structures.

Seagrass serves as a quintessential reservoir for obligate marine Lulworthiaceae fungi. Our current knowledge of the mycological diversity associated with seagrass in Hong Kong remains poor. We analyzed the diversity of fungi associated with the most widely distributed seagrass species in Hong Kong Halophila ovalis (Hydrocharitaceae), using a combination of culture-based methods and high-throughput amplicon sequencing. Halophilomyces hongkongensis, a novel fungal species in a newly proposed genus within the Lulworthiaceae family, was isolated from H. ovalis roots and rhizomes. The novel fungus showed distinct morphological characteristics, while both combined 18S-28S and internal transcribed spacer (ITS) phylogenetic trees based on maximum likelihood and Bayesian methods supported its discrimination from other existing Lulworthiaceae members. The ITS2 region in the Illumina sequencing results of multiple H. ovalis compartments, water, and adjacent non-seagrass sediments revealed continuous recruitment of H. hongkongensis by H. ovalis throughout the year despite dramatically fluctuating environmental conditions, with remarkably high proportions of this taxon found in root and rhizome internal tissues, possibly indicating a strong and specialized relationship established between the Lulworthiaceae fungal partner and its seagrass host. The inhibitory abilities exhibited by H. hongkongensis against Staphylococcus aureus SA29213 and ATCC 43300 (methicillin-resistant) may imply its capacity in producing (novel) antibacterial compounds. The discovery of H. hongkongensis as the first novel Lulworthiaceae taxon in Hong Kong, along with its distributional pattern in the seagrass meadow, provides valuable insights into the systematics and ecology of this strictly marine fungal family.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

Each application of neurostimulators requires unique stimulation parameter specifications to achieve effective stimulation. Balancing the current magnitude with stimulation resolution, waveform, size, and channel count is challenging, leading to a loss of generalizability across broad neural interfaces. To address this, this paper proposes a highly scalable, programmable neurostimulator with a System-on-Chip (SOC) capable of 32 channels of independent stimulation. The compliance voltage reaches up to ±22.5 V. A pair of 8-bit current-mode DACs support independent waveforms for source and sink operations and feature a user-selectable dual range for low-current intraparenchymal microstimulation with a resolution of 4.31 μA/bit, as well as high current stimulation for spinal cord and DBS applications with a resolution of 48.00 μA/bit, achieving a wide stimulation range of 12.24 mA while maintaining high-resolution biological stimulation. A dedicated communication protocol enables full programmable control of stimulation waveforms, effectively improving the range of stimulation parameters. In vivo electrophysiological experiments successfully validate the functionality of the proposed stimulator. This flexible stimulator architecture aims to enhance its generality across a wide range of neural interfaces and will provide more diverse and refined stimulation strategies.

To investigate the mechanical properties and energy evolution laws of rocks under true triaxial unloading conditions, a study was conducted using a true triaxial rock testing system on three different types of rocks: coal, sandy mudstone, and siltstone. The study examined the mechanical behavior, failure patterns, and fractal dimensions of these rocks under true triaxial unloading conditions. The tests revealed significant variations in stress–strain curves and failure patterns among the different rock types. Observation indicated that rocks with lower peak strength exhibited higher fractal dimensions and increased fragmentation upon failure. Subsequently, based on the experimental data of siltstone, the impact of the unloading rate and particle size distribution on the energy evolution under true triaxial single-sided unloading paths was further investigated using the three-dimensional particle flow software PFC3D 6.0, revealing the micro-mechanisms of rock energy evolution. The study revealed that when the initial stress unloading level was low, the total energy and strain energy at the peak strength exhibited a strong linear relationship with the unloading rate. Before the stress peak, the dissipative energy was mainly composed of frictional energy. After the stress peak, the dissipative energy consisted of frictional energy, damping energy, and kinetic energy. The heterogeneity of rock significantly affected the distribution of dissipative energy, with an increase in rock heterogeneity leading to a decrease in frictional energy and an increase in kinetic energy.

Deep geothermal energy is a sustainable and renewable spacing heating source. Although many studies have discussed the design optimization of deep borehole systems, few have accomplished optimization and in-depth analysis of system operation control. In this study, an analytical model of the U-type deep borehole heat exchanger is proposed, and the average relative error between the simulated outlet temperatures and experimental data is −3.2%. Then, this paper presents an integrated model for the operation optimization study of the U-type deep-borehole ground source heat pump system. The optimal control of flow rate is adopted to match the variation in heating load. Compared with the constant-flow rate (110 m3/h) operation mode, the variable flow rate method reduces the power consumption of the heat pump and circulating pump by 22.1%, from 288,423 kW·h to 224,592 kW·h, during 2112 h of operation. In addition, the system has a larger RHS and COP when the thermal conductivity of the backfill material increases. When the borehole depth increases by 200 m from 2300 m, the energy consumption of the circulating pump will drop from 85,844 kW·h to 56,548 kW·h. The COP of the heat pump unit will decrease approximately linearly as the heating load increases, and the total power consumption will increase accordingly. This work can provide guidance for the design and optimization of U-shaped GSHP systems.

Currently, China has entered the development stage of a “low birth rate” demographically. There is a huge contradiction between the unbalanced and inadequate distribution of preschool education facilities (PEFs) and the people’s expectations for “full and good education for children”. This study took the Lingui New District, a typical new urban area in West China as the research area, and through the introduction of POI big data and GIS analysis methods, supplemented by GeoDA Bivariate Moran index analyses, established a kindergarten spatial database. The study found that preschool education facilities have problems such as insufficient quantity, uneven quality, low service coverage, poor accessibility, etc. Therefore, it is suggested to increase the proportion of public affordable preschool education facilities including kindergartens and nurseries, optimize the spatial distribution of preschool education, and improve the accessibility of preschool services to promote affordable, safe, and high-quality development of preschool education and to provide reference suggestions for the revision of relevant standards and the adjustment of the layout of preschool education in undeveloped regions of China.

Landslide susceptibility prediction usually involves the comprehensive analysis of terrain and other factors that may be distributed with spatial patterns. Without considering the spatial correlation and mutual influence between pixels, conventional prediction methods often focus only on information from individual pixels. To address this issue, the present study proposes a new strategy for neighboring pixel collaboration based on the Unified Perceptual Parsing Network (UPerNet), the Vision Transformer (ViT), and Vision Graph Neural Networks (ViG). This strategy efficiently utilizes the strengths of deep learning in feature extraction, sequence modeling, and graph data processing. By considering the information from neighboring pixels, this strategy can more accurately identify susceptible areas and reduce misidentification and omissions. The experimental results suggest that the proposed strategy can predict landslide susceptibility zoning more accurately. These predictions can identify flat areas such as rivers and distinguish between areas with high and very high landslide susceptibility. Such refined zoning outcomes are significant for landslide prevention and mitigation and can help decision-makers formulate targeted response measures.

Soil desertification is an important challenge in global soil management, and effectively and stably restoring soil function is an urgent problem. Using synthetic microbial communities (SynComs) is a burgeoning microbial strategy aimed at enhancing soil nutrients through functional synergies among diverse microorganisms; nevertheless, their effectiveness in restoring desertified soils remains unknown. In this study, we conducted a two-year field experiment using a SynCom constructed by in situ probiotic bacteria and set up control, chemical fertilizer, and combined SynCom–chemical fertilizer (combined fertilizer) treatments to investigate the linkage between microbial communities and soil multifunctionality in the soil surface layer (0–10 cm). Both the bacterial and fungal communities differed the most under the combined fertilizer treatment compared to the control. The bacterial communities differed more under treatments of the SynCom than the chemical fertilizer, while the fungal communities differed more under the chemical fertilizer treatment than the SynCom treatment. Regarding soil function, the SynCom strengthened the correlation between enzyme activities and both bacterial communities and functional properties. pH and available potassium were the main influencing factors under the chemical fertilizer and combined fertilizer treatments. The beta-diversity of the bacterial communities was significantly correlated with soil multifunctionality. Random forest analyses showed that the SynCom significantly enhanced the bacterial communities, driving soil multifunctionality, and that some potential microbial taxa drove multiple nutrient cycles simultaneously. In summary, the SynCom effectively increased the abundance of most carbon, nitrogen, and phosphorus functional genes as well as soil enzyme activities. The bacterial community composition contributed significantly to soil multifunctionality. Hence, the development of novel microbial agents holds significant potential for improving soil functionality and managing desertification.

The mathematical model of a flexible multistate switch (FMSS) exhibits nonlinear and strong coupling characteristics, whereas traditional power decoupling control makes it difficult to completely decouple the output power. The traditional proportional–integral control parameters are difficult to adjust, and their robustness and dynamic performance are poor, which affects the stability of the voltage of the power distribution network and feeder power. To address these problems, this study first converted the original system into a linear system via coordinate transformation using feedback-accurate linearization to decouple active and reactive currents. Thereafter, a super-twisting sliding mode control (ST-SMC) algorithm was introduced, and an adaptive quasi-super-twisting sliding mode control (AQST-SMC) algorithm comprising the quasi-sliding mode function and adaptive proportional term was proposed. An FMSS double closed-loop controller was designed to achieve improved vibration suppression and convergence speed. A three-port FMSS simulation model was developed using MATLAB/Simulink, and the simulation results show that the proposed control strategy enhances the robustness and dynamic performance of the system.

Developing clinically meaningful nanomedicines for cancer therapy requires the drugs to be effective, safe, simple, cheap, and easy to store. In the present work, we report that a simple cationic Fe(III)-rich salt of [FeIIICl(TMPPH2)][FeIIICl4]2 (Fe-TMPP) exhibits a superior anticancer performance on a broad spectrum of cancer cell lines, including breast, colorectal cancer, liver, pancreatic, prostate, and gastric cancers, with half maximal inhibitory concentration (IC50) values in the range of 0.098–3.97 μM (0.066–2.68 μg mL−1), comparable to the best-reported medicines. Fe-TMPP can form stand-alone nanoparticles in water without the need for extra surface modification or organic-solvent-assisted antisolvent precipitation. Critically, Fe-TMPP is TME-responsive (TME = tumor microenvironment), and can only elicit its function in the TME with overexpressed H2O2, converting H2O2 to the cytotoxic •OH to oxidize the phospholipid of the cancer cell membrane, causing ferroptosis, a programmed cell death process of cancer cells.

Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional theoretical calculations and simulation analysis methods such as the large number of calculations, complex models, and large errors. Firstly, a mathematical prediction model is established according to the bearing temperature-rise law; then, a bearing steady-state temperature detection device is designed; and finally, the prediction model parameters are solved according to the experimental results, and experimental verification is carried out. It is shown that the prediction model has high accuracy under different load and speed conditions, and the error between the predicted steady-state temperature and the tested steady-state temperature is less than 0.7 °C. This prediction method reduces the single test time of the same speed to 60 min, which greatly improves the efficiency of the temperature detection test. The steady-state temperature model has important theoretical significance in guiding the study of the limiting speed of bearings.

As the prime candidate for plasma-facing materials (PFM), the response of tungsten (W) to thermal shock loads is an important research topic for future fusion devices. Under heat loads, the surface of tungsten plasma-facing materials (W-PFM) can experience thermal damage, including brittle cracking and fatigue cracks. Therefore, exploring solutions for thermal damage of W-PFM remains one of the current research focuses. We propose a novel approach to mitigate thermal radiation damage in PFM, namely, the stacked structure W-PFM. The surface thermal stress distribution of the stacked structure W-PFM under heat loads was simulated and analyzed by the finite element method. As the foil thickness decreases, both the peak thermal stresses in the normal direction (ND) and rolling direction (RD) decrease. When the thickness decreases to a certain value, the peak thermal stress in the RD decreases to about 1384 MPa and no longer decreases; while the peak thermal stress in the ND approaches 0 MPa and can be neglected. In the range of approximately 5–100 mm, the accumulated equivalent plastic strain decreases sharply as the thickness decreases; in other thickness ranges, it decreases slowly. Thermal fatigue experiments were conducted on the stacked structure W composed of W foils with different thicknesses and bulk W using an electron beam facility. The samples were applied with a power density of 30 MW/m2 for 10,000 and 20,000 pulses. The cracks on the surface of the stacked structure W extended along the ND direction, while on the surface of bulk W, besides the main crack in the ND direction, a crack network also formed. The experimental results were consistent with finite element simulations. When the pulse number was 10,000, as the thickness of the W foil decreased, the number and width of the cracks on the surface of the stacked structure W decreased. Only four small cracks were present on the surface of stacked structure W (0.05 mm). When the pulse number increased to 20,000, the plastic deformation and number of cracks on the surface of all samples increased. However, the stacked structure W (0.05 mm) only added one small crack and had the smallest surface roughness (Ra = 1.536 μm). Quantitative analysis of the fatigue cracks showed that the stacked structure W-PFM (0.05 mm) exhibited superior thermal fatigue performance.

This paper presents an ultra-wideband, low insertion loss, and high accuracy 6-bit digital step attenuator (DSA). To improve the accuracy of amplitude and phase shift of the attenuator, two innovative compensation structures are proposed in this paper: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SBCS) intended for large attenuation bits. Additionally, we propose insertion loss reduction techniques (ILRTs) to reduce insertion loss. The fabricated 6-bit DSA core area is only 0.51 mm2, and it exhibits an attenuation range of 31.5 dB in 0.5 dB steps. Measurements reveal that the root-mean-square (RMS) attenuation and phase errors for the 64 attenuation states are within 0.18 dB and 7°, respectively. The insertion loss is better than 2.54 dB; the return loss is better than −17 dB; and the input 1 dB compression point (IP1 dB) is 29 dBm at IF 12 GHz. To the best of our knowledge, this chip presents the highest attenuation accuracy, the lowest insertion loss, the best IP1dB, and a good matching performance in the range of 2–22 GHz using the 0.25 μm GaAs p-HEMT process.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are associated with adverse health effects. This study examined the trend of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) levels in individuals with and without pre-existing comorbidities. We analyzed the characteristics of 13,887 participants across nine U.S. NHANES cycles (1999–2000 to 2017–2018) and calculated the geometric mean (GM) of PFOA and PFOS levels, standardized by sex and age. A joinpoint regression model was used to analyze the temporal trends of serum PFOA and PFOS levels. We observed declining PFOA and PFOS serum levels among adults in NHANES from 1999–2000 to 2017–2018. Serum PFOA and PFOS concentrations were higher in men, smokers, and individuals with pre-existing CKD, hyperlipidemia, CVD, and cancer. We observed faster decline rates in PFOA levels among individuals with diabetes and CKD and faster decline rates in PFOS levels among individuals with diabetes and those without CKD. This study provided evidence of varying levels and changing trends of PFOA and PFOS between groups with and without established chronic disease, highlighting the role of environmental chemicals in the onset and development of chronic diseases.

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-σ method.

Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field.

RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.