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The confrontation between humans and bacteria is ongoing, with strategies for combating bacterial infections continually evolving. With the advancement of RNA sequencing technology, non-coding RNAs (ncRNAs) associated with bacterial infections have garnered significant attention. Recently, long ncRNAs (lncRNAs) have been identified as regulators of sterile inflammatory responses and cellular defense against live bacterial pathogens. They are involved in regulating host antimicrobial immunity in both the nucleus and cytoplasm. Increasing evidence indicates that lncRNAs are critical for the intricate interactions between host and pathogen during bacterial infections. This paper emphatically elaborates on the potential applications of lncRNAs in clinical hallmarks, cellular damage, immunity, virulence, and drug resistance in bacterial infections in greater detail. Additionally, we discuss the challenges and limitations of studying lncRNAs in the context of bacterial infections and highlight clear directions for this promising field.

Mining user satisfaction decision stages from online reviews is helpful for understanding user preferences and conducting user-centered product improvements. Therefore, this study develops a two-stage nonlinear user satisfaction decision model (USDM). First, we use word2vec technology and lexicon-based sentiment analysis to mine the sentiment polarity of each product attribute in the reviews. Then, we develop KANO mapping rules using utility functions to classify consumer preferences based on attribute importance. Based on this, a two-stage nonlinear USDM is developed to describe post-purchase evaluation behavior. In the first non-compensatory stage, consumers determine their initial satisfaction level based on the performance of basic attributes. If the performance of these attributes is poor, it is almost impossible for users to be satisfied. In the compensatory stage, the performance of the remaining attributes collectively affects final satisfaction through participation in user utility calculation. With the use of reviews from JD.com, we develop a genetic algorithm to determine feasible solutions for the USDM and verify its validity and robustness. The USDM is proven to be effective in predicting user satisfaction compared to other classic models and machine learning algorithms. This study provides a universal pattern for user satisfaction decisions and extends the study on preference analysis.

For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO4 battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO4 battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process.

Port coasts are affected by waves and tidal currents, and sediment continues to silt up, leading to a reduction in the depth of water in the channel, blocking the channel and seriously affecting the safe operation of ports. The main cause of sediment deposition in ports is suspended sediment transport, and the characteristics of the vertical distribution of suspended sediment concentrations are the embodiment of the suspended sediment transport law. This paper is divided into three parts to study the vertical distribution characteristics of suspended sediment concentrations. Firstly, the shortcomings of the traditional diffusion model were analysed by using the finite mixing theory (FMT); secondly, the sediment mixing length coefficient κs model was introduced and combined with the sediment group settling velocity model to establish the vertical distribution model of suspended sediment concentrations under wave–current; finally, the effects of various factors on the vertical distribution of the suspended sediment concentration were investigated. The results show that the model in this paper has the characteristics of “low variance and low bias”, which solves the problem that κs is difficult to determine. When the model κs < κs′ (κs′ = 0.4), the concentration of suspended sediment predicted by κs′ is overestimated, and vice versa. As the sediment concentration increases, the interaction between particles increases and the vertical distribution of the suspended sediment concentration shows the pattern of “small top and large bottom”. The larger the particle size, the greater the sedimentation rate of the suspended sediment, and a large amount of sediment will be suspended near the bottom without mixing. The higher the wave height, the stronger the boundary layer turbulence and the movement of the water particles’ trajectory, and the smaller the difference in sediment concentration between the bottom and the sea surface.

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions.

This paper presents the establishment of a sensory evaluation system for purple sweet potato rice steamed sponge cake (PSPRSSC) utilizing fuzzy mathematics. Initially, eight key sensory evaluation indices were identified through expert consultations. These indices were subsequently prioritized using fuzzy binary comparison, resulting in the following order: aroma, taste, crust color, elasticity, viscosity, chewiness, hardness, and brightness. The corresponding weight values assigned to each index were 23%, 18%, 16%, 13%, 12%, 8%, 6%, and 4%, respectively. Notably, aroma was found to have a more significant impact than visual attributes in the sensory evaluation of PSPRSSC. Based on this prioritization, comprehensive sensory evaluation criteria for PSPRSSC were formulated. Verification tests confirmed the efficacy of the proposed evaluation system. This study provides critical data and theoretical support for the sensory quality assessment of PSPRSSC, thereby facilitating its industrialization and enhancing its market viability.

This paper introduces a novel decomposition method for analyzing production efficiency based on the Data Envelopment Analysis framework, addressing the limitations of traditional approaches that often fail to isolate the contributions of individual factors. The proposed method disaggregates production efficiency into capacity utilization, labor utilization efficiency, energy utilization efficiency, and technological change, providing a more granular view of how different factors contribute to overall efficiency. By incorporating both contemporaneous and intertemporal perspectives, this approach enables a comprehensive understanding of efficiency dynamics and factor interactions over time. To demonstrate the feasibility and robustness of the proposed method, we apply it to the thermal power industry using data from 30 Chinese provinces covering the period from 2011 to 2021. The empirical results validate the effectiveness of the decomposition framework, revealing distinct regional disparities in efficiency and providing insights for targeted resource optimization strategies. Based on these findings, we offer recommendations to enhance capacity utilization, improve energy efficiency, and support sustainable development within the thermal power sector. This research contributes a powerful analytical tool for disaggregating production efficiency and offers a theoretical foundation for future studies seeking to understand the nuanced relationships between comprehensive production efficiency and single-factor efficiencies, thereby supporting better policy and management decisions in complex production systems.

Setophoma terrestris is an important phytopathogenic fungus listed by China as a harmful fungus subject to phytosanitary import control. This pathogen is a threat to a wide range of plants, particularly as the causal agent of onion pink root rot, one of the most severe diseases of onions. In order to provide rapid identification and early warning of S. terrestris and prevent its spread, we have developed a rapid, accurate, and visually intuitive diagnostic assay for this pathogen, by utilizing recombinase polymerase amplification (RPA), coupled with CRISPR/Cas12a cleavage and fluorescence-based detection systems or paper-based lateral flow strips. The developed RPA-CRISPR/Cas12a assay exhibited remarkable specificity for the detection of S. terrestris. Moreover, this protocol can detect the pathogen at a sensitivity level of 0.01 pg/μL, which significantly outperforms the 1 pg/μL sensitivity achieved by the existing qPCR-based detection method. The entire diagnostic procedure, including DNA extraction, the RPA reaction, the Cas12a cleavage, and the result interpretation, can be accomplished in 40 min. Furthermore, the successful application of the assay in infected plant samples highlighted its potential for rapid and accurate pathogen detection in agricultural settings. In summary, this RPA-CRISPR/Cas12a diagnostic method offers a potentially valuable technological solution for quarantine and disease management.

Cropland acidification seriously restricts sustainable agricultural development. The main purpose of this study was to determine whether deeper tilling could alleviate topsoil acidification to improve the quality of arable land. A soil column incubation experiment simulating tillage depths (10 cm, 30 cm and 50 cm) and lime addition was conducted to determine their effects on soil acidification improvement. The changes in soil pH, exchangeable acidity, ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), available phosphorus (AP), and microbial phospholipid fatty acids (PLFA) were analyzed. Tillage depth, lime application, and their interaction all had significant impacts on soil pH. T50 (simulated tillage depth of 50 cm) and T50+Lime (simulated tillage depth of 50 cm plus lime) treatments significantly increased the topsoil pH from 5.41 to 6.35 and 7.12, respectively. T50 treatment significantly reduced the soil exchangeable acid content compared to the T10 treatment. The nutrient accumulation along soil column indicated that the T50 and T50+Lime treatments significantly increased NO3−-N and AP content in the >30–50 cm soil layer. Compared with T30, NO3−-N accumulation in the >30–50 cm soil layers of T50 and T50+Lime treatments was 6.62 and 7.93 times higher, respectively. The accumulation of AP in the >30–50 cm soil layers of the T50 and T50+Lime treatments was 1.33 and 1.54 times higher than in the T30 treatment, respectively. These findings imply tillage up to 50 cm without exogenous materials could be a potential measure to reduce topsoil acidification and increase nutrition availability of >30–50 cm soil layers. Tillage of up to 30 cm combined with lime application confers greater benefits, which would particularly impact crops with shallow root systems. Subsequent field experiments will be conducted to further investigate the efficacy of these strategies in enhancing crop yield.

The control of flowering time plays an important role in the growth and development of potato tubers. The CCT (CO, COL and TOC1) gene family is involved in the flowering process of plants. In this study, a total of 32 StCCT family genes were identified and further classified into five subfamilies, including COL (17 members), PRR (4 members), ZIM (3 members), ASML2 (6 members) and TCR1 (2 members), based on their phylogenetic relationship. An analysis of the gene structure, motif compositions and conserved domain provided support for this classification. The StCCT genes were unevenly distributed on 12 chromosomes of the potato plant. In total, six gene duplication events were observed, which played a crucial role in the expansion of the StCCT family genes in the potato. The expression profiles exhibited diverse expression patterns of the StCCT genes in six tissues (leaf, shoot, root, tuber, stolon, and flower), StCCT32 is only expressed in flowers, while StCCT19 and StCCT8 are highly expressed in flowers and tubers, respectively. The StCCT genes exhibit different expression patterns in response to IAA and TIBA treatments at different concentrations across three tissues (leaf, stem, and tuber). After IAA and TIBA treatments, it was found that the expression level of StCCT7 was low in leaves and stems but significantly increased in tubers. Collectively, this study provided valuable information for the further study of potato formation and development and provided candidate genes for molecular breeding in the potato.

Taihe black-boned silky fowl (TS) is a native chicken breed in China with more than 2000 years of history. The present study aimed to characterize and evaluate the physical, nutritional, and flavor properties of TS eggs with a comparison to two other commercial breeds. Eggs from TS (n = 60) crossbred black-boned silky fowl (CB, n = 60) and Hy-line Brown (HL, n = 60) were used for physicochemical analysis. The evaluation system was divided into four parts based on nutrient and flavor profiles: protein and amino acids, lipids and fatty acids, mineral elements, and flavor-related amino acids and volatile compounds. Results showed that TS eggs were typically associated with the lowest egg weight and the highest yolk color, as compared with CB and HL eggs. No differences were found in crude protein, crude fat, triglycerides, and cholesterol content between eggs from the different breeds, but these eggs were distinct in terms of the amino acid, fatty acid, and volatile flavor compound profiles. Moreover, the differences in amino acid and fatty acid profiles might contribute to the specific flavor of TS eggs. Evaluation results indicated that TS egg whites may be suitable as a protein source for premature infants and young children under three years old and TS egg yolks could be considered a beneficial dietary lipid source due to their potential anti-cardiovascular properties. Additionally, TS whole eggs could serve as a valuable source of selenium (Se), molybdenum (Mo), zinc (Zn), and phosphorus (P) for adults aged 18 to 65. However, TS and CB eggs showed inferior Haugh units, eggshell quality, and essential amino acid compositions for older children, adolescents, and adults. These findings provide a better insight into the health benefits of TS eggs and contribute to the breeding and nutrition regulation of TS breeds.

In this paper, we propose a lightweight lithography machine learning-based hotspot detection model that integrates the Squeeze-and-Excitation (SE) attention mechanism and the Efficient Channel Attention (ECA) mechanism. These mechanisms can adaptively adjust channel weights, significantly enhancing the model’s ability to extract relevant features of hotspots and non-hotspots through cross-channel interaction without dimensionality reduction. Our model extracts feature vectors through seven convolutional layers and four pooling layers, followed by three fully connected layers that map to the output, thereby simplifying the CNN network structure. Experimental results on our collected layout dataset and the ICCAD 2012 layout dataset demonstrate that our model is more lightweight. By evaluating overall accuracy, recall, and runtime, the comprehensive performance of our model is shown to exceed that of ConvNeXt, Swin transformer, and ResNet 50.

Background/Objectives: Metabolic-associated fatty liver disease (MAFLD) is one of the most common liver disorders associated with obesity and metabolic syndrome, and poses a significant global health burden with limited effective treatments. The aim of this study was to assess the protective effects of mulberry twig alkaloids (SZ-A) on MAFLD and to further investigate the underlying mechanisms including the specific targets or pathways. Methods: Diet-induced obesity (DIO) and normal mouse models were established by feeding C57Bl/6J mice with a high-fat diet (HFD) or common diet for 12 weeks. SZ-A, dapagliflozin, and placebo were administered to corresponding mouse groups for 8 weeks. Data of fasting blood glucose, glucose tolerance, insulin tolerance, and the body weight of mice were collected at the baseline and termination of the experiment. Serum liver enzymes and lipids were measured by ELISA. Western blotting, qPCR, and pathological section staining were implemented to evaluate the degrees of liver steatosis, fibrosis, and oxidative stress in mice. Results: In DIO mouse models, high-dose SZ-A (800 mg/kg/d) treatment significantly inhibited HFD-induced weight gain, improved insulin tolerance, and reduced serum alanine aminotransferase, total cholesterol, and triglyceride levels compared with placebo. In DIO mice, SZ-A could alleviate the pathological changes of hepatic steatosis and fibrosis compared with placebo. Lipid catabolism and antioxidant stress-related proteins were significantly increased in the livers of the high-dose SZ-A group (p < 0.05). Inhibition of PGC1α could inhibit the function of SZ-A to enhance lipid metabolism in hepatocytes. PGC1α might interact with NRF2 to exert MAFLD-remedying effects. Conclusions: By regulating the expression of PGC1α and its interacting KEAP1/NRF2 pathway in mouse liver cells, SZ-A played important roles in regulating lipid metabolism, inhibiting oxidative stress, and postponing liver fibrosis in mice with MAFLD.

Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network (MDAF-Net), an advanced framework that significantly enhances image quality and detection accuracy. Firstly, we introduce the Multidirectional Filter (MF), which employs side-window filtering techniques and eight directional filters. This approach supports multidirectional image processing, effectively suppressing speckle noise and precisely preserving edge details. By utilizing deep neural network components, such as average pooling, the MF dynamically adapts to different noise patterns and textures, thereby enhancing image clarity and contrast. Building on this innovation, MDAF-Net integrates multidirectional feature learning with a multiscale self-attention mechanism. This design utilizes local edge information for robust noise suppression and combines global and local contextual data, enhancing the model’s contextual understanding and adaptability across various scenarios. Rigorous testing on six SAR datasets demonstrated that MDAF-Net achieves superior detection accuracy compared with other methods. On average, the Kappa coefficient improved by approximately 1.14%, substantially reducing errors and enhancing change detection precision.

Researches were conducted on the laser welding of 3 mm sheet-thickness lap joints of AA5052 with ER4043 filler wires. The effects of laser power on the joint morphology, microstructure, mechanical properties, and corrosion resistance were investigated. The results indicate that both increased heat input and the addition of filler wires make the molten pool more instable, which results in more process pores. Circular pores are observed in the upper part of the weld, while chain-like pores are distributed in the middle of the weld. The highest tensile strength of the weld joint is 192.61 MPa with an elongation of 10.1% at a laser power of 3.5 kW. The microhardness at the center of the weld is approximately 25% higher than the base material, which is probably because the addition of ER4043 filler wires brings more Si element to the weld. Moreover, the weld joints display superior corrosion resistance compared to the base material. These outcomes enhance the understanding of AA5052 laser welding with fillers wire and provide valuable in-sights for engineering applications.

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method.

Symmetric spaces and sn-symmetric spaces, as a generalization of metric spaces, have many important properties and have been widely discussed. We consider characterizations and mapping properties of sn-symmetric spaces under ideal convergence. I-symmetric spaces and I-sn-symmetric spaces are defined and studied. These not only generalize some classical results on symmetric spaces but also provide new directions to study generalized metric spaces using the notion of ideal convergence. As an application of I-sn-symmetric spaces, some relevant properties of statistical convergence are obtained. Some unanswered questions in this field are raised.

The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet’s superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation.

Daytime dysfunction, including symptoms like sleepiness, poor memory, and reduced responsiveness, is not well researched. It is crucial to develop animal models and study the biological mechanisms involved. We simulated sleep disorders through sleep deprivation, and stressful stimuli were used to establish daytime functional animal models. We used tests like the sodium pentobarbital sleep synergy test and the DSI telemetry system to measure sleep duration and structure. We also used tests like the Morris water maze, open field test, grip test, and baton twirling test to assess mental and physical fatigue. To assess the intrinsic biological mechanisms, we measured sleep–wake-related neurotransmitters and related receptor proteins, circadian rhythm-related proteins and cognition-related proteins in hypothalamus tissue, and oxidative stress, inflammatory factors, S100β, and HPA axis-related indexes in serum. Multi-factor sleep deprivation resulted in the disruption of sleep–wakefulness structure, memory–cognitive function degradation, decreased grip coordination, and other manifestations of decreased energetic and physical strength. The intrinsic biological mechanisms were related to the disturbed expression of sleep–wake, circadian rhythm, memory–cognition-related proteins, as well as the significant elevation of inflammatory factors, oxidative stress, the HPA axis, and other related indicators. Intrinsically related biological mechanisms and reduced sirt1 expression can lead to disruption of circadian rhythms; resulting in disruption of their sleep–wake-related neurotransmitter content and receptor expression. Meanwhile, the reduced expression of sirt1 also resulted in reduced expression of synapse-associated proteins. This study prepared an animal model of daytime dysfunction by means of multi-factor sleep deprivation. With sirt1 as a core target, the relevant biological mechanisms of neurological disorders were modulated.

The maintenance of biodiversity and ecological balance heavily relies on the diversity of insects. In order to investigate the impacts of elevation and human disturbance, as well as their interactions on insect diversity, we conducted an intensive survey of insects in the Hengduan Mountain Range, which is situated on the southeastern periphery of the Tibetan Plateau in China. A total of 50 line transects were established in this study to investigate the impact of elevation and human disturbance on insect diversity and distribution patterns. Designed insect surveys were conducted at various elevations and levels of human disturbance, and statistical methods such as generalized linear modeling and redundancy analysis were employed for data analysis. The results of this study indicated a negative correlation between insect diversity and elevation. Additionally, moderate disturbance was found to have a positive impact on insect diversity to some extent. The explanatory power of the model for the distribution of insect diversity could be improved if elevation and human disturbance were included as an interaction effect into the model, and there were differences in the effects of human disturbances on insect diversity at different elevation levels. The highest insect diversity was observed under low disturbance conditions below elevation of ~2200 m, whereas above this threshold, insect diversity was the highest under moderate disturbance compared to low disturbance. The response of different insect taxa to the interactions of elevation and human disturbance varied. The findings imply that when formulating strategies for managing insect diversity, it is crucial to thoroughly consider the interaction of environmental factors and disturbance response of individual insect taxa.

Autonomous underwater vehicles (AUVs) have been increasingly applied in marine environmental monitoring. Their outstanding capability of performing tasks without human intervention makes them a popular tool for environmental data collection, especially in unknown and remote regions. This paper addresses the path planning problem when AUVs are used to perform plume source tracing in an unknown environment. The goal of path planning is to locate the plume source efficiently. The path planning approach is developed using the Double Deep Q-Network (DDQN) algorithm in the deep reinforcement learning (DRL) framework. The AUV gains knowledge by interacting with the environment, and the optimal direction is extracted from the mapping obtained by a deep neural network. The proposed approach was tested by numerical simulation and on a real ground vehicle. In the numerical simulation, several initial sampling strategies were compared on the basis of survey efficiency. The results show that direct learning based on the interaction with the environment could be an appropriate survey strategy for plume source tracing problems. The comparison with the canonical lawnmower path used in practice showed that path planning using DRL algorithms could be potentially promising for large-scale environment exploration.

Coix lacryma-jobi L., an annual or perennial plant belonging to the Poaceae family, has long been cultivated as a food and medicine plant in China. In recent years, coix cultivation for high yields and good quality has become a research hotspot in Southwest China. Soil optimization is essential for improving crop growth. To ensure the robust establishment of coix plants, eight soil substrates, prepared from three typical soils, i.e., red clay soil, peat soil, and sandy soil, were used to cultivate two local coix varieties (Pu coix from Fujian Province, China; Qi coix from Hebei Province, China), and the plant growth and root coixol content of the two coix varieties were investigated. It was found that coix plants could maintain growth when cultivated with peat soil or sandy soil, but red clay soil was unfavorable for coix growth. The mixtures of sandy soils and peat soils resulted in synergistic benefits for coix growth and root coixol levels over the effects of sandy soil or peat soil alone. In conclusion, the mixtures of sandy soils and peat soils with appropriate proportions (sandy soils/peat soils = 2:1) were suggested as an ideal soil substrate for coix cultivation. The results provide valuable guidance for the establishment of coix plants, which could contribute to high yields and good quality in coix cultivation.

Dilated cardiomyopathy (DCM) is one of the major causes of heart failure. Although significant progress has been made in elucidating the underlying mechanisms, further investigation is required for clarifying molecular diagnostic and therapeutic targets. In this study, we found that the mRNA level of protein phosphatase 2 regulatory subunit B’ delta (Ppp2r5d) was altered in the peripheral blood plasma of DCM patients. Knockdown of Ppp2r5d in murine cardiomyocytes increased the intracellular levels of reactive oxygen species (ROS) and inhibited adenosine triphosphate (ATP) synthesis. In vivo knockdown of Ppp2r5d in an isoproterenol (ISO)-induced DCM mouse model aggravated the pathogenesis and ultimately led to heart failure. Mechanistically, Ppp2r5d-deficient cardiomyocytes showed an increase in phosphorylation of STAT3 at Y705 and a decrease in phosphorylation of STAT3 at S727. The elevated levels of phosphorylation at Y705 in STAT3 triggered the upregulation of interleukin 6 (IL6) expression. Moreover, the decreased phosphorylation at S727 in STAT3 disrupted mitochondrial electron transport chain function and dysregulated ATP synthesis and ROS levels. These results hereby reveal a novel role for Ppp2r5d in modulating STAT3 pathway in DCM, suggesting it as a potential target for the therapy of the disease.

Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority.

Sea cucumber viscera contain various naturally occurring active substances, but they are often underutilized during sea cucumber processing. Polydeoxyribonucleotide (PDRN) is an adenosine A2A receptor agonist that activates the A2A receptor to produce various biological effects. Currently, most studies on the activity of PDRN have focused on its anti-inflammatory, anti-apoptotic, and tissue repair properties, yet relatively few studies have investigated its antioxidant activity. In this study, we reported for the first time that PDRN was extracted from the sperm of Apostichopus japonicus (AJS-PDRN), and we evaluated its antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS), and hydroxyl radical scavenging assays. An in vitro injury model was established using H2O2-induced oxidative damage in RAW264.7 cells, and we investigated the protective effect of AJS-PDRN on these cells. Additionally, we explored the potential mechanism by which AJS-PDRN protects RAW264.7 cells from damage using iTRAQ proteomics analysis. The results showed that AJS-PDRN possessed excellent antioxidant activity and could significantly scavenge DPPH, ABTS, and hydroxyl radicals. In vitro antioxidant assays demonstrated that AJS-PDRN was cytoprotective and significantly enhanced the antioxidant capacity of RAW264.7 cells. The results of GO enrichment and KEGG pathway analysis indicate that the protective effects of AJS-PDRN pretreatment on RAW264.7 cells are primarily achieved through the regulation of immune and inflammatory responses, modulation of the extracellular matrix and signal transduction pathways, promotion of membrane repair, and enhancement of cellular antioxidant capacity. The results of a protein–protein interaction (PPI) network analysis indicate that AJS-PDRN reduces cellular oxidative damage by upregulating the expression of intracellular selenoprotein family members. In summary, our findings reveal that AJS-PDRN mitigates H2O2-induced oxidative damage through multiple pathways, underscoring its significant potential in the prevention and treatment of diseases caused by oxidative stress.

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.

(1) Background: Various factors, such as oxidative stress, mitochondrial dysfunction, tumors, inflammation, trauma, immune disorders, and neuronal toxicity, can cause nerve damage. Chemical nerve injury, which results from exposure to toxic chemicals, has garnered increasing research attention. The thioredoxin (Trx) system, comprising Trx, Trx reductase, nicotinamide adenine dinucleotide phosphate, and Trx-interacting protein (TXNIP; endogenous Trx inhibitor), helps maintain redox homeostasis in the central nervous system. The dysregulation of this system can cause dementia, cognitive impairment, nerve conduction disorders, movement disorders, and other neurological disorders. Thus, maintaining Trx system homeostasis is crucial for preventing or treating nerve damage. (2) Objective: In this review study, we explored factors influencing the homeostasis of the Trx system and the involvement of its homeostatic imbalance in chemical nerve injury. In addition, we investigated the therapeutic potential of the Trx system-targeting active substances against chemical nerve injury. (3) Conclusions: Chemicals such as morphine, metals, and methylglyoxal interfere with the activity of TXNIP, Trx, and Trx reductase, disrupting Trx system homeostasis by affecting the phosphatidylinositol-3-kinase/protein kinase B, extracellular signal-regulated kinase, and apoptotic signaling-regulated kinase 1/p38 mitogen-activated protein kinase pathways, thereby leading to neurological disorders. Active substances such as resveratrol and lysergic acid sulfide mitigate the symptoms of chemical nerve injury by regulating the Ras/Raf1/extracellular signal-regulated kinase pathway and the miR-146a-5p/TXNIP axis. This study may guide the development of Trx-targeting modulators for treating neurological disorders and chemical nerve injuries.

Barringtonia racemosa (L.) Spreng. (Lecythidaceae), a crucial species in mangrove ecosystems, is facing endangerment primarily due to habitat loss. To address this issue, research is imperative to identify suitable conservation habitats for the endangered B. racemosa within mangrove ecosystems. The utilization of the optimized Maximum Entropy (MaxEnt) model has been instrumental in predicting potential suitable regions based on global distribution points and environmental variables under current and future climates conditions. The study revealed that the potential distribution area of B. racemosa closely aligns with its existing range with an Area Under the Curve (AUC) greater than 0.95. The Jackknife, AUC, percent contribution (PC), and permutation importance (PI) tests were employed alongside the optimized MaxEnt model to examine the influence of environmental variables on the distribution of B. racemosa. The primary factors identified as significant predictors of B. racemosa distribution included the average temperature of the ocean surface (Temperature), average salinity of the ocean surface (Salinity), precipitation of the warmest quarter (Bio18), precipitation of the driest month (Bio14), seasonal variation coefficient of temperature (Bio4), and isothermality (Bio3). Currently, the habitat range of B. racemosa is predominantly found in tropical and subtropical coastal regions near the equator. The total suitable habitat area measures 246.03 km2, with high, medium, low, and unsuitable areas covering 3.90 km2, 8.57 km2, 16.94 km2, and 216.63 km2, respectively. These areas represent 1.58%, 3.48%, 6.88%, and 88.05% of the total habitat area, respectively. The potential distribution area of B. racemosa demonstrated significant variations under three climate scenarios (SSP126, SSP245, and SSP585), particularly in Asia, Africa, and Oceania. Both low and high suitable areas experienced a slight increase in distribution. In summary, the research suggests that B. racemosa primarily flourishes in coastal regions of tropical and subtropical areas near the equator, with temperature and precipitation playing a significant role in determining its natural range. This study offers important implications for the preservation and control of B. racemosa amidst habitat degradation and climate change threats. Through a comprehensive understanding of the specific habitat needs of B. racemosa and the implementation of focused conservation measures, efforts can be made to stabilize and rejuvenate its populations in their natural environment.

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography–tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method’s practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

Pesticides play a crucial role in agricultural production by preventing diseases and pests and ensuring food yield. However, the irrational use of pesticides can lead to numerous issues that compromise crop quality and counteract the original intentions of their application. Therefore, it is necessary to identify more effective methods to counteract pesticide stress. Here we review the impacts of herbicides, insecticides, and fungicides on plants and the measures taken to reduce pesticide residues on plants. We have found that despite the substantial differences in the mechanisms of action of the aforementioned three types of pesticides, the adverse effects they inflict on plants are similar, and at certain dosages, they can severely constrain plant growth and disrupt physiological functions. Also, most current research on using exogenous growth regulators to alleviate pesticide stress still focuses on photosynthesis, the antioxidant system, three-stage detoxification, and secondary metabolites, neglecting the search for genes that respond to pesticide stress. We believe that by combining biological protection with post-harvest treatment techniques and exploring potential genes that are responsive to pesticide stress, a better strategy for dealing with pesticide stress can be found, thereby promoting sustainable agricultural development.

The application of potassium fertilizer application and straw return are effective agronomic measures for increasing crop productivity; however, information on how straw return—when substituting potassium fertilizer—affects crop yield, efficiency, and quality in dryland remains limited. In this study, an experiment on a dryland summer maize and winter wheat rotation system was initiated in 2007. This study included four treatments: CK (no fertilizer and no straw return), NP (nitrogen and phosphorus fertilizer application without straw return), NPK (nitrogen, phosphorus, and potassium fertilizer application without straw return), and NPS (NP treatment with straw return, substituting potassium fertilizer as used in the NPK treatment). These treatments were employed to assess grain yield and fertilizer agronomic efficiency in 2015–2020. Additionally, we evaluated the content of nitrogen (N), phosphorus (P), and potassium (K), as well as the protein content and protein yield in maize and wheat grains and the protein components in wheat grains in 2019–2020. The results showed that compared to the CK treatment, NP, NPK, and NPS treatments not only significantly increased the yield, protein yield, and fertilizer agronomic efficiency in both maize and wheat but also increased the content of protein and protein components in wheat grains. Compared to the NP treatment, the NPK treatment significantly increased the contents of N, K, globulin, and gluten in wheat grains by 5.11%, 21.59%, 10.06%, and 15.14%. Compared to NPK treatment, NPS treatment significantly increased the average yield of summer maize by 21.33% and 20.91%, respectively, as well as the annual yield by 9.99% and 13.59%, the N fertilizer agronomic efficiency of summer maize by 132.47%, and the annual N and P fertilizer agronomic efficiency by 42.83% and 64.36%, over the five-year period. The NPS treatment also significantly increased the summer maize protein yield and annual protein yield by 10.43% and 23.08%, as well as the content of protein components, the protein content, and P content in wheat grains by 4.93–13.58%, 7.81%, and 28.89%, respectively. In conclusion, NPS treatment can not only enhance crop yield, protein yield, and fertilizer agronomic efficiency in summer maize annually but also has the advantage of promoting wheat quality. NPS is an efficient strategy to improve crop yield, efficiency, and quality in a dryland maize–wheat rotation system.

The advancement of virtual coupling technology in urban rail transit has facilitated the online coupling and decoupling of trains, enabling a range of flexible transportation configurations, including various route types and adjustable formations. This study targets the fluctuating passenger demands on urban rail lines, aiming to minimize both passenger travel and operational costs. The model integrates constraints associated with virtual coupling, train operations, rolling stock circulation, and the interaction between virtually coupled trains and passenger arrivals. New decision variables are introduced to depict the train formation state under virtual coupling scenarios. An integrated optimization model for train diagrams and rolling stock circulation under virtual coupling conditions is developed, employing a genetic-simulated annealing algorithm informed by train operation simulations. A case study on an urban rail line during the morning peak examines the optimization of train diagrams for full-length and short-turn routes. Findings confirm that virtual coupling technology effectively adapts to lines with uneven passenger flow distribution, significantly enhancing the match between supply and demand, equalizing spatial and temporal traffic variations, and harmonizing the quality of passenger services with operational efficiency.

Thermal storage tanks are the most widely used devices for thermodynamic storage. Their stratification performance is a key factor in determining their effectiveness. In this study, a structure was proposed to improve the thermal stratification of an elbow-type thermal storage tank. An experimental study was conducted on its exothermic properties for applications in hot water storage tanks. An experimental analysis was performed to investigate the exothermic properties of the proposed structure, and the results were compared with those obtained from simulations using CFD (ANSYS 19.1) software. To investigate the effect of thermal stratification on the water inlet structure, the Richardson number, hot water output rate, and MIX number of the elbow inlet structure were compared with those of the water distributor inlet structure. The results show that the MIX numbers corresponding to the inlet structures of the two types of tanks, the elbow and water distributor types, are not very different. These values were almost identical to the Richardson numbers. Under the same working conditions, the hot water output rate was 84.90% for the elbow inlet structure and 76.39% for the water distributor inlet structure. In conclusion, elbow-type water inlet structures are easy to install, and the manufacturing process is simplified.

Ischemic stroke is a common cerebrovascular disease with high mortality, high morbidity, and high disability. Cerebral ischemia/reperfusion injury seriously affects the quality of life of patients. Luteolin-7-O-β-d-glucuronide (LGU) is a major active flavonoid compound extracted from Ixeris sonchifolia (Bge.) Hance, a Chinese medicinal herb mainly used for the treatment of coronary heart disease, angina pectoris, cerebral infarction, etc. In the present study, the protective effect of LGU on cerebral ischemia/reperfusion injury was investigated in an oxygen–glucose deprivation/reoxygenation (OGD/R) neuronal model and a transient middle cerebral artery occlusion (tMCAO) rat model. In in vitro experiments, LGU was found to improve the OGD/R-induced decrease in neuronal viability effectively by the MTT assay. In in vivo experiments, neurological deficit scores, infarction volume rates, and brain water content rates were improved after a single intravenous administration of LGU. These findings suggest that LGU has significant protective effects on cerebral ischemia/reperfusion injury in vitro and in vivo. To further explore the potential mechanism of LGU on cerebral ischemia/reperfusion injury, we performed a series of tests. The results showed that a single administration of LGU decreased the content of EB and S100B and ameliorated the abnormal expression of tight junction proteins ZO-1 and occludin and metalloproteinase MMP-9 in the ischemic cerebral cortex of the tMCAO 24-h injury model. In addition, LGU also improved the tight junction structure between endothelial cells and the degree of basement membrane degradation and reduced the content of TNF-α and IL-1β in the brain tissue. Thereby, LGU attenuated cerebral ischemia/reperfusion injury by improving the permeability of the blood–brain barrier. The present study provides new insights into the therapeutic potential of LGU in cerebral ischemia.

Four new diterpenoids, isodosins A–D (1–4), together with nine known compounds (5–13) were isolated and identified from the aerial parts of Isodon serra (Maxim.) Hara. The structures of the new diterpenoids were elucidated based on the analysis of HR-ESI-MS data, 1D/2D-NMR-spectroscopic data, and electronic circular dichroism (ECD) calculations. Cytotoxicities of compounds 2, 3, 5, 6, and 9 against the HepG2 and H1975 cell lines were evaluated with the MTT assay. As a result, compounds 2, 3, and 6 revealed higher levels of cytotoxicity against HepG2 cells than against H1975 cells. Moreover, compund 6 demonstrated the most efficacy in inhibiting the proliferation of HepG2 cells, with an IC50 value of 41.13 ± 3.49 μM. This effect was achieved by inducing apoptosis in a dose-dependent manner. Furthermore, the relationships between the structures and activities of these compounds are briefly discussed.

Cadmium (Cd), as the most prevalent heavy metal contaminant poses serious risks to plants, humans, and the environment. The ubiquity of this toxic metal is continuously increasing due to the rapid discharge of industrial and mining effluents and the excessive use of chemical fertilizers. Nanoparticles (NPs) have emerged as a novel strategy to alleviate Cd toxicity. Zinc oxide nanoparticles (ZnO-NPs) have become the most important NPs used to mitigate the toxicity of abiotic stresses and improve crop productivity. The plants quickly absorb Cd, which subsequently disrupts plant physiological and biochemical processes and increases the production of reactive oxygen species (ROS), which causes the oxidation of cellular structures and significant growth losses. Besides this, Cd toxicity also disrupts leaf osmotic pressure, nutrient uptake, membrane stability, chlorophyll synthesis, and enzyme activities, leading to a serious reduction in growth and biomass productivity. Though plants possess an excellent defense mechanism to counteract Cd toxicity, this is not enough to counter higher concentrations of Cd toxicity. Applying Zn-NPs has proven to have significant potential in mitigating the toxic effects of Cd. ZnO-NPs improve chlorophyll synthesis, photosynthetic efficiency, membrane stability, nutrient uptake, and gene expression, which can help to counter toxic effects of Cd stress. Additionally, ZnO-NPs also help to reduce Cd absorption and accumulation in plants, and the complex relationship between ZnO-NPs, osmolytes, hormones, and secondary metabolites plays an important role in Cd tolerance. Thus, this review concentrates on exploring the diverse mechanisms by which ZnO nanoparticles can alleviate Cd toxicity in plants. In the end, this review has identified various research gaps that need addressing to ensure the promising future of ZnO-NPs in mitigating Cd toxicity. The findings of this review contribute to gaining a deeper understanding of the role of ZnO-NPs in combating Cd toxicity to promote safer and sustainable crop production by remediating Cd-polluted soils. This also allows for the development of eco-friendly approaches to remediate Cd-polluted soils to improve soil fertility and environmental quality.

The association between post-traumatic growth (PTG) and cognitive empathy is well documented; however, few studies have tested the causal pathways explaining this association in school bullying victims’ later recovery and growth in the long term. This study used a longitudinal design to examine the reciprocal relations between cognitive empathy and post-traumatic growth (PTG) in school bullying victims. We screened 725 adolescents who had experienced school bullying as our final subjects out of the 2173 adolescents we surveyed over three periods (November 2019, 2020, and 2021). Controlling for gender, cross-lagged analysis revealed that both cognitive empathy at T1 and T2 predicted adolescents’ later PTG at T2 (γ = 0.096, p < 0.05) and T3 (γ = 0.085, p < 0.05), respectively, but the predictive effect across time points from PTG to cognitive empathy was not significant. The results delineated a specific directionality in the relation between cognitive empathy and PTG and suggested an important role of cognitive empathy in fostering school bullying victims’ later recovery and growth. These findings contribute to ongoing research into ways researchers and educators may help and support school bullying victims.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are central components of the machinery mediating cell membrane fusion and intracellular vesicular trafficking in eukaryotic cells, and have been well-documented to play critical roles in growth, development, and pathogenesis in the filamentous fungal plant pathogens. However, little is known about the contributions of SNAREs to the physiology and biocontrol potential in entomopathogenic filamentous fungi. Here, a genome-wide analysis of SNARE genes was performed taking advantage of the available whole genome sequence of Beauveria bassiana, a classical entomopathogenic fungus. Based on the compared genomic method, 22 genes encoding putative SNAREs were identified from the whole genome of B. bassiana, and were classified into four groups (7 Qa-, 4 Qb-, 6 Qc-, and 5 R-SNAREs) according to the conserved structural features of their encoding proteins. An R-SNARE encoding gene BbSEC22 was further functionally characterized by gene disruption and complementation. The BbSEC22 null mutant showed a fluffy appearance in mycelial growth and an obvious lag in conidial germination. The null mutant also exhibited significantly increased sensitivity to oxidative stress and cell wall perturbing agents and reduced the yield of conidia production by 43.1% compared with the wild-type strain. Moreover, disruption of BbSEC22 caused a significant decrease in conidial virulence to Spodoptera litura larvae. Overall, our results provide an overview of vesicle trafficking in B. bassiana and revealed that BbSec22 was a multifunctional protein associated with mycelial growth, sporulation, conidial germination, stress tolerance, and insecticidal virulence.

Staphylococcus aureus (S. aureus) is a major human pathogen and can cause a wide range of diseases, including pneumonia, osteomyelitis, skin and soft tissue infections (SSTIs), endocarditis, mastitis, bacteremia, and so forth. Rats have been widely used in the field of infectious diseases due to their unique advantages, and the models of S. aureus infections have played a pivotal role in elucidating their pathogenic mechanisms and the effectiveness of therapeutic agents. This review outlined the current application of rat models in S. aureus infections and future prospects for rat models in infectious diseases caused by S. aureus.

The Supreme Deity Talismans of XuHuo, as the mysterious visual artforms in Daoism, are significant ritual images created by Daoists during the Song and Yuan dynasties. This article explores the creation process of the SDTXH by applying psychoanalytic theory to analyze the literature related to their drawings in Daozang. The ritualistic concentration involved in writing the SDTXH resembles Jung’s systematic exercises aimed at eliminating critical attention, thus creating a vacuum in conscious. This vacuum serves as the key premise for active imagination, through which the unconscious is integrated into the conscious. Fuqiao, in fact, is a symbol dynamically depicting the process of the unconscious being inseminated by the conscious through immersion within it. This integration, or unity, represents the embodiment of concepts such as “mandala”, Guizhong, and Taiji. The SDTXH originate from Guizhong and essentially serve as archetypal images, similar to the sandplay images constructed by the clients. As the language of the unconscious, the SDTXH effectively bridge the conscious and unconscious, enabling the unconscious content, namely the archetype, to be symbolically expressed and bringing order to the creator through active imagination. The emergence of the SDTXH signifies the practitioner’s progress towards achieving psychic wholeness.

The Internet of Things (IoT) is rapidly transforming our lives and work, enabling a wide range of emerging services and applications. However, as the scale of the IoT expands, its security issues are becoming increasingly prominent. Malicious actors can exploit vulnerabilities in IoT devices to launch attacks. Protecting the IoT begins with device identification. Identified devices can have corresponding protective measures selected based on the information, thereby enhancing network security. In this study, we propose a dual-machine-learning-based IoT device identification algorithm, Dual-IoTID, which identifies devices based on the payload of IoT device sessions. In contrast to existing methods that rely on extracting header fields or network layer features, our approach attempts to obtain identification information from session payloads. Dual-IoTID first extracts frequent items from sessions and uses a first-layer classifier to obtain a confidence matrix for initial classification. Then, the confidence matrix, along with extracted session communication features, is fed into a second-layer classifier for IoT device identification. Our proposed method is applicable to any IoT device, and it is also suitable for networks with NAT enabled. Experimental results demonstrate that Dual-IoTID has higher accuracy than existing methods, achieving 99.48% accuracy in the UNSW dataset and accurately identifying IoT devices even in environments containing non-IoT devices.

The thickness of the Antarctic ice sheet is a crucial parameter for inferring glacier mass and its evolution process. In the literature, the gravity method has been proven to be one of the effective means for estimating ice sheet thickness. And it is a preferred approach when direct measurements are not available. However, few gravity inversion methods are valid in rugged terrain areas with undulating observation surfaces (UOSs). To solve this problem, this paper proposes an improved high-precision 3D density interface inversion method considering terrain and UOSs simultaneously. The proposed method utilizes airborne gravity data at their flight altitudes, instead of the continued data yield from the unstable downward continuation procedure. In addition, based on the undulating right rectangular prism model, the large reliefs of the terrain are included in the iterative inversion. The proposed method is verified on two synthetic examples and is successfully applied to real data in East Antarctica.

Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.

In recent years, optical fibers have found extensive use in special environments, including high-energy radiation scenarios like nuclear explosion diagnostics and reactor monitoring. However, radiation exposure, such as X-rays, gamma rays, and neutrons, can compromise fiber safety and reliability. Consequently, researchers worldwide are focusing on radiation-resistant fiber optic technology. This paper examines optical fiber radiation damage mechanisms, encompassing ionization damage, displacement damage, and defect centers. It also surveys the current research on radiation-resistant fiber optic design, including doping and manufacturing process improvements. Ultimately, it summarizes the effectiveness of various approaches and forecasts the future of radiation-resistant optical fibers.

Recently, the vascular protective effect of anti-diabetic agents has been receiving much attention. Sodium glucose cotransporter 2 (SGLT2) inhibitors had demonstrated reductions in cardiovascular (CV) events. However, the therapeutic effect of dapagliflozin on angiogenesis in peripheral arterial disease was unclear. This study aimed to explore the effect and mechanism of dapagliflozin on angiogenesis after hindlimb ischemia. We first evaluated the effect of dapagliflozin on post-ischemic angiogenesis in the hindlimbs of rats. Laser doppler imaging was used to detect the hindlimb blood perfusion. In addition, we used immunohistochemistry to detect the density of new capillaries after ischemia. The relevant signaling pathways of dapagliflozin affecting post-ischemic angiogenesis were screened through phosphoproteomic detection, and then the mechanism of dapagliflozin affecting post-ischemic angiogenesis was verified at the level of human umbilical vein endothelial cells (HUVECs). After subjection to excision of the left femoral artery, all rats were randomly distributed into two groups: the dapagliflozin group (left femoral artery resection, receiving intragastric feeding with dapagliflozin (1 mg/kg/d), for 21 consecutive days) and the model group, that is, the positive control group (left femoral artery resection, receiving intragastric feeding with citric acid–sodium citrate buffer solution (1 mg/kg/d), for 21 consecutive days). In addition, the control group, that is the negative control group (without left femoral artery resection, receiving intragastric feeding with citric acid–sodium citrate buffer solution (1 mg/kg/d), for 21 consecutive days) was added. At day 21 post-surgery, the dapagliflozin-treatment group had the greatest blood perfusion, accompanied by elevated capillary density. The results showed that dapagliflozin could promote angiogenesis after hindlimb ischemia. Then, the ischemic hindlimb adductor-muscle tissue samples from three rats of model group and dapagliflozin group were taken for phosphoproteomic testing. The results showed that the PI3K-Akt-eNOS signaling pathway was closely related to the effect of dapagliflozin on post-ischemic angiogenesis. Our study intended to verify this mechanism from the perspective of endothelial cells. In vitro, dapagliflozin enhanced the tube formation, migration, and proliferation of HUVECs under ischemic and hypoxic conditions. Additionally, the dapagliflozin administration upregulated the expression of angiogenic factors phosphorylated Akt (p-Akt) and phosphorylated endothelial nitric oxide synthase (p-eNOS), as well as vascular endothelial growth factor A (VEGFA), both in vivo and in vitro. These benefits could be blocked by either phosphoinositide 3-kinase (PI3K) or eNOS inhibitor. dapagliflozin could promote angiogenesis after ischemia. This effect might be achieved by promoting the activation of the PI3K-Akt-eNOS signaling pathway. This study provided a new perspective, new ideas, and a theoretical basis for the treatment of peripheral arterial disease.

In this paper, a new axial-type magnetic gear with an auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AMGs, it has a higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of the proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterparts in RMGs, the AFS-AMG achieves a small size. Then, the geometrical parameters of the AFS-AMG are optimized to obtain better electromagnetic performance, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

Organic dyes are widely used in many important areas, but they also bring many issues for water pollution. To address the above issues, a reconstructed kaolinite hybrid compound (γ-AlOOH@A-Kaol) was obtained from raw kaolinite (Kaol) in this work. The product was then characterized by X-ray diffraction (XRD), Fourier-transform infrared (ATR-FTIR), Brunauer-Emmett-Teller (BET), and scanning electron microscopy (SEM), and the absorption properties of γ-AlOOH@A-Kaol for congo red were further studied. The results demonstrated that flower-like γ-AlOOH with nanolamellae were uniformly loaded on the surface of acid-treated Kaol with a porous structure (A-Kaol). In addition, the surface area (36.5 m2/g), pore volume (0.146 cm3/g), and pore size (13.0 nm) of γ-AlOOH@A-Kaol were different from those of A-Kaol (127.4 m2/g, 0.127 cm3/g, and 4.28 nm, respectively) and γ-AlOOH (34.1 m2/g, 0.315 cm3/g, and 21.5 nm, respectively). The unique structure could significantly enhance the sorption capacity for congo red, which could exceed 1000 mg/g. The reasons may be ascribed to the abundant groups of -OH, large specific surface area, and porous structure of γ-AlOOH@A-Kaol. This work provides an efficient route for comprehensive utilization and production of Kaol-based compound materials that could be used in the field of environmental conservation.

Marine-derived Penicillium fungi are productive sources of structurally unique and diverse bioactive secondary metabolites, representing a hot topic in natural product research. This review describes structural diversity, bioactivities and statistical research of 452 new natural products from marine-derived Penicillium fungi covering 2021 to 2023. Sediments are the main sources of marine-derived Penicillium fungi for producing nearly 56% new natural products. Polyketides, alkaloids, and terpenoids displayed diverse biological activities and are the major contributors to antibacterial activity, cytotoxicity, anti-inflammatory and enzyme inhibitory capacities. Polyketides had higher proportions of new bioactive compounds in new compounds than other chemical classes. The characteristics of studies in recent years are presented.

This study investigates the growth dynamics and heavy metal immobilization in Sudan grass cultivated on substrates composed of electrolytic manganese residue (EMR), phosphogypsum, and chili straw biochar. Pot experiments revealed that a substrate with phosphogypsum constituting 75% of the mix hinders Sudan grass seed germination. Compared with sole EMR utilization, the composite substrates notably enhanced plant growth, evidenced by increases in plant height and fresh weight. The integration of these substrates led to a significant elevation in total chlorophyll content (up to 54.39%) and a reduction in malondialdehyde (MDA) levels (up to 21.66%), indicating improved photosynthetic activity and lower oxidative stress. The addition of biochar reduced the content of Zn, Cd, and Mn in the roots of Sudan grass by up to 25.92%, 20.00%, and 43.17%, respectively; and reduced the content of Pb, Mn, and Cr in the shoot by up to 33.72%, 17.53%, and 26.32%, respectively. Fuzzy membership function analysis identified the optimal substrate composition as 75% EMR and 25% phosphogypsum, with 5% chili straw biochar, based on overall performance metrics. This study adopts the concept of “to treat waste with waste”. The approach is to fully consider the fertility characteristics of EMR, phosphogypsum, and biochar, underscoring the potential for utilizing waste-derived materials in cultivating Sudan grass and offering a sustainable approach to plant growth and heavy metal management.

In this work, analytic solutions of initial value problems for fractional Langevin equations involving Hilfer fractional derivatives and variable coefficients are studied. Firstly, the equivalence of an initial value problem to an integral equation is proved. Secondly, the existence and uniqueness of solutions for the above initial value problem are obtained without a contractive assumption. Finally, a formula of explicit solutions for the proposed problem is derived. By using similar arguments, corresponding conclusions for the case involving Riemann–Liouville fractional derivatives and variable coefficients are obtained. Moreover, the nonlinear case is discussed. Two examples are provided to illustrate theoretical results.