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The structures and properties of hydrophobic association polymers can be controlled using micelles. In this work, we synthesize a reactive hydrophobic surfactant monomer, KS-3, from oleic acid, N,N-dimethylpropylenediamine, and allyl chloride. A strong synergistic effect between KS-3 and cocamidopropyl betaine in aqueous solution enhances the hydrophilic dispersibility of KS-3, thereby transforming spherical micelles into cylindrical micelles. KS-3 was grafted onto a polyacrylamide chain via aqueous free-radical polymerization to obtain RES, a hydrophobic association polymer. Structural analysis revealed that the RES polymers assembled in wormlike micelles were more tightly arranged than those assembled in spherical micelles, resulting in a compact network structure in water, smooth surface, and high thermal stability. Rheological tests revealed that the synthesized polymers with wormlike and spherical micelles exhibited shear-thinning properties along with different structural strengths and viscoelasticities. Therefore, controlling the micellar state can effectively regulate the polymer properties. The polymers obtained through wormlike micelle polymerization have potential applications in fields with high demands, such as drug release, water purification, and oilfield development.

The development of titanium alloys is limited by issues such as low hardness, poor wear resistance, and sensitivity to adhesive wear. Using laser cladding technology to create high-hardness wear-resistant coatings on the surface of titanium alloys is an economical and efficient method that can enhance their surface hardness and wear resistance. This paper presents the preparation of two types of nickel-based composite coatings, Ni60-Ti-Cu-xB4C and Ni60-Ti-Cu-B4C-xCeO2, on the surface of TC4 titanium alloy using laser cladding. When the B4C addition was 8 wt.%, the hardness of the cladding layer was the highest, with an average microhardness of 1078 HV, which was 3.37 times that of the TC4 substrate. The friction coefficient was reduced by 24.7% compared to the TC4 substrate, and the wear volume was only 2.7% of that of the substrate material. When the CeO2 content was 3 wt.%, the hardness of the cladding layer was the highest, with an average microhardness of 1105 HV, which was 3.45 times that of the TC4 substrate. The friction coefficient was reduced by 33.7% compared to the substrate material, and the wear volume was only 1.8% of that of the substrate material.

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD’s exceptional performance across multiple evaluation metrics, including accuracy, Matthews’ correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields.

Oxidative stress has been identified as a major factor in the development and progression of pain and psychiatric disorders, but the underlying biomarkers and molecular signaling pathways remain unclear. This study aims to identify oxidative stress-related biomarkers and signaling pathways in pain–depression comorbidity. Integrated bioinformatics analyses were applied to identify key genes by comparing pain–depression comorbidity-related genes and oxidative stress-related genes. A total of 580 differentially expressed genes and 35 differentially expressed oxidative stress-related genes (DEOSGs) were identified. By using a weighted gene co-expression network analysis and a protein–protein interaction network, 43 key genes and 5 hub genes were screened out, respectively. DEOSGs were enriched in biological processes and signaling pathways related to oxidative stress and inflammation. The five hub genes, RNF24, MGAM, FOS, and TKT, were deemed potential diagnostic and prognostic markers for patients with pain–depression comorbidity. These genes may serve as valuable targets for further research and may aid in the development of early diagnosis, prevention strategies, and pharmacotherapy tools for this particular patient population.

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.

Cinnamomi ramulus (CR, Guizhi in Chinese) and Zingiberis rhizoma recens (ZRR, Shengjiang in Chinese) are couplet medicines (drug pairs or drug combinations) in traditional Chinese medicine prescriptions. They contain a high amount of volatile oils which endow them with unique flavors and pharmacological activities. Extraction methods have a great influence on the extraction efficiency and composition of volatile oils. Firstly, the volatile oils of CR and ZRR were extracted by steam distillation (SD) and analyzed by GC-MS to obtain their chemical profiles. In total, 35 and 55 compounds were identified in the volatile oils of CR and ZRR, respectively. In order to find a suitable extraction method for the couplet medicine CR-ZRR, subsequently, steam distillation (SD), azeotropic distillation (AD) and supercritical fluid extraction (SFE) were applied to extract the volatile oils from CR-ZRR. The average extraction yields by SD, AD and SFE were 0.573%, 0.62% and 2.135%, respectively. The chemical composition of the volatile oils was then analyzed by GC-MS. In total, 73, 59 and 71 compounds were identified from the extracts obtained by SD, AD and SFE, respectively. Principal component analysis (PCA) and OPLS-DA showed that citral, (E)-cinnamaldehyde dimethyl acetal, zingiberene, cinnamaldehyde and β-sesquiphellandrene were the main contributors to distinguish the volatile oils that were obtained by different processes. Considering the chemical diversity and the total content of the main bioactive components of the volatiles oils, SD was more suitable for CR-ZRR volatile oil extraction. This study provides a basis for elucidating the chemical composition and suitable extraction method for the volatile oils of CR-ZRR.

Resonance is commonly used for in situ measurement of the dynamic elastic modulus to evaluate the strength of concrete samples. Many researchers are also exploring the application of this convenient measurement technology for safety monitoring. Nevertheless, the presence of cracks and variations in curing conditions within samples can impact the resonance frequency range, potentially leading to potential inaccuracies in measurements. In order to improve the measurement accuracy of resonance frequency, this study introduces the Generalized S-Transform (GST) algorithm for measuring the dynamic elastic modulus, which utilizes its high time-frequency resolution to scan the power peak-point in non-stationary and transient excitation signals to determine the resonance frequency. For concrete materials with lower consistency, the experimental results verify the high accuracy of this algorithm in measuring resonance frequency compared with Fast Fourier Transform (FFT). This provides a reference for using the algorithm to measure the dynamic elastic modulus in civil engineering applications, such as buildings and bridges.

Transplanted organs experience several episodes of ischemia and ischemia-reperfusion. The graft injury resulting from ischemia-reperfusion (IRI) remains a significant obstacle to the successful survival of transplanted grafts. Temperature significantly influences cellular metabolic rates because biochemical reactions are highly sensitive to temperature changes. Consequently, lowering the temperature could reduce the degradative reactions triggered by ischemia. In mitigating IRI in liver grafts, the potential protective effect of localized hypothermia on the liver prior to blood flow obstruction has yet to be explored. In this study, we applied local hypothermia to mouse donor livers for a specific duration before stopping blood flow to liver lobes, a procedure called “liver precooling”. Mouse donor liver temperature in control groups was controlled at 37 °C. Subsequently, the liver donors were preserved in cold University of Wisconsin solution for various durations followed by orthotopic liver transplantation. Liver graft injury, function and inflammation were assessed at 1 and 2 days post-transplantation. Liver precooling exhibited a significant improvement in graft function, revealing more than a 47% decrease in plasma aspartate transaminase (AST) and alanine aminotransferase (ALT) levels, coupled with a remarkable reduction of approximately 50% in liver graft histological damage compared to the control group. The protective effects of liver precooling were associated with the preservation of mitochondrial function, a substantial reduction in hepatocyte cell death, and a significantly attenuated inflammatory response. Taken together, reducing the cellular metabolism and enzymatic activity to a minimum level before ischemia protects against IRI during transplantation.

The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a lightweight attention module is introduced into MnasNet, and the improved MobileNetV3 is employed as the backbone network to enhance the feature extraction efficiency. Then, PANet and SPPnet are incorporated to aggregate the features from multiple effective feature layers. Subsequently, to reduce latency and improve efficiency, YOMO-Runwaynet generates a single optimal prediction for each object, eliminating the need for non-maximum suppression (NMS). Finally, experimental results on embedded devices demonstrate that YOMO-Runwaynet achieves a detection accuracy of over 89.5% on the ATD (Aerovista Runway Dataset), with a pixel error rate of less than 0.003 for runway keypoint detection, and an inference speed exceeding 90.9 FPS. These results indicate that the YOMO-Runwaynet algorithm offers high accuracy and real-time performance, providing effective support for the visual navigation of fixed-wing aircraft.

With the development and widespread adoption of smart machines, researchers across various disciplines and fields are exploring the contributions of computers and intelligent machines to human science and society through interdisciplinary collaboration. In this study, we investigated the potential applications of artificial intelligence and multi-source big data in the selection and design of urban greenways, using the city of Nanjing as a case study. Utilizing computer vision technology and the DeepLabV3+ neural network model, we analyzed over 320,000 street view images and 530,000 fine-grained urban data points from Nanjing. We also trained the place space material quantification model using the Street Space Greening Structure (S.S.G.S) dataset. This dataset not only achieved high-precision semantic segmentation but also surpassed previous datasets in predicting greenery at the street level. The performance metrics for this model are as follows: MIoU is 0.6344, Recall is 0.7287, and Precision is 0.8074. Through Robust regression, we identified several micro and macro-level factors influencing the Panoramic View Green View Index (PVGVI). The results indicate that multiple factors have significant positive or negative effects on PVGVI. This research not only provides new decision-making tools for landscape architecture and urban planning but also opens new avenues for applying artificial intelligence in urban environmental studies.

Tibetan sheep are vital to the ecosystem and livelihood of the Tibetan Plateau; however, traditional breeding methods limit their production and growth. Modern molecular breeding techniques are required to improve these traits. This study identified a single nucleotide polymorphism (SNP) in myostatin (MSTN) and Callipyge in Tibetan sheep. The findings indicated notable associations between MSTN genotypes and growth traits including birth weight (BW), body length (BL), chest width (ChW), and chest circumference (ChC), as well as a particularly strong association with cannon circumference (CaC) at 2 months of age. Conversely, Callipyge polymorphisms did not have a significant impact on Tibetan sheep. Moreover, the analyses revealed a significant association between sex and BW or hip width (HW) at 2 months of age and ChW, ChC, and CaC at 4 months of age. Furthermore, the study’s results suggested that the genotype of MSTN as a GA was associated with a notable sex effect on BW, while the genotype of Callipyge (CC) showed a significant impact of sex on CaC at 2 months of age. These results indicated that the SNP of MSTN could potentially serve as a molecular marker for early growth traits in Tibetan sheep.

A small molecule disulfide unit technology platform based on dynamic thiol exchange chemistry at the cell membrane has the potential for drug delivery. However, the alteration of the CSSC dihedral angle of the disulfide unit caused by diverse substituents directly affects the effectiveness of this technology platform as well as its own chemical stability. The highly stable open-loop relaxed type disulfide unit plays a limited role in drug delivery due to its low dihedral angle. Here, we have built a novel disulfide unit starship based on the 3,4,5-trihydroxyphenyl skeleton through trigonometric bundling. The intracellular delivery results showed that the trigonometric bundling of the disulfide unit starship effectively promoted cellular uptake without any toxicity, which is far more than 100 times more active than that of equipment with a single disulfide unit in particular. Then, the significant reduction in cell uptake capacity (73–93%) using thiol erasers proves that the trigonometric bundling of the disulfide starship is an endocytosis-independent internalization mechanism via a dynamic covalent disulfide exchange mediated by thiols on the cell surface. Furthermore, analysis of the molecular dynamics simulations demonstrated that trigonometric bundling of the disulfide starship can significantly change the membrane curvature while pushing lipid molecules in multiple directions, resulting in a significant distortion in the membrane structure and excellent membrane permeation performance. In conclusion, the starship system we built fully compensates for the inefficiency deficiencies induced by poor dihedral angles.

Opsins are a class of transmembrane proteins encoded by opsin genes, and they play a variety of functional roles. Short wavelength-sensitive opsin 2 (sws2), one of the five classes of visual opsin genes, mainly senses blue light. Previous research has indicated that sws2 is essential for melanocyte formation in fish; however, its specific role in skin color differentiation remains to be elucidated. Here, we identified the sws2 gene in a prized reef-dwelling fish, Plectropomus leopardus. The full-length P. leopardus sws2 gene encodes a protein consisting of 351 amino acids, and exhibits substantial homology with other fish species. The expression of the sws2 gene was widespread across P. leopardus tissues, with high expression in eye and skin tissues. Through immunohistochemistry and in situ hybridization analyses, we discovered that the sws2 gene was primarily localized in the rod and cone cells of the retina, and epidermal cells of the skin. Furthermore, dsRNA interference was used for sws2 gene knockdown in living P. leopardus to elucidate its function in skin color differentiation. Black-color-related genes, melanin contents, and tyrosinase activity in the skin significantly decreased after sws2 knockdown (p < 0.05), but red-color-related genes and carotenoid and lutein contents significantly increased (p < 0.05). Retinoic acid injection produced the opposite results. Our results suggested that the sws2 gene influences P. leopardus skin color regulation by affecting vitamin synthesis and melanin-related gene expression levels. This study establishes a foundation for elucidating the molecular mechanisms by which sws2 regulates melanocyte formation in fish skin.

Carbon dioxide (CO2) injection for enhanced oil recovery (EOR) has attracted great attention due to its potential to increase ultimate recovery from mature oil reservoirs. Despite the reported efficiency of CO2 in enhancing oil recovery, the high mobility of CO2 in porous media is one of the major issues faced during CO2 EOR projects. Foam injection is a proven approach to overcome CO2 mobility problems such as early gas breakthrough and low sweep efficiency. In this experimental study, we investigated the foam performance of a commercial anionic surfactant, alpha olefin sulfonate (AOS), in carbonate core samples for gas mobility control and oil recovery. Bulk foam screening tests demonstrated that varying surfactant concentrations above a threshold value had an insignificant effect on foam volume and half-life. Moreover, foam stability and capacity decreased with increasing temperature, while variations in salinity over the tested range had a negligible influence on foam properties. The pressure drop across a brine-saturated core sample increased with an increasing concentration of surfactant in the injected brine during foam flooding experiments. Co-injection of CO2 and AOS solution at an optimum concentration and gas fractional flow enhanced oil recovery by 6–10% of the original oil in place (OOIP).

The Lanzhou lily is a regionally distinctive vegetable; the emergence of lightly processed lilies has addressed the inconvenience of consuming fresh lilies. However, the cleaning and impurity removal during the processing of lightly processed lily may strip off its original protective barrier and affect the edible quality. As one of the preservation methods, tea tree essential oil (TTEO) has the characteristics of being green, safe, and efficient preservative properties. This study focused on investigating the effects of different concentrations (25 μL/L, 50 μL/L, and 100 μL/L) of TTEO on the quality and microbiological safety of lightly processed lily. The results showed that compared with the control, appropriate concentrations of TTEO treatment could delay weight loss, improve appearance, firmness, and sensory quality, and maintain microbiological safety with the best effect observed at 50 μL/L. Meanwhile, TTEO treatment induced phenylalanine ammonia-lyase activity, thereby increasing the total phenolic content. Furthermore, TTEO enhanced the superoxide dismutase (SOD) and ascorbate peroxidase (APX) activity, which reduced O2-· production rate and H2O2 content. TTEO inhibited lipoxygenase (LOX) activity, reducing the relative conductivity and malondialdehyde content, thereby delaying lipid peroxidation and quality deterioration. This indicates that TTEO could enhance antioxidant capacity by regulating reactive oxygen species (ROS) metabolism and delay the quality deterioration of lightly processed lily by inhibiting lipid peroxidation.

Monoterpenes are a class of volatile organic compounds that play crucial roles in imparting floral and fruity aromas to Muscat-type grapes. However, our understanding of the regulatory mechanisms underpinning monoterpene biosynthesis in grapes, particularly following abscisic acid (ABA) treatment, remains elusive. This study aimed to explore the impact of exogenous ABA on monoterpene biosynthesis in Ruiduhongyu grape berries by employing Headspace Solid-Phase Micro-Extraction Gas Chromatography–Mass Spectrometry (HS-SPME/GC–MS) analysis and transcriptome sequencing. The results suggested significant differences in total soluble solids (TSS), pH, and total acid content. ABA treatment resulted in a remarkable increase in endogenous ABA levels, with concentrations declining from veraison to ripening stages. ABA treatment notably enhanced monoterpene concentrations, particularly at the E_L37 and E_L38 stages, elevating the overall floral aroma of grape berries. According to the variable gene expression patterns across four developmental stages in response to ABA treatment, the E_L37 stage had the largest number of differential expressed genes (DEGs), which was correlated with a considerable change in free monoterpenes. Furthermore, functional annotation indicated that the DEGs were significantly enriched in primary and secondary metabolic pathways, underlining the relationship between ABA, sugar accumulation, and monoterpene biosynthesis. ABA treatment upregulated key genes involved in the methylerythritol phosphate (MEP) pathway, enhancing carbon allocation and subsequently impacting terpene synthesis. This study also identified transcription factors, including MYB and AP2/ERF families, potentially modulating monoterpene and aroma-related genes. Weighted gene co-expression network analysis (WGCNA) linked ABA-induced gene expression to monoterpene accumulation, highlighting specific modules enriched with genes associated with monoterpene biosynthesis; one of these modules (darkgreen) contained genes highly correlated with most monoterpenes, emphasizing the role of ABA in enhancing grape quality during berry maturation. Together, these findings provide valuable insights into the multifaceted effects of exogenous ABA on monoterpene compounds and grape berry flavor development, offering potential applications in viticulture and enology.

Intermittent rivers and ephemeral streams are crucial for the water cycle and ecosystem services, yet they are often neglected by managers and researchers, especially in headwater areas. This oversight has caused a lack of comprehensive basemaps for these vital river systems. In headwater regions, water bodies are typically sparse and disconnected, with narrow and less distinct channels. Therefore, we propose a novel hybrid method that integrates topographic data and remote sensing imagery to delineate river networks. Our method reestablishes connectivity among sparsely distributed water bodies through topographic pairs, enhances less distinct channel features using the gamma function, and converts topographic and water indices data into a weighted graph to determine optimal channels with the A* algorithm. The topographic and water indices data are derived from the Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) and an average composite of the Modified Normalized Difference Water Index (MNDWI), respectively. In the upper Lancang-Mekong River basin, our method outperformed five publicly available DEM datasets, achieving over 91% positional accuracy within a 30 m buffer. This hybrid method enhances positional accuracy and effectively connects sparse water bodies in headwater areas, offering promising applications for delineating intermittent rivers and ephemeral streams and providing baseline information for these river systems.

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite’s position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method’s potential for practical applications.

Rural tourism is a new driving force for achieving rural revitalization and development, promoting rural economic prosperity, and serving as a new industrial approach to achieving the transformation and upgrading of farmers’ livelihoods. This paper focuses on Manas County as the research subject and employs farmer questionnaire interviews and participatory rural appraisal methods to categorize villages into four types: “scenic area-dependent” villages, “industry-dependent” villages, “folklore-dependent” villages, and “suburban-dependent” villages. Based on the sustainable livelihood analysis framework proposed by the Department for International Development, we developed a sustainable livelihood framework for farm households in Manas County and analyzed the effects of rural tourism on farm households’ livelihood capital and livelihood strategies as the watershed of the rural tourism takes shape, taking livelihood capital and livelihood strategies as the entry point. The factors influencing the livelihood capital and livelihood options of farm households in the context of rural tourism are analyzed. The results show that: (1) The overall livelihood capital of farmers engaging in rural tourism is significantly greater than that of farmers not participating in rural tourism. Additionally, variations exist in the livelihood capital of different types of rural tourism within villages. (2) Participation in rural tourism significantly influences farmers’ choices of livelihood strategies. Compared with individuals who do not engage in rural tourism, those involved in rural tourism are more likely to opt for self-management livelihood strategies. The proportion of self-managed farmers in villages based around scenic areas is the highest. (3) The effect of rural tourism on farm households in Manas County is generally positive, and various types of villages yield different impact effects. (4) Participation in rural tourism significantly increases the possibility of farmers choosing self-management livelihood strategies, while it reduces the possibility of farmers choosing agriculture-oriented livelihood strategies. In all villages, except those dependent on industry, the decision to engage in rural tourism significantly impacts farmers’ selection of self-management livelihood strategies.

The overuse of traditional antibiotics has resulted in bacterial resistance and seriously compromised the therapeutic efficacy of traditional antibiotics, making the exploration of new antimicrobials particularly important. Several studies have shown that bioactive peptides have become an important source of new antimicrobial drugs due to their broad-spectrum antibacterial action and lack of susceptibility to resistance. In this study, a novel bioactive peptide Nigrosin-6VL was characterised from the skin secretion of the golden cross band frog, Odorrana andersonii, by using the ‘shotgun’ cloning strategy. Modifications on the Rana Box of Nigrosin-6VL revealed its critical role in antimicrobial functions. The peptide analogue, 2170-2R, designed to preserve the Rana Box structure while enhancing cationicity, exhibited improved therapeutic efficacy, particularly against Gram-negative bacteria, with a therapeutic value of 45.27. Synergistic studies demonstrated that 2170-2R inherits the synergistic antimicrobial activities of the parent peptides and effectively enhances the antimicrobial capacity of cefepime and gentamicin against both planktonic cells and biofilms. Specifically, 2170-2R can synergise effectively with cefepime and gentamicin against different strains of P. aeruginosa biofilms. Consequently, 2170-2R holds promise as a potent antimicrobial agent developed to combat infections induced by Pseudomonas aeruginosa.

Tourists’ environmentally responsible behavior (TERB) is one of the key areas in tourism as it concerns tourism sustainability that further influences a destination’s competitiveness and image. This study sought to deepen the formation of TERB through tourists’ degree of environmental perception, mediated by their feeling of awe towards the environment, and moderated by their level of face consciousness. Data were collected from tourists in Tibet and the findings indicated that awe exerted partial mediation on the tourists’ perception-behavior relationship. Furthermore, face consciousness moderated the mediated relationship of humanistic environmental perception, awe, and TERB. This study adds value to existing tourism studies by revealing the influences of awe on constructing tourists’ attitude in sustainable tourism. From a practical perspective, the findings offer insights for destination management organizations (DMOs) in promoting tourists’ environmentally responsible behavior that fosters sustainable tourism development.

Porins are crucial proteins located in the outer membrane that directly influence antimicrobial resistance mechanisms and virulence in bacteria. In this study, a porin gene (Vp-porin) was cloned in V. parahaemolyticus, and the function of Vp-Porin in biological characteristics and virulence was investigated. The results of sequence analysis showed that Vp-Porin is highly conserved in Vibrio spp., and the predicted 3D structure showed it could form a 20-strand transmembrane β-barrel domian. Membrane permeabilization provides evidence that the membrane integrity of ∆Vp-porin was damaged and the sensitivity to tetracycline, polymyxin B, rifampicin and cephalothin of ∆Vp-porin obviously increased. In addition, loss of Vp-porin damaged motility due to downregulated flagellar synthesis. In addition, ∆Vp-porin exhibited attenuated cytotoxicity to Tetrahymena. The relative survival rate of Tetrahymena infection with ∆Vp-porin was 86%, which is much higher than that with WT (49%). Taken together, the results of this study indicate that Vp-Porin in V. parahaemolyticus plays various roles in biological characteristics in membrane integrity, antimicrobial resistance and motility and contributes to virulence.

In general, the design of a safe and rational laneway support scheme signifies a crucial prerequisite for ensuring the security and efficiency of mining exploitation in mines. Nevertheless, the conventional empirical support system for mining laneways faces challenges in assessing the rationality of support methods, which can compromise the safety and reliability of the laneways. To address this issue, the safety factor was incorporated into research on laneway support, and a safety evaluation method for laneway support in line with the safety factor was established. In light of the data from a specific iron mine laneway in central China, the CRITIC method was employed to preprocess the sample data. Going one step further, a Bayesian algorithm was utilized to optimize the hyperparameters of the CatBoost model, followed by proposing a prediction model based on the BO-CatBoost model for evaluating laneway safety factors of plain shotcrete support. Furthermore, the performance indexes, such as the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R2), the variance accounts for (VAF), and the a-20 index, were determined to examine the predictive performance of each proposed model. In contrast to the other models, the BO-CatBoost model demonstrated the optimal predictive output item for safety factors with the lowest RMSE and MAE, the largest R2 and VAF, and an appropriate a-20 index value of 0.5688, 0.4074, 0.9553, 95.25%, and 0.9167 in the test set, respectively. Therefore, the BO-CatBoost model was proven to be the most appropriate machine learning method that can more accurately predict the safety factor, which will provide a novel approach for optimizing laneway support design and laneway safety evaluation.

In response to the issues of low outdoor thermal comfort and poor ventilation environment in Beijing Hutong, this paper proposes a rapid intelligent optimization method combining Pix2Pix (Image-to-Image Translation with Conditional Adversarial Networks) with a genetic algorithm. Firstly, the architectural types of the research objects are highly refined and summarized into four traditional building types. Then, they are placed in the site with open spaces in a certain proportion, and a multi-objective optimization model for the UTCI (Universal Thermal Climate Index) and building area is constructed using a genetic algorithm, generating and iteratively optimizing the spatial layout of the building population. Finally, Pix2Pix is used to learn and train a large number of Hutong combination samples, rapidly generating the UTCI and ventilation results, which serve as the optimization objectives to obtain the optimal solution set for Hutong spatial forms. Compared with traditional empirical design methods, this method allows for a rapid and efficient traversal of vast solution spaces, intelligently generating Hutong renovation schemes that balance cultural heritage and healthy comfort. The research results demonstrate that this method can quickly find (26.4 times faster than traditional performance simulation methods) that the reasonable proportions of Siheyuan, Sanheyuan, Erheyuan, new buildings, and empty spaces in the Da Yuan Hutong in Beijing should be controlled at 11.8%, 16.9%, 23.8%, 33.8%, and 13.7%, respectively. Meanwhile, the building density should be maintained between 0.5 and 0.58, and the floor area ratio should be kept between 0.96 and 1.14. This significantly improves outdoor comfort, enhances the living environment of the Hutong, and promotes sustainable urban development.

Herein, a green biocatalytic approach using lipase as a catalyst has been developed for the synthesis of 3-selanyl-isoflavones through the selenylation/cyclization of 2-hydroxyphenyl enaminones and diphenyl di-selenide under mild conditions. The environmentally friendly method reached high yields of 87–95% in a short time at 30 °C, with 17 examples of 3-selanyl-isoflavones successfully prepared. Furthermore, we have investigated the possible mechanisms underlying this reaction.

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability.

Rapid urbanization in the Songhua River Basin (SRB), a crucial ecological barrier in China and Northeast Asia, has led to the degradation of ecosystem service functions and a decline in their value, thereby posing a significant threat to regional ecological security. Clarifying the complex coupling coordination relationship between urbanization and ecosystem services (ESs) and identifying the spatiotemporal heterogeneity of their interactions will facilitate the high-quality and coordinated development of urbanization and ESs in the SRB. This study employed a systems approach, treating urbanization and ESs as overarching systems and delineating different aspects of urbanization and ecosystem service functions as subsystems within these systems. The spatiotemporal characteristics of urbanization and the ecosystem service value (ESV) in the SRB from 1985 to 2021 were revealed. The coupling coordination relationship and the spatiotemporal heterogeneity of the interactions between urbanization and ESs in the SRB at both the system and subsystem levels were analyzed using the coupling coordination degree (CCD) model and the spatiotemporal geographically weighted regression (GTWR) model. The findings indicated that during the study period: (1) The urbanization index of SRB rose from 0.09 to 0.34, while the ESV experienced a decrease from 2091.42 × 107 CNY to 2002.44 × 107 CNY. (2) The coupling coordination degree (CCD) between urbanization and ESs in the SRB at both the system and subsystem levels increased significantly, generally transitioning from the moderately unbalanced to the basically balanced stage. Areas with high CCD values were mainly distributed in ecological function areas and low-level urbanized areas, while areas with low CCD values were mainly distributed in grassland ecological degradation areas, ecologically fragile areas, resource-dependent old industrial cities, and highly urbanized areas. (3) The subsystems of urbanization had an overall negative impact on Ess, with varying trends, but the spatial distribution pattern of the interactions remained relatively stable. Conversely, the subsystems of ESs all exhibited a trend of initially strengthening and then weakening their negative impacts on urbanization, and the spatial distribution pattern was highly correlated with the spatial distribution pattern of ESV in the SRB.

In the current context of rapid Internet of Things (IoT) and cloud computing technology development, the Single Packet Authorization (SPA) protocol faces increasing challenges, such as security threats from Distributed Denial of Service (DDoS) attacks. To address these issues, we propose the Advanced Network-Hiding Access Control (AHAC) framework, designed to enhance security by reducing network environment exposure and providing secure access methods. AHAC introduces an independent control surface as the access proxy service and combines it with a noise generation mechanism for encrypted access schemes, replacing the traditional RSA signature method used in SPA protocols. This framework significantly improves system security, reduces computational costs, and enhances key verification efficiency. The AHAC framework addresses several limitations inherent in SPA: users need to know the IP address of resources in advance, exposing the resource address to potential attacks; SPA’s one-way authentication mechanism is insufficient for multi-level authentication in dynamic environments; deploying the knocking module and protected resources on the same host can lead to resource exhaustion and service unavailability under heavy loads; and SPA often uses high-overhead encryption algorithms like RSA2048. To counter these limitations, AHAC separates the Port Knocking module from the access control module, supports mutual authentication, and implements an extensible two-way communication mechanism. It also employs ECC and ECDH algorithms, enhancing security while reducing computational costs. We conducted extensive experiments to validate AHAC’s performance, high availability, extensibility, and compatibility. The experiments compared AHAC with traditional SPA in terms of time cost and performance.

With the gradual emphasis on health by people, the research on the pathogenesis of endemic diseases has become increasingly in-depth. Through analyzing the environmental selenium characteristics and conducting a comparative study in typical areas of Chinese loess and black soil in this paper, it is concluded that the environmental selenium in the two regions has different characteristics. The soil in the loess area has the characteristics of high alkalinity, low selenium, and relatively high selenium availability, and the crops are selenium-deficient, while the soil in the black soil area has the characteristics of high organic matter, low selenium availability, and relatively high selenium in crops. The research concluded that the environmental occurrence mechanism of Keshan disease in the loess area and the black soil area is different. Keshan disease can be induced in both low-selenium and sufficient-selenium environments, and environmental selenium should be one of the inducing factors of Keshan disease. This research provides a reference for predicting the areas where Keshan disease occurs and for disease prevention, and it can also serve in the prevention and control of endemic diseases.

Deoxynivalenol (DON) is a global contaminant found in crop residues, grains, feed, and animal and human food. Biodegradation is currently the best solution for addressing DON pollution. However, efficient detoxification bacteria or enzymes that can be applied in complex matrices are lacking. The aim of this study was to isolate a DON-detoxifying probiotic strain with a high degradation rate, a good safety profile, and a clear genetic background. One hundred and eight bacterial strains were isolated from 300 samples collected from a school farm and surrounding livestock farms. A new DON-degrading strain, Lactobacillus rhamnosus MY-1 (L. rhamnosus MY-1), with a degradation rate of 93.34% after 48 h and a comprehensive degradation method, was identified. Then, MY-1 at a concentration of 1 × 108 CFU/mL was administered to mice in a chronic intoxication experiment for 28 days. The experimental group showed significantly higher weight gain and exhibited good production performance compared to the control group. The length of the ileal villi in the experimental group was significantly longer than that in the control group. The expression of pro-inflammatory cytokines decreased, while the expression of anti-inflammatory factors increased in the experimental group. Whole-genome analysis revealed that most of the MY-1 genes were involved in carbohydrate metabolism and membrane transport, with a cluster of secondary metabolite genes encoding antimicrobial properties. In summary, this study successfully identified a Lactobacillus strain with good safety performance, high DON degradation efficiency, and a clear genetic background, providing a new approach for the treatment of DON contamination.

In order to study the impact of risk aversion characteristics of enterprises on supply chain risk transmission, the risk aversion utility function is introduced, and the risk elasticity coefficient is used to construct a supplier-dominated low-carbon supply chain risk transmission model. Simulation analysis is conducted to investigate the transmission of emission reduction and revenue risks caused by internal and external contingent risk factors. The study reveals that under conditions of market demand uncertainty, the risk transmission effect is unaffected by the risk aversion characteristics of members in the low-carbon supply chain. While the risk-aversion characteristics of suppliers can decrease their own profit risk, they have a negative effect on the profit risk of manufacturers and the emission reduction risk of supply chain nodes. There exists a critical threshold for the impact of the risk-aversion degree of suppliers on their own emission reduction risk transmission effect. When this threshold is exceeded, the emission reduction risk decreases with increasing risk aversion intensity of suppliers, and vice versa. The risk aversion characteristics of manufacturers can weaken the negative effect of supplier risk aversion on the fluctuation risk of manufacturer profits, but they exacerbate the emission reduction risk transmission effect of manufacturers under asymmetric information influence. The findings have important theoretical and practical implications for supply chain risk management.

Gastrin-releasing peptide receptor (GRPR), overexpressed in many solid tumors, is a promising imaging marker and therapeutic target. Most reported GRPR-targeted radioligands contain a C-terminal amide. Based on the reported potent antagonist D-Phe-Gln-Trp-Ala-Val-Gly-His-Leu-NHOH, we synthesized C-terminal hydroxamate-derived [68Ga]Ga-LW02075 ([68Ga]Ga-DOTA-pABzA-DIG-D-Phe-Gln-Trp-Ala-Val-Gly-His-Leu-NHOH) and [68Ga]Ga-LW02050 ([68Ga]Ga-DOTA-Pip-D-Phe-Gln-Trp-Ala-Val-Gly-His-Leu-NHOH), and compared them with the closely related and clinically validated [68Ga]Ga-SB3 ([68Ga]Ga-DOTA-pABzA-DIG-D-Phe-Gln-Trp-Ala-Val-Gly-His-Leu-NHEt). Binding affinities (Ki) of Ga-SB3, Ga-LW02075, and Ga-LW02050 were 1.20 ± 0.31, 1.39 ± 0.54, and 8.53 ± 1.52 nM, respectively. Both Ga-LW02075 and Ga-LW02050 were confirmed to be GRPR antagonists by calcium release assay. Imaging studies showed that PC-3 prostate cancer tumor xenografts were clearly visualized at 1 h post injection by [68Ga]Ga-SB3 and [68Ga]Ga-LW02050 in PET images, but not by [68Ga]Ga-LW02075. Ex vivo biodistribution studies conducted at 1 h post injection showed that the tumor uptake of [68Ga]Ga-LW02050 was comparable to that of [68Ga]Ga-SB3 (5.38 ± 1.00 vs. 6.98 ± 1.36 %ID/g), followed by [68Ga]Ga-LW02075 (3.97 ± 1.71 %ID/g). [68Ga]Ga-SB3 had the highest pancreas uptake (37.3 ± 6.90 %ID/g) followed by [68Ga]Ga-LW02075 (17.8 ± 5.24 %ID/g), while the pancreas uptake of [68Ga]Ga-LW02050 was only 0.53 ± 0.11 %ID/g. Our data suggest that [68Ga]Ga-LW02050 is a promising PET tracer for detecting GRPR-expressing cancer lesions.

Soil acidification is a significant form of agricultural soil degradation, which is accelerated by irrational fertilizer application. Sweetpotato and wheat rotation has emerged as an important rotation system and an effective strategy to optimize nutrient cycling and enhance soil fertility in hilly areas, which is also a good option to improve soil acidification and raise soil quality. Studying the effects of different fertilization regimes on soil acidification provides crucial data for managing it effectively. An eight-year field experiment explored seven fertilizer treatments: without fertilization (CK), phosphorus (P) and potassium (K) fertilization (PK), nitrogen (N) and K fertilization (NK), NP fertilization (NP), NP with K chloride fertilization (NPK1), NP with K sulfate fertilization (NPK2), and NPK combined with organic fertilization (NPKM). This study focused on the soil acidity, buffering capacity, and related indicators. After eight years of continuous fertilization in the sweetpotato–wheat rotation, all the treatments accelerated the soil acidification. Notably, N fertilization reduced the soil pH by 1.30–1.84, whereas N-deficient soil showed minimal change. Organic fertilizer addition resulted in the slowest pH reduction among the N treatments. Both N-deficient (PK) and organic fertilizer addition (NPKM) significantly increased the soil cation exchange capacity (CEC) by 8.83% and 6.55%, respectively, compared to CK. Similar trends were observed for the soil-buffering capacity (pHBC). NPK2 increased the soil K+ content more effectively than NPK1. NPKM reduced the sodium and magnesium content compared to CK, with the highest magnesium content among the treatments at 1.60 cmol·kg−1. Regression tree analysis identified the N input and soil magnesium and calcium content as the primary factors influencing the pHBC changes. Structural equation modeling showed that the soil pH is mainly influenced by the soil ammonium N content and pHBC, with coefficients of −0.28 and 0.29, respectively. Changes in the soil pH in the sweetpotato–wheat rotation were primarily associated with the pHBC and N input, where the CEC content emerged as the main factor, modulated by magnesium and calcium. Long-term organic fertilization enhances the soil pHBC and CEC, slowing the magnesium reduction and mitigating soil acidification in agricultural settings.

Platelet-rich fibrin (PRF), the coagulated plasma of fractionated blood, is widely used to support tissue regeneration in dentistry, and the underlying cellular and molecular mechanisms are increasingly being understood. Periodontal connective tissues steadily express CXCL8, a chemokine that attracts granulocytes and lymphocytes, supporting homeostatic immunity. Even though PRF is considered to dampen inflammation, it should not be ruled out that PRF increases the expression of CXCL8 in gingival fibroblasts. To test this hypothesis, we conducted a bioassay where gingival fibroblasts were exposed to PRF lysates and the respective serum. We show here that PRF lysates and, to a lesser extent, PRF serum increased the expression of CXCL8 by the gingival fibroblasts, as confirmed by immunoassay. SB203580, the inhibitor of p38 mitogen-activated protein kinase, reduced CXCL8 expression. Consistently, PRF lysates and, to a weaker range, the PRF serum also caused phosphorylation of p38 in gingival fibroblasts. Assuming that PRF is a rich source of growth factors, the TGF-β receptor type I kinase inhibitor SB431542 decreased the PRF-induced expression and translation of CXCL8. The findings suggest that PRF lysates and the respective serum drive CXCL8 expression by activating TGF-β and p38 signaling in gingival fibroblasts.

The identification and expression of germ cells are important for studying sex-related mechanisms in fish. The vasa gene, encoding an ATP-dependent RNA helicase, is recognized as a molecular marker of germ cells and plays a crucial role in germ cell development. Silurus asotus, an important freshwater economic fish species in China, shows significant sex dimorphism with the female growing faster than the male. However, the molecular mechanisms underlying these sex differences especially involving in the vasa gene in this fish remain poorly understood. In this work, the vasa gene sequence of S. asotus (named as Savasa) was obtained through RT-PCR and rapid amplification of cDNA end (RACE), and its expression in embryos and tissues was analyzed using qRT-PCR and an in situ hybridization method. Letrozole (LT) treatment on the larvae fish was also conducted to investigate its influence on the gene. The results revealed that the open reading frame (ORF) of Savasa was 1989 bp, encoding 662 amino acids. The SaVasa protein contains 10 conserved domains unique to the DEAD-box protein family, showing the highest sequence identity of 95.92% with that of Silurus meridionalis. In embryos, Savasa is highly expressed from the two-cell stage to the blastula stage in early embryos, with a gradually decreasing trend from the gastrula stage to the heart-beating stage. Furthermore, Savasa was initially detected at the end of the cleavage furrow during the two-cell stage, later condensing into four symmetrical cell clusters with embryonic development. At the gastrula stage, Savasa-positive cells increased and began to migrate towards the dorsal side of the embryo. In tissues, Savasa is predominantly expressed in the ovaries, with almost no or lower expression in other detected tissues. Moreover, Savasa was expressed in phase I–V oocytes in the ovaries, as well as in spermatogonia and spermatocytes in the testis, implying a specific expression pattern of germ cells. In addition, LT significantly upregulated the expression of Savasa in a concentration-dependent manner during the key gonadal differentiation period of the fish. Notably, at 120 dph after LT treatment, Savasa expression was the lowest in the testis and ovary of the high concentration group. Collectively, findings from gene structure, protein sequence, phylogenetic analysis, RNA expression patterns, and response to LT suggest that Savasa is maternally inherited with conserved features, serving as a potential marker gene for germ cells in S.asotus, and might participate in LT-induced early embryonic development and gonadal development processes of the fish. This would provide a basis for further research on the application of germ cell markers and the molecular mechanisms of sex differences in S. asotus.

Understanding solar radiation in urban street spaces is crucial for comprehending residents’ environmental experiences and enhancing their quality of life. However, existing studies rarely focus on the patterns of urban street solar radiation over time and across different urban and suburban areas. In this study, street view images from the summers of 2013 and 2019 in Shanghai were used to calculate solar radiation in urban street spaces. The results show a general decrease in street solar radiation in 2019 compared to 2013, with an average drop of 12.34%. The decrease was most significant in October (13.47%) and least in May (11.71%). In terms of solar radiation data gathered from street view sampling points, 76.57% showed a decrease, while 23.43% showed an increase. Spatially, solar radiation decreased by 79.66% for every additional 1.5 km from the city centre. In summary, solar radiation generally shows a decreasing trend, with significant variations between different areas. These findings are vitally important for guiding urban planning, optimising green infrastructure, and enhancing the urban ecological environment, further promoting sustainable urban development and improving residents’ quality of life.

Welcome to our Special Issue, “Advances in Breast Cancer Research and Treatment” of Life, where we have embarked on a comprehensive exploration of groundbreaking studies that advance our understanding and management of breast cancer [...]

Endothelial hyperpermeability is pivotal in sepsis-associated multi-organ dysfunction. Increased von Willebrand factor (vWF) plasma levels, stemming from activated platelets and endothelium injury during sepsis, can bind to integrin αvβ3, exacerbating endothelial permeability. Hence, targeting this pathway presents a potential therapeutic avenue for sepsis. Recently, we identified isaridin E (ISE), a marine-derived fungal cyclohexadepsipeptide, as a promising antiplatelet and antithrombotic agent with a low bleeding risk. ISE’s influence on septic mortality and sepsis-induced lung injury in a mouse model of sepsis, induced by caecal ligation and puncture, is investigated in this study. ISE dose-dependently improved survival rates, mitigating lung injury, thrombocytopenia, pulmonary endothelial permeability, and vascular inflammation in the mouse model. ISE markedly curtailed vWF release from activated platelets in septic mice by suppressing vesicle-associated membrane protein 8 and soluble N-ethylmaleide-sensitive factor attachment protein 23 overexpression. Moreover, ISE inhibited healthy human platelet adhesion to cultured lipopolysaccharide (LPS)-stimulated human umbilical vein endothelial cells (HUVECs), thereby significantly decreasing vWF secretion and endothelial hyperpermeability. Using cilengitide, a selective integrin αvβ3 inhibitor, it was found that ISE can improve endothelial hyperpermeability by inhibiting vWF binding to αvβ3. Activation of the integrin αvβ3-FAK/Src pathway likely underlies vWF-induced endothelial dysfunction in sepsis. In conclusion, ISE protects against sepsis by inhibiting endothelial hyperpermeability and platelet-endothelium interactions.

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

Background: The metabolic-status-related mechanisms underlying the deterioration of the lung function in obese asthma have not been completely elucidated. Objective: This study aimed to investigate the basal metabolic rate (BMR) in patients with obese asthma, its association with the lung function, and its mediating role in the impact of obesity on the lung function. Methods: A 12-month prospective cohort study (n = 598) was conducted in a real-world setting, comparing clinical, body composition, BMR, and lung function data between patients with obese (n = 282) and non-obese (n = 316) asthma. Path model mediation analyses for the BMR and skeletal muscle mass (SMM) were conducted. We also explored the effects of the BMR on the long-term lung function in patients with asthma. Results: Patients with obese asthma exhibited greater airway obstruction, with lower FEV1 (1.99 vs. 2.29 L), FVC (3.02 vs. 3.33 L), and FEV1/FVC (65.5 vs. 68.2%) values compared to patients with non-obese asthma. The patients with obese asthma also had higher BMRs (1284.27 vs. 1210.08 kcal/d) and SMM (23.53 vs. 22.10 kg). Both the BMR and SMM mediated the relationship between obesity and the lung function spirometers (FEV1, %FEV1, FVC, %FVC, and FEV1/FVC). A higher BMR or SMM was associated with better long-term lung function. Conclusions: Our study highlights the significance of the BMR and SMM in mediating the relationship between obesity and spirometry in patients with asthma, and in determining the long-term lung function. Interventions for obese asthma should focus not only on reducing adiposity but also on maintaining a high BMR.

An all-fiber low-repetition-rate SESAM mode-locked fiber oscillator combined with a dispersion-managed active fiber loop produces a flexible GHz burst-mode laser source. The high-power output is then produced by amplifying the GHz burst-mode laser source using an all-fiber chirped-pulse amplification system. Then, the laser is compressed using a grating pair compressor; a maximum amplified power of 97 W is obtained. This results in a compressed high power of 82.07 W with a power stability RMS of 0.09% and beam quality better than 1.2. Accurate dispersion control allows for the production of a high-quality pulse duration of 265 fs.

A compound channel’s discharge capacity and boundary shear force can be predicted as a sum of the discharge capacity of different sub-regions once the apparent shear stress of the dividing line is reasonably quantified. The apparent shear stress was usually expressed as a coefficient multiplied by the difference between two squared velocities of two adjacent regions. This study investigated the range of the coefficient values and their influencing factors. Firstly, the optimal values of the coefficient were obtained based on experimental data. Then, comparisons between the optimal values and several parameters used in quantifying the apparent shear stress were conducted. The results show that the coefficient is mainly related to a morphological parameter of the floodplain and the ratio of resistance coefficients between the floodplain and the main channel. An empirical formula to calculate the coefficient was developed and introduced to calculate the flow discharge and boundary shear stress. Experimental data, including 142 sets of test data of symmetric-floodplain cases and 104 sets of one-floodplain cases, have been used to examine the prediction accuracy of discharges and boundary shear stress. For all these tests, the ranges of water depth of the main channel and the total width of the compound cross-section are about 0.05~0.30 m and 0.3~10 m, respectively; the Q range and the range of Froude numbers of the main channel flow are about 0.0033~1.11 m3/s and 0.3~2.3, respectively. Comparison with other methods and experimental data from both rigid and erodible compound channels indicated that the proposed method not only provided acceptable accuracy for the computation of discharge capacity and boundary shear stress of compound channels in labs but also gave insights for calculating discharge capacity in natural compound channels.

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction.

The direct hydrogenation of greenhouse gas CO2 to higher alcohols (C2+OH) provides a new route for the production of high-value chemicals. Due to the difficulty of C-C coupling, the formation of higher alcohols is more difficult compared to that of other compounds. In this review, we summarize recent advances in the development of multifunctional catalysts, including noble metal catalysts, Co-based catalysts, Cu-based catalysts, Fe-based catalysts, and tandem catalysts for the direct hydrogenation of CO2 to higher alcohols. Possible reaction mechanisms are discussed based on the structure–activity relationship of the catalysts. The reaction-coupling strategy holds great potential to regulate the reaction network. The effects of the reaction conditions on CO2 hydrogenation are also analyzed. Finally, we discuss the challenges and potential opportunities for the further development of direct CO2 hydrogenation to higher alcohols.

With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time–Frequency (TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time–Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time–Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network’s overall structure and improves the model’s training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection.

The deactivation of active sites caused by high-temperature sintering and the deposition of a large amount of carbon are the main difficulties in the reforming of methane using Ni-based catalysts. La, as a promoter, has an ameliorating effect on the defects of Ni-based catalysts. In this article, the mechanism of action of Ni-based catalysts with the introduction of the rare-earth metal additive La was reviewed, and the effects of La on the methane-reforming performance of Ni-based catalysts were examined. The physical properties, alkalinity, and activity of Ni-based catalysts can be enhanced by the use of the auxiliary agent La, which promotes the conversion of CH4 and CO2 as well as the selectivity towards H2 and CO formation in the reforming of methane. The reason why the Ni-based catalysts could maintain long-term stability in the presence of La was discussed. Furthermore, the current state of research on the introduction of different amounts of La in the reforming of methane at home and abroad was analyzed. It was found that 2–5 wt.% La is the most optimal quantity for improving the catalyst activity and stability, as well as the CO2 chemisorption. The limitations and directions for future research in the reforming of methane were discussed.

The catalytic efficiency of oxygen reduction catalysts is notably influenced by the dissociative adsorption of O2. We conducted a systematic investigation into the dissociative adsorption of O2 on the Ag3Au(111) surface using ab initio density functional theory (DFT) calculations. Our computational findings indicate that adsorption the configuration designated t-b-t exhibits favorable energetics on the Ag3Au(111) surface. Regarding the dissociation of O2, we identified a reasonable dissociation pathway, which proceeds from the initial t-b-t state to the creation of two oxygen atoms that occupy a set of neighboring fcc sites. Furthermore, our analysis indicates that the adsorption of O2 on the Ag3Au(111) surface is less favored thermodynamically and more difficult to dissociate than that on the Ag(111) surface. This study furnishes a theoretical framework elucidating the prospective utilization of Ag-Au alloy in the capacity of oxygen reduction catalysts.

The segmentation of floating algae is a hot topic in the field of marine environmental research. Given the vastness of coastal areas and complex environments, algae detection models must have both higher performance and lower deployment costs. However, relying solely on a single Convolutional Neural Network (CNN) or transformer structure fails to achieve this objective. In this paper, a novel real-time floating algae segmentation method using a distillation network (ADNet) is proposed, based on the RGB images. ADNet can effectively transfer the performance of the transformer-based teacher network to the CNN-based student model while preserving its lightweight design. Faced with complex marine environments, we introduce a novel Channel Purification Module (CPM) to simultaneously strengthen algae features and purify interference responses. Importantly, the CPM achieves this operation without increasing any learnable parameters. Moreover, considering the huge scale differences among algae targets in surveillance RGB images, we propose a lightweight multi-scale feature fusion network (L-MsFFN) to improve the student’s modeling ability across various scales. Additionally, to mitigate interference from low-level noises on higher-level semantics, a novel position purification module (PPM) is proposed. The PPM can achieve more accurate weight attention calculation between different pyramid levels, thereby enhancing the effectiveness of fusion. Compared to CNNs and transformers, our ADNet strikes an optimal balance between performance and speed. Extensive experimental results demonstrate that our ADNet achieves higher application performance in the field of floating algae monitoring tasks.

With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.