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Cyclocarya paliurus tea, also known as “sweet tea”, an herbal tea with Cyclocarya paliurus leaves as raw material, is famous for its unique nutritional benefits and flavor. However, due to the unique “bittersweet” of Cyclocarya paliurus tea, it is still unable to fully satisfy consumers’ high-quality taste experience and satisfaction. Therefore, this study aimed to explore metabolites in Cyclocarya paliurus leaves during their growth period, particularly composition and variation of sweet and bitter taste compounds, by combining multi-platform metabolomics analysis with an electronic tongue system and molecular docking simulation technology. The results indicated that there were significant differences in the contents of total phenols, flavonoids, polysaccharides, and saponins in C. paliurus leaves in different growing months. A total of 575 secondary metabolites were identified as potential active metabolites related to sweet/bitter taste using nontargeted metabolomics based on UHPLC-MS/MS analysis. Moreover, molecular docking technology was utilized to study interactions between the candidate metabolites and the sweet receptors T1R2/T1R3 and the bitter receptors T2R4/T2R14. Six key compounds with high sweetness and low bitterness were successfully identified by using computational simulation analysis, including cis-anethole, gluconic acid, beta-D-Sedoheptulose, asparagine, proline, and citrulline, which may serve as candidates for taste modification in Cyclocarya paliurus leaves. These findings provide a new perspective for understanding the sweet and bitter taste characteristics that contribute to the distinctive sensory quality of Cyclocarya paliurus leaves.

Product quality is a key factor affecting consumers’ willingness to buy, providing greater advantages to an enterprise than product price. This paper investigates the impact of two factors, price and quality, on the operational decisions of hybrid competing supply chains. Supply chain I, which consists of a manufacturer and a retailer, is a decentralized structure. Supply chain II, where the manufacturer and retailer are integrated, is a centralized structure. Quality investment and vertical shareholding are introduced into the decentralized supply chain. Models are constructed for three different scenarios, examining whether the manufacturer makes a quality investment and whether the retailer holds shares in the quality investment. By comparing the equilibrium results, solved by the Stackelberg game method, the following conclusions are drawn: (1) Quality investment and shareholding can enhance product quality and price. (2) The retail price in a centralized supply chain is consistently lower than that in a decentralized one, leading to generally higher total profits for centralized supply chain. (3) The total profit of the decentralized supply chain only exceeds that of the centralized ones when the degree of substitution between products is lower than 0.6285 and the quality effort cost factor is within a specific range. While centralized supply chain is generally more advantageous, decentralized supply chain can outperform him under specific conditions.

Retained placenta (RP) affects lactation and fertility in dairy cows and causes economic losses to the dairy industry. Therefore, screening for early warning of this disease is important. This study used multi omics techniques to reveal the metabolic differences of dairy cows before RP onset and to find potential warning markers. Fecal samples and serum samples of 90 healthy Holstein cows were collected 7 days pre-calving; 10 healthy and 10 RP cows were enrolled according to normal expulsion of fetal membranes after calving. Fecal samples were subjected to 16S rRNA sequencing and untargeted metabolomics analysis, while plasma was analyzed using targeted metabolomics. Pathogenic bacteria levels increased in the intestines of cows with RP compared to those in healthy cows. Lipid metabolites constituted the largest proportion of differential metabolites between feces and plasma. Six potential warning markers for RP in cows were identified, including two fecal microbiomics markers (Oscillospiraceae UCG-005 and Escherichia-Shigella), one fecal untargeted metabolomics marker (N-acetylmuramic acid), and three plasma targeted metabolomics markers (glycylcholic acid-3 sulfate, 7-ketolithocholic acid, and 12-ketolithocholic acid). These biomarkers can predict RP occurrence in the early perinatal period. These results lay a theoretical foundation for early nutritional intervention and pathogenesis research in dairy cows.

The frequency support capacity of power loads is essential for maintaining active power symmetry and balance between the generation and demand sides of power systems. As the proportion of renewable energy sources and power electronic equipment increases, the inertia on the power generation side decreases, highlighting the growing importance of frequency support on the load side. As it is generally believed that the active power balance of power systems determines the frequency stability, few studies have considered the effect of voltage variation on the frequency response dynamics. It is important to note that the node voltage keeps fluctuating throughout the frequency dynamic process, which affects the active power of loads and should not be neglected. Based on the aforementioned rationales, this paper endeavors to investigate the modeling of power load frequency response and analyze its support capability considering the voltage variation effect. This paper initially establishes the small-signal model of dynamic load under frequency dynamics, derives the transfer function relating active power to system frequency deviation, and subsequently develops its frequency response model. Subsequently, commencing with the ZIP model of static load, the power fluctuation of load nodes is derived from the influence of preceding nodes, and the frequency response model of the static load is formulated and its frequency support capacity is scrutinized. Based on this foundation, a comprehensive aggregation model of the complex load is constructed, and its frequency support capability is assessed using actual data. Finally, the proposed model and analysis results are validated through simulation, confirming their correctness and effectiveness.

We developed and experimentally realized a scheme of optical nonreciprocity (ONR) by using degenerate two-level atoms embedded in an optical ring cavity. For the degenerate transition Fg = 4 ↔ Fe = 3, we first studied the cavity-transmission property in different coupling field configurations and verified that under the strong-coupling regime, the single-dark-state peak formed by electromagnetically induced transparency (EIT) showed ONR. The stable ground-state Zeeman coherence for Λ-chains involved in the degenerate two-level system was found to be important in the formation of intracavity EIT. However, different from the three-level atom–cavity system, in the degenerate two-level system, the ONR effect based on intracavity EIT occurred only at a low probe intensity, because the cavity–atom coupling strength was weakened in the counter-propagating probe and coupling field configuration. Furthermore, ONR transmission with a high contrast and linewidth-narrowing was experimentally demonstrated.

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA.

Land use/cover change (LUCC) is a vital aspect representing global change and humans’ impact on Earth’s surface. This study utilized the ESRI Land Cover 2020 and China Land Cover Dataset (CLCD), along with historical imagery from Google Earth, to develop a method for the assessment of land use data quality. Based on the assessment, the CLCD was updated to generate an improved Re-CLCD for the Pan-Pearl River Basin (PPRB) from 1985 to 2020, and to analyze LUCC in the PPRB over the past 35 years. The results indicate the following: (1) Among the seven land uses, built-up land experienced the most dramatic change, followed by cropland, forestland, grassland, shrubland, waterbody, and bare land, with notable increases in built-up land and forestland, and rapid decreases in cropland, grassland, and shrubland. (2) The magnitude of land use changed very widely, with the highest change in the Pearl River Delta, followed by small coastal river basins in southern Guangdong and western Guangxi, the Dongjiang River Basin, the Hanjiang River Basin, the Xijiang River Basin, the Beijiang River Basin, and lastly, Hainan Island. (3) The largest increase happened in built-up land, with a total increase of 12,184 km2, mainly due to the occupation of cropland and forestland, corresponding to the highest decrease in cropland, with a net loss of 10,435 km2, which was primarily converted to forestland and built-up land. The study results are valuable in providing a scientific basis for policy overhaul regarding land resources and management to safeguard ecological balance and promote sustainable development in the Pan-Pearl River Basin.

Doping inorganic acid ions represents a promising pathway to improving the photocatalytic activity of TiO2, and oxygen vacancy has been regarded as the determinant factor for photocatalytic activity. A series of samples doped with Cl−, NO3−, and SO42− was prepared via a simple sol–gel method. Two different oxygen vacancies in the crystal layer of NO3−/TiO2 and Cl−/TiO2 were found, and those are [Ti3+]-V0-[Ti3+] and [Ti3+]-Cl, respectively. The photocurrent of NO3−/TiO2 with [Ti3+]-V0-[Ti3+] is significantly greater than that of Cl−/TiO2 with [Ti3+]-Cl. The least oxygen vacancy is in the gel layer of SO42−/TiO2, and the negligible photocurrent is due to difficulty in forming a stable sol. Furthermore, the process conditions for the application of TiO2 were investigated in this work. The optimal process parameters are to adjust the solution to pH = 3 during sol–gel preparation, to adopt 550 °C as the calcination temperature, and to use an alkaline electrolyte, while the rest of the preparation conditions remain unchanged. This work reveals a new avenue for designing efficient photocatalysts for air pollutant degradation.

Compared to traditional biochar (BC), nano-biochar (NBC) boasts superior physicochemical properties, promising extensive applications in agriculture, ecological environments, and beyond. Due to its strong adsorption and migration properties, NBC may carry nutrients or pollutants to deeper soil layers or even groundwater, causing serious environmental risks. Nevertheless, the migration rules and mechanisms of NBC in soil are still unclear. Therefore, this study employed soil column migration experiments to systematically explore the migration rules and mechanisms of NBC under various flow rates, initial soil water contents, soil depths, and soil textures. The results showed that regulated by smaller particle size differences and greater surface charges, NBC exhibited a stronger migration ability compared with traditional BC. As the soil texture transitioned from fine to coarse, the migration capability of NBC significantly improved, driven by both pore structure and interaction forces as described by the DLVO theory. The migration ability of NBC was also greatly boosted as the soil transitioned from saturated to unsaturated conditions, primarily because of preferential flow. When the flow rate increased from 70% KS to 100% KS and 130% KS, the migration ability of NBC also increased accordingly, as changes in injection flow rates altered the velocity distribution of pore water. NBC in 25 cm soil columns was more prone to shallow retention compared with 10 cm soil columns, resulting in weaker overall migration ability. In addition, through fitting of the two-site kinetic model and related parameters, the penetration curves of NBC under various variable conditions were effectively characterized. These findings could offer valuable insights for NBC’s future efficient, rational, and sustainable utilization, facilitating the evaluation and mitigation of its potential environmental risks.

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data.

Selective laser melting (SLM) technology is a promising additive manufacturing technology. However, due to the numerous influencing factors in this complex process, a reliable real-time method is needed to monitor the forming process of SLM. The molten pool is the smallest forming unit in the SLM process, the consistency of which can effectively reflect the quality of the printing process. By using a coaxial optical path structure and a compound amplifier circuit, high-speed acquisition of molten pool radiation can be realized. Next, single factor analysis and orthogonal experimentation were used to investigate the influence levels of key process parameters on the radiation of molten pool. In addition, numerical simulation was carried out with the same parameter setting schemes, the results of which are consistent with those in radiation detection experiments. It is shown that the laser power has the greatest effect on the radiation of the molten pool, while the scanning speed and the hatch spacing have little effect on the radiation. Finally, the positioning experiment involving the small hole structure was carried out, and the experimental results showed that the device could accurately locate the position coordinates of the given hole structure.

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper.

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC.

Seed storage underpins global agriculture and the seed trade and revealing the mechanisms of seed aging is essential for enhancing seed longevity management. Safflower is a multipurpose oil crop, rich in unsaturated fatty acids that are at high risk of peroxidation as a contributory factor to seed aging. However, the molecular mechanisms responsible for safflower seed viability loss are not yet elucidated. We used controlled deterioration (CDT) conditions of 60% relative humidity and 50 °C to reduce germination in freshly harvested safflower seeds and analyzed aged seeds using biochemical and molecular techniques. While seed malondialdehyde (MDA) and fatty acid content increased significantly during CDT, catalase activity and soluble sugar content decreased. KEGG analysis of gene function and qPCR validation indicated that aging severely impaired several key functional and biosynthetic pathways including glycolysis, fatty acid metabolism, antioxidant activity, and DNA replication and repair. Furthermore, exogenous sucrose and diethyl aminoethyl hexanoate (DA-6) treatment partially promoted germination in aged seeds, further demonstrating the vital role of impaired sugar and fatty acid metabolism during the aging and recovery processes. We concluded that energy metabolism and genetic integrity are impaired during aging, which contributes to the loss of seed vigor. Such energy metabolic pathways as glycolysis, fatty acid degradation, and the tricarboxylic acid cycle (TCA) are impaired, especially fatty acids produced by the hydrolysis of triacylglycerols during aging, as they are not efficiently converted to sucrose via the glyoxylate cycle to provide energy supply for safflower seed germination and seedling growth. At the same time, the reduced capacity for nucleotide synthesis capacity and the deterioration of DNA repair ability further aggravate the damage to DNA, reducing seed vitality.

The mechanical strength of rice culm, an essential factor for lodging resistance and yield maintenance, is influenced by the composition and structure of the cell wall. In this study, we characterized a rice brittle culm mutant 22 (bc22), derived from LR005 through ethyl methanesulfonate (EMS) mutagenesis. The bc22 culm exhibited increased fragility and reduced mechanical strength compared to LR005. The mutant displayed pleiotropic effects, including a shorter plant height and panicle length, a smaller grain size, and the absence of the glume hairs. Scanning electron microscopy revealed a decrease in cell density and a looser structure in the bc22 culms. Biochemical analysis demonstrated a significant increase in hemicellulose content and a marked reduction in lignin content in the culm of bc22. Genetic analysis indicated that the brittle culm trait was governed by a single recessive gene. After employing bulked segregant analysis (BSA), whole-genome resequencing, and MutMap methods, LOC_Os02g25230 was identified as the candidate gene responsible for bc22. In bc22, a point mutation from proline (Pro) to leucine (Leu) in its coding region led to the pleiotropic phenotype. A complementation test further confirmed that the missense mutation causing the proline to leucine amino acid substitution in LOC_Os02g25230 was causative of the observed bc22 phenotype. Additionally, gene expression analysis showed that BC22 had higher expression levels in the culms, leaves, and spikelets compared to the roots. Taken together, our findings indicate that BC22 is a pleiotropic gene, and the influence of BC22 on brittleness may be associated with cell wall biosynthesis in rice culm.

As an emerging additive manufacturing technology with potential, selective laser melting (SLM) technology is one of the current mainstream additive manufacturing processes. Many complex conditions affect the quality of the SLM products, such as extremely short forming time, rapidly changing forming environment, and micron-level melting area. Numerical simulation and a printing experiment were used to study the influence of typical technological parameters on the forming quality of SLM products, where TA1 was used as the printing material. The typical technological parameters mainly considered laser power, laser scanning speed, hatch spacing, and laser energy density. Simulation analysis was based on the maximum temperature and the size of the molten pool, and the printing experiment was focused on the parameter design methods for typical processes and the forming quality of printed products. The results show the following: (1) An increase in laser power and laser energy density can significantly improve the forming quality. However, excessive input energy will lead to poor surface quality; (2) Scanning speed and hatch spacing need to be selected according to the size of the molten pool, which mainly influences the lap rate of the molten pool. The results of this study may provide some guidance for parameter setting and forming quality optimization in SLM manufacturing process.

The autonomous shipping industry is increasingly focusing on enhancing the safety and reliability of software-based systems. Conducting a risk assessment is a requirement for demonstrating the safety equivalence of autonomous ships based on such systems to conventional vessels. Traditional risk assessment models, however, primarily focus on hardware failures, often overlooking potential software-related failures and functional inadequacies. This study proposes a framework integrating Software Failure Mode and Effects Analysis (FMEA), System–Theoretic Process Analysis (STPA), and Bayesian Network (BN) for risk identification of autonomous ship software systems. The results of a case study reveal that the framework sufficiently addresses the multifaceted nature of risks related to software in autonomous ships. Based on the findings of this study, we suggest the need for standardization of software architecture development in the autonomous ship industry and highlight the necessity for an enhanced understanding of AI-specific risks and the development of tailored risk assessment methodologies.

Zearalenone (ZEN) is a mycotoxin produced by Fusarium spp., which commonly and severely contaminate food/feed. ZEN severely affects food/feed safety and reduces economic losses owing to its carcinogenicity, genotoxicity, reproductive toxicity, endocrine effects, and immunotoxicity. To explore efficient methods to detoxify ZEN, we identified and characterized an efficient ZEN-detoxifying microbiota from the culturable microbiome of Pseudostellaria heterophylla rhizosphere soil, designated Bacillus amyloliquefaciens D-1. Its highest ZEN degradation rate reached 96.13% under the optimal condition. And, D-1 can almost completely remove ZEN (90 μg·g−1) from coix semen in 24 h. Then, the D-1 strain can detoxify ZEN to ZEM, which is a new structural metabolite, through hydrolyzation and decarboxylation at the ester group in the lactone ring and amino acid esterification at C2 and C4 hydroxy. Notably, ZEM has reduced the impact on viability, and the damage of cell membrane and nucleus DNA and can significantly decrease the cell apoptosis in the HepG2 cell and TM4 cell. In addition, it was found that the D-1 strain has no adverse effect on the HepG2 and TM4 cells. Our findings can provide an efficient microbial resource and a reliable reference strategy for the biological detoxification of ZEN.

Liver fibrosis refers to a complex inflammatory response caused by multiple factors, which is a known cause of liver cirrhosis and even liver cancer. As a valuable medicine food homology herb, saffron has been widely used in the world. Saffron is commonly used in liver-related diseases and has rich therapeutic and health benefits. The therapeutic effect is satisfactory, but its mechanism is still unclear. In order to clarify these problems, we planned to determine the pharmacological effects and mechanisms of saffron extract in preventing and treating liver fibrosis through network pharmacology analysis combined with in vivo validation experiments. Through UPLC-Q-Exactive-MS analysis, a total of fifty-six nutrients and active ingredients were identified, and nine of them were screened to predict their therapeutic targets for liver fibrosis. Then, network pharmacology analysis was applied to identify 321 targets for saffron extract to alleviate liver fibrosis. Functional and pathway enrichment analysis showed that the putative targets of saffron for the treatment of hepatic fibrosis are mainly involved in the calcium signaling pathway, the HIF-1 signaling pathway, endocrine resistance, the PI3K/Akt signaling pathway, lipid and atherosclerosis, and the cAMP signaling pathway. Based on the CCl4-induced liver fibrosis mice model, we experimentally confirmed that saffron extract can alleviate the severity and pathological changes during the progression of liver fibrosis. RT-PCR and Western blotting analysis confirmed that saffron treatment can prevent the CCl4-induced upregulation of HIF-1α, VEGFA, AKT, and PI3K, suggesting that saffron may regulate AKT/HIF-1α/VEGF and alleviate liver fibrosis.

The Western Pacific Coasts (WPC) are the outlets of many large Asian rivers. In recent years, the interplay of climate changes and human activities has persistently altered the suspended sediment concentrations (SSC) in the WPC, triggering substantial shifts in coastal ecosystems. However, the scarcity of coastal observation stations hampered comprehensive investigations at large scales. This study employed three representative SSC retrieval models and utilized Landsat images acquired from 1990 to 2020 to estimate the SSC in the WPC with a focused endeavor to dissect the intricate spatial and temporal variability of SSC in the region. The findings revealed the following insights: (1) The outcomes derived from the three distinct SSC models consistently manifested a discernible decreasing pattern in SSC changes over the past three decades across all six major estuaries (Liao River Estuary, Yellow River Estuary, Yangtze River Estuary, Hangzhou Bay, Pearl River Estuary, and Mekong River Estuary). (2) The seasonal attributes of the six major estuaries differed, primarily due to distinct dominant influencing factors like precipitation, upstream sediment load, wind, and tides. (3) Collectively, SSC tends to be relatively higher in the Yangtze River Estuary, Hangzhou Bay, and Yellow River Estuary, while the Pearl River and Mekong River Estuaries exhibit relatively lower levels. Notably, the SSC exhibited distinct spatial traits along the coastlines of different estuaries. (4) SSC in the non-estuarine regions along the WPC, a similar significant declining trend in SSC is observed as in the estuaries, albeit the rate of decline generally appeared to be less pronounced. Furthermore, regions with faster rates of SSC reduction are typically concentrated near major estuaries in the northern part of the Coasts. The decline in estuarine SSC plays an important role in the overall decrease in SSC across the WPC. These study outcomes held substantial significance for advancing the stability and sustainable evolution of the WPC.

The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum within the realm of combinatorial optimization, serving as a prevalent organizational structure in authentic production settings. Given that conventional scheduling approaches fall short of effectively addressing the intricate and ever-shifting production landscape of PFSP, this study proposes an end-to-end deep reinforcement learning methodology with the objective of minimizing the maximum completion time. To tackle PFSP, we initially model it as a Markov decision process, delineating pertinent states, actions, and reward functions. A notably innovative facet of our approach involves leveraging disjunctive graphs to represent PFSP state information. To glean the intrinsic topological data embedded within the disjunctive graph’s underpinning, we architect a policy network based on a graph isomorphism network, subsequently trained through proximal policy optimization. Our devised methodology is compared with six baseline methods on randomly generated instances and the Taillard benchmark, respectively. The experimental results unequivocally underscore the superiority of our proposed approach in terms of makespan and computation time. Notably, the makespan can save up to 183.2 h in randomly generated instances and 188.4 h in the Taillard benchmark. The calculation time can be reduced by up to 18.70 s for randomly generated instances and up to 18.16 s for the Taillard benchmark.

Orthogonal time frequency space (OTFS) is a novel modulation scheme that enables reliable communication in high-mobility environments. In this paper, we propose a Transformer-based channel estimation method for OTFS systems. Initially, the threshold method is utilized to obtain preliminary channel estimation results. To further enhance the channel estimation, we leverage the inherent temporal correlation between channels, and a new method of channel response prediction is performed. To enhance the accuracy of the preliminary results, we utilize a specialized Transformer neural network designed for processing time series data for refinement. The simulation results demonstrate that our proposed scheme outperforms the threshold method and other deep learning (DL) methods in terms of normalized mean squared error and bit error rate. Additionally, the temporal complexity and spatial complexity of different DL models are compared. The results indicate that our proposed algorithm achieves superior accuracy while maintaining an acceptable computational complexity.

Estimating object counts within a single image or video frame represents a challenging yet pivotal task in the field of computer vision. Its increasing significance arises from its versatile applications across various domains, including public safety and urban planning. Among the various object counting tasks, crowd counting is particularly notable for its critical role in social security and urban planning. However, intricate backgrounds in images often lead to misidentifications, wherein the complex background is mistaken as the foreground, thereby inflating forecasting errors. Additionally, the uneven distribution of crowd density within the foreground further exacerbates predictive errors of the network. This paper introduces a novel architecture with a three-branch structure aimed at synergistically incorporating hierarchical foreground information and global scale information into density map estimation, thereby achieving more precise counting results. Hierarchical foreground information guides the network to perform distinct operations on regions with varying densities, while global scale information evaluates the overall density level of the image and adjusts the model’s global predictions accordingly. We also systematically investigate and compare three potential locations for integrating hierarchical foreground information into the density estimation network, ultimately determining the most effective placement.Through extensive comparative experiments across three datasets, we demonstrate the superior performance of our proposed method.

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.

The panel block is a quite important “intermediate product” in the shipbuilding process. However, the assembly efficiency of the panel block assembly line is not high. Therefore, rational scheduling optimization is of great significance for improving shipbuilding efficiency. Currently, the processing sequence of the panel blocks in the panel block assembly line is mainly determined using heuristic and metaheuristic algorithms. However, these algorithms have limitations, such as small problem-solving capacity and low computational efficiency. To address these issues, this study proposes an end-to-end approach based on deep reinforcement learning to solve the scheduling problem of the ship’s panel block assembly line. First, a Markov decision model is established, and a disjunctive graph is creatively used to represent the current scheduling status of the panel block assembly line. Then, a policy function based on a graph isomorphism network is designed to extract information from the disjunctive graph’s state and train it using Proximal Policy Optimization algorithms. To validate the effectiveness of our method, tests on both real shipbuilding data and publicly available benchmark datasets are conducted. We compared our proposed end-to-end deep reinforcement learning algorithm with heuristic algorithms, metaheuristic algorithms, and the unimproved reinforcement learning algorithm. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of model performance and computation time. Moreover, our model exhibits strong generalization capabilities for larger instances.

Plant viruses, as obligate intracellular parasites, induce significant changes in the cellular physiology of host cells to facilitate their multiplication. These alterations often lead to the development of symptoms that interfere with normal growth and development, causing USD 60 billion worth of losses per year, worldwide, in both agricultural and horticultural crops. However, existing literature often lacks a clear and concise presentation of the key information regarding the mechanisms underlying plant virus-induced symptoms. To address this, we conducted a comprehensive review to highlight the crucial interactions between plant viruses and host factors, discussing key genes that increase viral virulence and their roles in influencing cellular processes such as dysfunction of chloroplast proteins, hormone manipulation, reactive oxidative species accumulation, and cell cycle control, which are critical for symptom development. Moreover, we explore the alterations in host metabolism and gene expression that are associated with virus-induced symptoms. In addition, the influence of environmental factors on virus-induced symptom development is discussed. By integrating these various aspects, this review provides valuable insights into the complex mechanisms underlying virus-induced symptoms in plants, and emphasizes the urgency of addressing viral diseases to ensure sustainable agriculture and food production.

With the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system’s capability to provide inertia support and frequency regulation during disturbance events. The wind turbines (WT), as the main representatives of renewable energy generation, should be more efficiently involved in the power system frequency regulation dynamics. However, optimal frequency regulation is difficult to achieve through the combined inertial control strategy of wind turbines because it greatly depends on control parameters and fluctuates in different scenarios. To cope with disturbance efficiently and quickly in different scenarios and obtain the optimal frequency regulation results, this paper presents an improved combined inertial intelligent control strategy of WT based on contractive autoencoder (CAE) and deep neural network (DNN). This method obtains the optimal parameters for combined inertial control using the particle swarm optimization (PSO) algorithm, then effectively extracts features from actual data using CAE followed by building a network model to predict the optimal combined inertial control parameters online. To verify and test the proposed method, it is applied in the IEEE 9-bus test system. The simulation results show that the method can obtain optimal control parameters with a faster computational time, good prediction accuracy, and generalization capability.

Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability density distribution of trip distance in each city, which enables us to construct long- and short-distance trip networks. To identify critical nodes within these networks, we employ the PageRank algorithm and categorize them using centrality and participation indices. Furthermore, we explore the factors that contribute to their influence and observe a clear hierarchical multi-centre structure in Chengdu’s trip networks, while no such phenomenon is evident in New York City’s. Our study provides insight into the impact of trip distance on important nodes within trip networks in both cities and serves as a reference for distinguishing between long and short taxi trips. Our findings also reveal substantial differences in network structures between the two cities, highlighting the nuanced relationship between network structure and socio-economic factors. Ultimately, our research sheds light on the underlying mechanisms shaping transportation networks in urban areas and offers valuable insights into urban planning and policy making.

In this paper, a photonic-enabled image rejection mixer (IRM) that features an ultrawideband self-interference cancellation (SIC) function and a compact configuration is proposed. The parameter tuning of SIC is realized in an optical domain, which avoids the use of electrically tuned devices with limited bandwidth and precision, so that high-precision parameter matching can be realized in the optical domain to realize deep and ultrawideband SIC. The key point of image rejection (IR) is to construct a pair of orthogonal local oscillation (LO) signals through DC-bias-induced phase shift. This not only avoids a high-frequency electrical 90-degree hybrid coupler (HC) applied in the traditional Hartley structure, but also compensates the phase deviation in the electrical intermediate frequency (IF) 90-degree HC flexibly, ensuring wideband and deep IR operation. The simulation results show that the proposed IRM can achieve ultrawideband SIC and IR with the simultaneous high-efficiency recovery of useful signals. They also verify that the scheme has good resistance to strong interference, and can cope with the phase imbalance of the IF 90-degree electrical HC, ensuring the good performance of the system, which has a wide application prospect in various in-band full-duplex (IBFD) systems.

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.

Rapid and accurate anomaly traffic detection is one of the most important research problems in cyberspace situational awareness. In order to improve the accuracy and efficiency of the detection, a two-stage anomaly detection method based on user preference features and a deep fusion model is proposed. First, a user-preference list of attack detection tasks is constructed based on the resilient distributed dataset. Following that, the detection tasks are divided into multiple stages according to the detection framework, which allows multiple worker hosts to work in parallel. Finally, a deep fusion classifier is trained using the features extracted from the input traffic data. Experimental results indicate that the proposed method achieves better detection accuracy compared to the existing typical methods. Furthermore, compared with stand-alone detection, the proposed method can effectively improve the time efficiencies of the model’s training and testing to a large extent. The ablation experiment justifies the use of the machine learning method.

Flavor, which mainly depends on volatile compounds, is an important index for evaluating the quality of clam sauce. This study investigated the volatile compounds in clam sauce prepared using four different methods and the influence of aroma characteristics. Fermenting a mixture of soybean koji and clam meat improved the flavor of the final product. Solid-phase microextraction (SPME) combined with gas chromatography-mass spectrometry (GC-MS) identified 64 volatile compounds. Nine key flavor compounds, namely, 3-methylthio-1-propanol, 2-methoxy-4-vinylphenol, phenylethyl alcohol, 1-octen-3-ol, α-methylene phenylacetaldehyde, phenyl-oxirane, 3-phenylfuran, phenylacetaldehyde, and 3-octenone, were selected using variable importance in projection (VIP). The results of the electronic nose and tongue detection of the aroma characteristics of the samples prepared by four different fermentation methods were consistent with those of GC-MS analysis. The clam sauce prepared by mixing soybean koji with fresh clam meat possessed better flavor and quality than that prepared via other methods.

Mn-Ce catalysts modified by Mo were loaded on low-density porous ceramics (LDPC) for simultaneous denitrification and dust removal. The Mn-Ce-Mo catalyst on LDPC had nearly 99% NOx conversion efficiency from 120 °C to 200 °C and still maintained more than 90% NOx conversion efficiency when the filtration velocity reached to 4 m/min. Mn-Ce-Mo catalysts/LDPC not only exhibited excellent catalytic performance at low temperature, they also exhibited good resistance to H2O and SO2. The NOx conversion efficiency remained above 89% at 160 °C when the flue gas contained 100 ppm SO2 and 7 vol.% H2O. The analysis of NH3-TPD and XPS confirmed that Mn2Ce1Ox catalysts modified with Mo had the stronger surface acidity and more adsorbed oxygen, leading to higher NH3-SCR activity and better resistance to SO2 and H2O.

With the construction of the Power Internet of Things (PIoT) in full swing as well as the development of wireless communication technology, the deployment of wireless sensor networks is the key to the intelligent transformation of power systems. This paper proposes an asymmetric double-layer wireless sensor network coverage scheme for the intelligent substation. We conducted field measurements in the substation to record the received power at different locations, which were compared with the prediction results of the simulation model established by Winprop to verify the effectiveness of the simulation method. Based on the simulation platform, two different simulation models of substation scenarios are built, including a 220 kV outdoor substation and a 110 kV GIS room. The coverage of received power and line-of-sight (LOS) transmission are analyzed for various nodes of different layers. Several node distribution planning methods are proposed and proven to be feasible according to the simulation results. The wireless coverage scheme can provide useful references for the implementation of PIoT in substations.

“Alto2” is a new biomass sorghum variety, which has the characteristics of fast growth, high growth, and strong cadmium (Cd) resistance, so it has the application prospect of soil remediation plants. In order to reveal the Cd resistance mechanism of this plant and pave the way for genetic breeding and cultivation of efficient remediation plants in the future, in this research, through the determination of Cd content in various tissues of sorghum under Cd stress and the physicochemical response combined with RNA-Seq analysis, the mechanism of Cd resistance of “Alto2” was initially revealed. The results show biomass sorghum “Alto2” was mainly connected with aboveground and underground parts through the MAPK signaling pathway and plant hormone signaling pathway, and transmit stress signal in response to Cd stress. Chelase and metal-binding proteins may be the functional genes mainly responsible for Cd enrichment and transport and regulated by stress signals. However, the expression of aboveground transporters was not significant. This may be because Cd in biomass sorghum is mainly concentrated in the underground part and is enriched by the chelation of secondary metabolites from plant roots by the cell wall leading to inhibition of aboveground transporter expression. The results of this study indicate that the biomass sorghum “Alto2” on Cd has high resistance, but the lack of the aboveground enrichment of transportability requires further research to improve the Cd transportability of this plant.

Shaped cooling holes have received considerable attention in recent years due to their potential to improve heat transfer while minimizing pressure drop. In this study, the effects of lobe-shaped cooling holes on film cooling performance and turbulent flow characteristics are investigated using large-eddy simulation (LES). The LES approach was validated by comparing the calculated film cooling effectiveness with experimental data. The results show that well-designed lobe-shaped cooling holes can improve the cooling efficiency by up to 37% and reduce the total pressure drop. Additionally, the cooling jets of lobe-shaped cooling holes have a smaller angle to the wall surface, indicating better wall attachment. However, poorly designed lobe-shaped cooling holes can reduce the cooling efficiency. Overall, this study suggests that lobe-shaped cooling holes are a promising technology for improving gas turbine engine performance.

Tomato chlorosis virus (ToCV) severely threatens tomato production worldwide. P27 is known to be involved in virion assembly, but its other roles in ToCV infection are unclear. In this study, we found that removal of p27 reduced systemic infection, while ectopic expression of p27 promoted systemic infection of potato virus X in Nicotiana benthamiana. We determined that Solanum lycopersicum catalases (SlCAT) can interact with p27 in vitro and in vivo and that amino acids 73 to 77 of the N-terminus of SlCAT represent the critical region for their interaction. p27 is distributed in the cytoplasm and nucleus, and its coexpression with SlCAT1 or SlCAT2 changes its distribution in the nucleus. Furthermore, we found that silencing of SlCAT1 and SlCAT2 can promote ToCV infection. In conclusion, p27 can promote viral infection by binding directly to inhibit anti-ToCV processes mediated by SlCAT1 or SlCAT2.

Since the hydraulic axial piston pump is the engine that drives hydraulic transmission systems, it is widely utilized in aerospace, marine equipment, civil engineering, and mechanical engineering. Operating safely and dependably is crucial, and failure poses a major risk. Hydraulic axial piston pump malfunctions are characterized by internal concealment, challenging self-adaptive feature extraction, and blatant timing of fault signals. By completely integrating the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model, a novel intelligent fault identification method is proposed in this paper. First, the status data are transformed into two dimensions in terms of time and frequency by using SWT. Second, the depth features of the time–frequency map are obtained and dimensionality reduction is carried out by using the deep feature mining capability of VGG11. Third, LSTM is added to provide the damage identification model for long-term memory capabilities. The Softmax layer is utilized for the intelligent evaluation of various damage patterns and health state. The proposed method is utilized to identify and diagnose five typical states, including normal state, swash plate wear, sliding slipper wear, loose slipper, and center spring failure, based on the externally observed vibration signals of a hydraulic axial piston pump. The results indicate that the average test accuracy for five typical state signals reaches 99.43%, the standard deviation is 0.0011, and the average test duration is 2.675 s. The integrated model exhibits improved all-around performance when compared to LSTM, LeNet-5, AlexNet, VGG11, and other typical models. The proposed method is validated to be efficient and accurate for the intelligent identification of common defects of hydraulic axial piston pumps.

Hydraulic axial piston pumps are the power source of fluid power systems and have important applications in many fields. They have a compact structure, high efficiency, large transmission power, and excellent flow variable performance. However, the crucial components of pumps easily suffer from different faults. It is therefore important to investigate a precise fault identification method to maintain reliability of the system. The use of deep models in feature learning, data mining, automatic identification, and classification has led to the development of novel fault diagnosis methods. In this research, typical faults and wears of the important friction pairs of piston pumps were analyzed. Different working conditions were considered by monitoring outlet pressure signals. To overcome the low efficiency and time-consuming nature of traditional manual parameter tuning, the Bayesian algorithm was introduced for adaptive optimization of an established deep learning model. The proposed method can explore potential fault feature information from the signals and adaptively identify the main fault types. The average diagnostic accuracy was found to reach up to 100%, indicating the ability of the method to detect typical faults of axial piston pumps with high precision.

Reasonable resource potential calculation is the basic premise for the country to formulate planning and carry out rural housing land consolidation, which can deepen the research on the economical and intensive use of rural construction land. This paper evaluates farmers’ area demands of rural housing land by using the consumption utility function, analyzes the spatial pattern characteristics of demand and potential, and puts forward suggestions for the resource optimization of rural housing land based on the survey data of 613 farmers and land use data in the Pinggu District of Beijing in 2005 and 2018. Research shows that the utilities of life, production, and the ecological are carried and measured by the corresponding internal land-use structure of rural housing land. The proportion of life land area was the largest, and the life and production land area decreased from plain to mountain, from 119.76 m2 to 89.07 m2 and from 44.87 m2 to 32.85 m2, respectively. The average area potential is 395.64 m2 and the change range is −30.35–1413.75 m2, which is basically in accordance with the normal distribution. The area potential can be divided into five grades, which are expressed by I, II, III, IV, and V in order of potential from small to large by using the natural fracture method. The utility needed should be enhanced and expanded, and the redundant utility should be removed and socialized in the village. The resource optimization and allocation of rural housing land should be promoted according to the idea of regionalization and classification. It provides scientific support for the reform of rural housing land.

Metformin is a first-line drug for the clinical treatment of type 2 diabetes; however, it always leads to gastrointestinal tolerance, low bioavailability, short half-life, etc. Liposome acts as an excellent delivery system that could reduce drug side effects and promote bioavailability. Hyodeoxycholic acid, a cholesterol-like structure, can regulate glucose homeostasis and reduce the blood glucose levels. As an anti-diabetic active ingredient, hyodeoxycholic acid modifies liposomes to make it overcome the disadvantages of metformin as well as enhance the hypoglycemic effect. By adapting the thin-film dispersion method, three types of liposomes with different proportions of hyodeoxycholic acid and metformin were prepared (HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips). Further, the liposomes were characterized, and the anti-type 2 diabetes activity of liposomes was evaluated. The results from this study indicated that three types of liposomes exhibited different characteristics—Excessive hyodeoxycholic acid decreased encapsulation efficiency and drug loading. In the in vivo experiments, liposomes could reduce the fasting blood glucose levels, improve glucose tolerance, regulate oxidative stress markers and protect liver tissue in type 2 diabetic mice. These results indicated that HDCA:ME-(1:1)-Lips was the most effective among the three types of liposomes prepared and showed better effects than metformin. Hyodeoxycholic acid can enhance the hypoglycemic effect of metformin and play a suitable role as an excipient in the liposome.

Organic dyes and heavy metals often coexist in industrial effluents, and their simultaneous removal is a grand challenge. Herein, a hydrochar and MgAl layered double hydroxide (HC–MgAlLDH) nanocomposite was prepared via a facile one-step hydrothermal route, and applied to remove anionic Congo red (CR), cationic Methylene blue (MB) and Pb(II) from aqueous solutions. The nanocomposite was formed by interweaving amorphous HC and crystalline MgAlLDH nanoplates and possessed more functional groups, lower zeta potential and larger specific surface area than uncomposited MgAlLDH. Batch removal experiments showed that the components HC and LDH dominated the CR and MB removals, respectively, whereas Pb(II) removal was conjointly controlled by the two components. The maximum Langmuir removal capacities of the nanocomposite to sole CR, MB, or Pb(II) were 348.78, 256.54 or 33.55 mg/g. In binary and ternary systems, the removal capacities of CR and MB only slightly decreased, while the capacity of Pb(II) increased by 41.13–88.61%. The increase was related to the coordination of Pb(II) with the sulfur-containing groups in dyes and the precipitation of PbSO4. Therefore, the simultaneous removal of CR, MB and Pb(II) was involved in a synergistic effect, including electrostatic adsorption, π–π interaction, coordination and precipitation. The present work shows that the HC–MgAlLDH nanocomposite has great potential for wastewater integrative treatment.

Students’ behaviors have a close relationship with their learning efficiencies, particularly about professional knowledge. Different types of behaviors should have different influences. Disclosing the special relationship between undergraduate students’ conscious safety behaviors in their laboratory experiments with their learning efficiencies is important for fostering them into professional talents. In this study, a course entitled “Advanced Methods of Materials Characterization” was arranged to contain three sections: theoretical learning in the classroom, eight characterization experiments in the laboratory in sequence, and self-training to apply the knowledge. In the final examination, eighteen percent was allocated to the examination questions about safety issues. The students’ scores for this section were associated with their total roll scores. Two quantitative relationships are disclosed. One is between the students’ final examination score (y) and their subjective consciousness of safety behaviors (x) in their laboratory experiments, as y = 5.56 + 4.83 x (R = 0.9192). The other is between their grade point average (y) and safety behavior evaluation (x) as y = 0.51 + 0.15 x (R = 0.7296). Undergraduate students’ behaviors in scientific laboratories need to be verified to have a close and positive relationship with their professional knowledge learning efficiencies. This offers a hint that improving students’ safety behaviors and enhancing their subjective safety awareness are conducive to improving their learning efficiency for professional knowledge.

The bile acid transport system is a natural physiological cycling process between the liver and the small intestine, occurring approximately 6–15 times during the day. There are various bile acid transporter proteins on hepatocytes that specifically recognize bile acids for transport. Therefore, in this paper, a novel liposome, cholic acid-modified irinotecan hydrochloride liposomes (named CA-CPT-11-Lip), was prepared based on the “Trojan horse” strategy. The liposomes preparation process was optimized, and some important quality indicators were investigated. The distribution of irinotecan hydrochloride in mice was then analyzed by high-performance liquid chromatography (HPLC), and the toxicity of liposomes to hepatocellular carcinoma cells (HepG-2) was evaluated in vitro. As a result, CA-CPT-11-Lip was successfully prepared. It was spherical with a particle size of 154.16 ± 4.92 nm, and the drug loading and encapsulation efficiency were 3.72 ± 0.04% and 82.04 ± 1.38%, respectively. Compared with the conventional liposomes (without cholic acid modification, named CPT-11-Lip), CA-CPT-11-Lip had a smaller particle size and higher encapsulation efficiency, and the drug accumulation in the liver was more efficient, enhancing the anti-hepatocellular carcinoma activity of irinotecan hydrochloride. The novel nanoliposome modified by cholic acid may help to expand the application of irinotecan hydrochloride in the treatment of hepatocellular carcinoma and construct the drug delivery system mode of drug liver targeting.

Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, thus performing well in road segmentation tasks. Existing multi-modal segmentation methods assume that all the inputs are well-aligned, and then the problem is converted to fuse feature maps from different modalities. However, there exist cases where sensors cannot be well-calibrated. In this paper, we propose a novel network named multi-modal cross-attention network (MMCAN) for multi-modal free-space detection with uncalibrated hyperspectral sensors. We first introduce a cross-modality transformer using hyperspectral data to enhance RGB features, then aggregate these representations alternatively via multiple stages. This transformer promotes the spread and fusion of information between modalities that cannot be aligned at the pixel level. Furthermore, we propose a triplet gate fusion strategy, which can increase the proportion of RGB in the multiple spectral fusion processes while maintaining the specificity of each modality. The experimental results on a multi-spectral dataset demonstrate that our MMCAN model has achieved state-of-the-art performance. The method can be directly used on the pictures taken in the field without complex preprocessing. Our future goal is to adapt the algorithm to multi-object segmentation and generalize it to other multi-modal combinations.

The structural, photophysical, and vibrational properties of a new hydroxyphenylamino Meldrum’s acid derivative, 3-((2-hydroxyphenylamino)methylene)-1,5-dioxaspiro[5.5]undecane-2,4-dione (HMD), were studied. The comparison of experimental and theoretical vibrational spectra can help understand basic vibration patterns and provides a better interpretation of IR spectra. The UV–Vis spectrum of HMD was computed using density functional theory (DFT)/B3LYP/6-311 G(d,p) basis set in the gas state, and the maximum wavelength was in accord with the experimental data. The molecular electrostatic potential (MEP) and Hirshfeld surface analysis confirmed O(1)–H(1A)···O(2) intermolecular hydrogen bonds in the HMD molecule. The natural bond orbital (NBO) analysis provided delocalizing interactions between π→π* orbitals and n→σ*/π* charge transfer transitions. Finally, the thermal gravimetric (TG)/differential scanning calorimeter (DSC) and the non-linear optical (NLO) properties of HMD were also reported.

Dairy cows with ketosis have high circulating beta-hydroxybutyric acid (BHBA) concentrations alongside which inflammation is concomitantly developed. Tryptophan (Trp) is an essential amino acid that participates in the regulation of the inflammatory response. However, the association between Trp metabolism and inflammation in dairy cows with ketosis remains unclear. Therefore, blood samples from healthy (n = 10) and ketotic (n = 10) primiparous dairy cows were collected at the calving date and the day of ketosis diagnosis (7 days in milk (7 DIM)). Serum levels of non-esterified fatty acids (NEFA), BHBA, haptoglobin (HP), serum amyloid A (SAA), lipopolysaccharide, and cortisol were analyzed. Tryptophan and its metabolites were quantified using liquid chromatography–tandem mass spectrometry. At 7 DIM, the concentrations of NEFA, BHBA, HP, and SAA were higher and the levels of Trp, kynurenine (KYN), indoleacetic acid, indole-3-lactic acid, and 3-indoxyl sulfate were lower in the dairy cows with ketosis compared with those in the healthy cows. However, the KYN/Trp and melatonin/Trp ratios increased in the cows with ketosis. At the calving date, the serum lipopolysaccharide levels did not differ between the healthy and ketotic cows, whereas the levels of NEFA, HP, and cortisol increased in the ketotic cows. Correlation analysis showed that Trp deficiency and elevated Trp metabolism in the dairy cows occurred during ketosis. Overall, our results suggest that abnormal Trp metabolism may contribute to the pathogenesis of ketosis.

A photonic approach to the cancellation of self-interference in the optical domain with fiber dispersion immunity and harmonic frequency down-conversion function is proposed based on an integrated, dual-parallel, dual-drive Mach–Zehnder modulator (DP-DMZM). A dual-drive Mach–Zehnder modulator (DMZM) is used as an optical interference canceller, which cancels the self-interference from the impaired signal before fiber transmission to avoid the effect of fiber transmission on the cancellation performance. Another DMZM is used to provide carrier-suppressed, local-oscillation (LO)-modulated, high-order double optical sidebands for harmonic frequency down-conversion to release the strict demand for high-frequency LO sources. By regulating the DC bias of the main modulator, the signal of interest (SOI) can be down-converted to the intermediated frequency (IF) band after photoelectric conversion with improved frequency-conversion efficiency, immunity to the fiber-dispersion-induced power-fading (DIPF) effect, and effective signal recovery. Theoretical analyses and simulation results show that the desired SOI in the X and K bands with a bandwidth of 500 MHz and different modulation formats can be down-converted to the IF frequency. The self-interference noise with the 2 GHz bandwidth is canceled, and successful signal recovery is achieved after a 10 km fiber transmission. The recovery performance of down-converted signals and the self-interference cancellation depth under different interference-to-signal ratios (ISRs) is also investigated. In addition, the compensation performance of DIPF is verified, and a 6 dB improvement in frequency conversion gain is obtained compared with previous work. The proposed scheme is compact, cost-effective, and thus superior in wideband self-interference cancellation, long-range signal transmission, and effective recovery of weak desired signals.

The aeroderivative gas turbine is widely used as it demonstrates many advantages. Adding hydrogen to natural gas fuels can improve the performance of combustion. Following this, the effects of hydrogen enrichment on combustion characteristics were analyzed in an aeroderivative gas turbine combustor using CFD simulations. The numerical model was validated with experimental results. The conditions of the constant mass flow rate and the constant energy input were studied. The results indicate that adding hydrogen reduced the fuel residues significantly (fuel mass at the combustion chamber outlet was reduced up to 60.9%). In addition, the discharge of C2H2 and other pollutants was reduced. Increasing the volume fraction of hydrogen in the fuel also reduced CO emissions at the constant energy input while increasing CO emissions at the constant fuel mass flow rate. An excess in the volume fraction of added hydrogen changed the combustion mode in the combustion chamber, resulting in fuel-rich combustion (at constant mass flow rate) and diffusion combustion (at constant input power). Hydrogen addition increased the pattern factor and NOx emissions at the outlet of the combustion chamber.

Semen coicis is not only a traditional Chinese medicine (TCM), but also a typical food in China, with significant medical and healthcare value. Because semen coicis is rich in starch and oil, it can be easily contaminated with Aspergillus flavus and its aflatoxins (AFs). Preventing and controlling the contamination of semen coicis with Aspergillus flavus and its aflatoxins is vital to ensuring its safety as a drug and as a food. In this study, the endosphere bacteria Pseudomonas palleroniana strain B-BH16-1 produced volatiles that strongly inhibited the mycelial growth and spore formation activity of A. flavus. Gas chromatography–mass spectrometry profiling revealed three volatiles emitted from B-BH16-1, of which 1-undecene was the most abundant. We obtained authentic reference standards for these three volatiles; these significantly reduced mycelial growth and sporulation in Aspergillus, with dimethyl disulfide showing the most robust inhibitory activity. Strain B-BH16-1 was able to completely inhibit the biosynthesis of aflatoxins in semen coicis samples during storage by emitting volatile bioactive components. The microscope revealed severely damaged mycelia and a complete lack of sporulation. This newly identified plant endophyte bacterium was able to strongly inhibit the sporulation and growth of Aspergillus and the synthesis of associated mycotoxins, thus not only providing valuable information regarding an efficient potential strategy for the prevention of A. flavus contamination in TCM and food, but potentially also serving as a reference in the control of toxic fungi.