Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Comprehensive regulatory networks for tomato organ development based on the genome and RNAome of MicroTom tomato

MicroTom has a short growth cycle and high transformation efficiency,and is a prospective model plant for studying organ development,metabolism,and plant–microbe interactions.Here,with a newly assembled reference genome for this tomato cultivar and abundant RNA-seq data derived from tissues of different organs/developmental stages/treatments,we constructed multiple gene co-expression networks,which will provide valuable clues for the identification of important genes involved in diverse regulatory pathways during plant growth,e.g.arbuscular mycorrhizal symbiosis and fruit development.Additionally,non-coding RNAs,including miRNAs,lncRNAs,and circRNAs were also identified,together with their potential targets.Interacting networks between different types of non-coding RNAs(miRNA-lncRNA),and non-coding RNAs and genes(miRNA-mRNA and lncRNA-mRNA)were constructed as well.Our results and data will provide valuable information for the study of organ differentiation and development of this important fruit.Lastly,we established a database(http://eplant.njau.edu.cn/microTomBase/)with genomic and transcriptomic data,as well as details of gene co-expression and interacting networks on MicroTom,and this database should be of great value to those who want to adopt MicroTom as a model plant for research.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Prevotella and succinate treatments altered gut microbiota,increased laying performance,and suppressed hepatic lipid accumulation in laying hens

Background This work aimed to investigate the potential benefits of administering Prevotella and its primary metabolite succinate on performance,hepatic lipid accumulation and gut microbiota in laying hens.Results One hundred and fifty 58-week-old Hyline Brown laying hens,with laying rate below 80%and plasma triglyceride(TG)exceeding 5 mmol/L,were used in this study.The hens were randomly allocated into 5 groups and subjected to one of the following treatments:fed with a basal diet(negative control,NC),oral gavage of 3 mL/hen saline every other day(positive control,PC),gavage of 3 mL/hen Prevotella melaninogenica(10^(7)CFU/mL,PM)or 3 mL/hen Prevotella copri(10^(7)CFU/mL,P.copri)every other day,and basal diet supplemented with 0.25%sodium succinate(Succinate).The results showed that PM and P.copri treatments significantly improved laying rate compared to the PC(P<0.05).The amount of lipid droplet was notably decreased by PM,P.copri,and Succinate treatments at week 4 and decreased by P.copri at week 8(P<0.05).Correspondingly,the plasma TG level in Succinate group was lower than that of PC(P<0.05).Hepatic TG content,however,was not significantly influenced at week 4 and 8(P>0.05).PM treatment increased(P<0.05)the mRNA levels of genes PGC-1βand APB-5B at week 4,and ACC and CPT-1 at week 8.The results indicated enhanced antioxidant activities at week 8,as evidenced by reduced hepatic malondialdehyde(MDA)level and improved antioxidant enzymes activities in PM and Succinate groups(P<0.05).Supplementing with Prevotella or succinate can alter the cecal microbiota.Specifically,the abundance of Prevotella in the Succinate group was significantly higher than that in the other 4 groups at the family and genus levels(P<0.05).Conclusions Oral intake of Prevotella and dietary supplementation of succinate can ameliorate lipid metabolism of laying hens.The beneficial effect of Prevotella is consistent across different species.The finding highlights that succinate,the primary metabolite of Prevotella,represents a more feasible feed additive for alleviating fatty liver in laying hens.

Multi-omics analysis reveals the molecular regulatory network underlying the prevention of Lactiplantibacillus plantarum against LPS-induced salpingitis in laying hens

Background Salpingitis is one of the common diseases in laying hen production, which greatly decreases the economic outcome of laying hen farming. Lactiplantibacillus plantarum was effective in preventing local or systemic inflammation, however rare studies were reported on its prevention against salpingitis. This study aimed to investigate the preventive molecular regulatory network of microencapsulated Lactiplantibacillus plantarum(MLP) against salpingitis through multi-omics analysis, including microbiome, transcriptome and metabolome analyses.Results The results revealed that supplementation of MLP in diet significantly alleviated the inflammation and atrophy of uterus caused by lipopolysaccharide(LPS) in hens(P < 0.05). The concentrations of plasma IL-2 and IL-10 in hens of MLP-LPS group were higher than those in hens of LPS-stimulation group(CN-LPS group)(P < 0.05). The expression levels of TLR2, MYD88, NF-κB, COX2, and TNF-α were significantly decreased in the hens fed diet supplemented with MLP and suffered with LPS stimulation(MLP-LPS group) compared with those in the hens of CN-LPS group(P < 0.05). Differentially expressed genes(DEGs) induced by MLP were involved in inflammation, reproduction, and calcium ion transport. At the genus level, the MLP supplementation significantly increased the abundance of Phascolarctobacterium, whereas decreased the abundance of Candidatus_Saccharimonas in LPS challenged hens(P < 0.05). The metabolites altered by dietary supplementation with MLP were mainly involved in galactose, uronic acid, histidine, pyruvate and primary bile acid metabolism. Dietary supplementation with MLP inversely regulates LPSinduced differential metabolites such as Lyso PA(24:0/0:0)(P < 0.05).Conclusions In summary, dietary supplementation with microencapsulated Lactiplantibacillus plantarum prevented salpingitis by modulating the abundances of Candidatus_Saccharimonas, Phascolarctobacterium, Ruminococcus_torques_group and Eubacterium_hallii_group while downregulating the levels of plasma metabolites, p-tolyl sulfate, o-cresol and N-acetylhistamine and upregulating S-lactoylglutathione, simultaneously increasing the expressions of CPNE4, CNTN3 and ACAN genes in the uterus, and ultimately inhibiting oviducal inflammation.

Rosemary extract improves egg quality by altering gut barrier function,intestinal microbiota and oviductal gene expressions in late-phase laying hens

Background Rosemary extract(RE)has been reported to exert antioxidant property.However,the application of RE in late-phase laying hens on egg quality,intestinal barrier and microbiota,and oviductal function has not been systematically studied.This study was investigated to detect the potential effects of RE on performance,egg quality,serum parameters,intestinal heath,cecal microbiota and metabolism,and oviductal gene expressions in late-phase laying hens.A total of 21065-week-old“Jing Tint 6”laying hens were randomly allocated into five treatments with six replicates and seven birds per replicate and fed basal diet(CON)or basal diet supplemented with chlortetracycline at 50 mg/kg(CTC)or RE at 50 mg/kg(RE50),100 mg/kg(RE100),and 200 mg/kg(RE200).Results Our results showed that RE200 improved(P<0.05)Haugh unit and n-6/n-3 of egg yolk,serum superoxide dismutase(SOD)compared with CON.No significant differences were observed for Haugh unit and n-6/n-3 of egg yolk among CTC,RE50,RE100 and RE200 groups.Compared with CTC and RE50 groups,RE200 increased serum SOD activity on d 28 and 56.Compared with CON,RE supplementation decreased(P<0.05)total cholesterol(TC)level.CTC,RE100 and RE200 decreased(P<0.05)serum interleukin-6(IL-6)content compared with CON.CTC and RE200 increased jejunal m RNA expression of ZO-1 and Occludin compared with CON.The biomarkers of cecal microbiota and metabolite induced by RE 200,including Firmicutes,Eisenbergiella,Paraprevotella,Papillibacter,and butyrate,were closely associated with Haugh unit,n-6/n-3,SOD,IL-6,and TC.PICRUSt2 analysis indicated that RE altered carbohydrate and amino acid metabolism of cecal microbiota and increased butyrate synthesizing enzymes,including 3-oxoacid Co A-transferase and butyrate-acetoacetate Co A-transferase.Moreover,transcriptomic analysis revealed that RE200 improved gene expressions and functional pathways related to immunity and albumen formation in the oviductal magnum.Conclusions Dietary supplementation with 200 mg/kg RE could increase egg quality of late-phase laying hens via modulating intestinal barrier,cecal microbiota and metabolism,and oviductal function.Overall,RE could be used as a promising feed additive to improve egg quality of laying hens at late stage of production.

Coated sodium butyrate ameliorates high‑energy and low‑protein diet induced hepatic dysfunction via modulating mitochondrial dynamics, autophagy and apoptosis in laying hens

Background Fatty liver hemorrhagic syndrome(FLHS),a fatty liver disease in laying hens,poses a grave threat to the layer industry,stemming from its ability to trigger an alarming plummet in egg production and usher in acute mortality among laying hens.Increasing evidence suggests that the onset and progression of fatty liver was closely related to mitochondria dysfunction.Sodium butyrate was demonstrated to modulate hepatic lipid metabolism,alle-viate oxidative stress and improve mitochondrial dysfunction in vitro and mice models.Nevertheless,there is limited existing research on coated sodium butyrate(CSB)to prevent FLHS in laying hens,and whether and how CSB exerts the anti-FLHS effect still needs to be explored.In this experiment,the FLHS model was induced by administering a high-energy low-protein(HELP)diet in laying hens.The objective was to investigate the effects of CSB on alleviating FLHS with a focus on the role of CSB in modulating mitochondrial function.Methods A total of 288 healthy 28-week-old Huafeng laying hens were arbitrarily allocated into 4 groups with 6 replicates each,namely,the CON group(normal diet),HELP group(HELP diet),CH500 group(500 mg/kg CSB added to HELP diet)and CH750 group(750 mg/kg CSB added to HELP diet).The duration of the trial encompassed a period of 10 weeks.Results The result revealed that CSB ameliorated the HELP-induced FLHS by improving hepatic steatosis and patho-logical damage,reducing the gene levels of fatty acid synthesis,and promoting the mRNA levels of key enzymes of fatty acid catabolism.CSB reduced oxidative stress induced by the HELP diet,upregulated the activity of GSH-Px and SOD,and decreased the content of MDA and ROS.CSB also mitigated the HELP diet-induced inflammatory response by blocking TNF-α,IL-1β,and F4/80.In addition,dietary CSB supplementation attenuated HELP-induced activation of the mitochondrial unfolded protein response(UPRmt),mitochondrial damage,and decline of ATPase activity.HELP diet decreased the autophagosome formation,and downregulated LC3B but upregulated p62 protein expression,which CSB administration reversed.CSB reduced HELP-induced apoptosis,as indicated by decreases in the Bax/Bcl-2,Caspase-9,Caspase-3,and Cyt C expression levels.Conclusions Dietary CSB could ameliorate HELP diet-induced hepatic dysfunction via modulating mitochondrial dynamics,autophagy,and apoptosis in laying hens.Consequently,CSB,as a feed additive,exhibited the capacity to prevent FLHS by modulating autophagy and lipid metabolism.

Decreased eggshell strength caused by impairment of uterine calcium transport coincide with higher bone minerals and quality in aged laying hens

Background Deteriorations in eggshell and bone quality are major challenges in aged laying hens.This study compared the differences of eggshell quality,bone parameters and their correlations as well as uterine physiologi-cal characteristics and the bone remodeling processes of hens laying eggs of different eggshell breaking strength to explore the mechanism of eggshell and bone quality reduction and their interaction.A total of 24074-week-old Hy-line Brown laying hens were selected and allocated to a high(HBS,44.83±1.31 N)or low(LBS,24.43±0.57 N)eggshell breaking strength group.Results A decreased thickness,weight and weight ratio of eggshells were observed in the LBS,accompanied with ultrastructural deterioration and total Ca reduction.Bone quality was negatively correlated with eggshell quality,marked with enhanced structures and increased components in the LBS.In the LBS,the mammillary knobs and effective layer grew slowly.At the initiation stage of eggshell calcification,a total of 130 differentially expressed genes(DEGs,122 upregulated and 8 downregulated)were identified in the uterus of hens in the LBS relative to those in the HBS.These DEGs were relevant to apoptosis due to the cellular Ca overload.Higher values of p62 protein level,caspase-8 activity,Bax protein expression and lower values of Bcl protein expression and Bcl/Bax ratio were seen in the LBS.TUNEL assay and hematoxylin-eosin staining showed a significant increase in TUNEL-positive cells and tissue damages in the uterus of the LBS.Although few DEGs were identified at the growth stage,similar uterine tissue damages were also observed in the LBS.The expressions of runt-related transcription factor 2 and osteocal-cin were upregulated in humeri of the LBS.Enlarged diameter and more structural damages of endocortical bones and decreased ash were observed in femurs of the HBS.Conclusion The lower eggshell breaking strength may be attributed to a declined Ca transport due to uterine tissue damages,which could affect eggshell calcification and lead to a weak ultrastructure.Impaired uterine Ca transport may result in reduced femoral bone resorption and increased humeral bone formation to maintain a higher mineral and bone quality in the LBS.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Verifying hierarchical network nonlocality in general quantum networks

Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Since China’s reform and opening-up,the growing disparity between urban and rural areas and regions has led to massive migration.With China’s Rural Revitalization Strategy and the industrial transfer from the eastern coastal areas to the inland,the migration direction and pattern of the floating population have undergone certain changes.Using the 2017 China Migrants Dynamic Survey(CMDS),excluding Hong Kong,Macao,and Taiwan regions of China,organized by China’s National Health Commission,the relationship matrix of the floating population is constructed according to the inflow place of the interviewees and their outflow place(the location of the registered residence)in the questionnaire survey.We then apply the complex network model to analyze the migration direction and network pattern of China’s floating population from the city scale.The migration network shows an obvious hierarchical agglomeration.The first-,second-,third-and fourth-tier distribution cities are municipalities directly under the central government,provincial capital cities,major cities in the central and western regions and ordinary cities in all provinces,respectively.The migration trend is from the central and western regions to the eastern coastal areas.The migration network has‘small world’characteristics,forming nine communities.It shows that most node cities in the same community are closely linked and geographically close,indicating that the migration network of floating population is still affected by geographical proximity.Narrowing the urban-rural and regional differences will promote the rational distribution this population.It is necessary to strengthen the reform of the registered residence system,so that the floating population can enjoy urban public services comparable to other populations,and allow migrants to live and work in peace.

Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a jam.Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks.However,most real-world networks are directed.To fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks.We find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network dynamics.Due to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis behavior.In addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery processes.These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

Lactiplantibacillus plantarum FRT4 attenuates high-energy low-protein dietinduced fatty liver hemorrhage syndrome in laying hens through regulating gut-liver axis

Background Fatty liver hemorrhage syndrome(FLHS)becomes one of the most major factors resulting in the laying hen death for caged egg production.This study aimed to investigate the therapeutic effects of Lactiplantibacillus plan-tarum(Lp.plantarum)FRT4 on FLHS model in laying hen with a focus on liver lipid metabolism,and gut microbiota.Results The FLHS model of laying hens was established by feeding a high-energy low-protein(HELP)diet,and the treatment groups were fed a HELP diet supplemented with differential proportions of Lp.plantarum FRT4.The results indicated that Lp.plantarum FRT4 increased laying rate,and reduced the liver lipid accumulation by regulating lipid metabolism(lipid synthesis and transport)and improving the gut microbiota composition.Moreover,Lp.plan-tarum FRT4 regulated the liver glycerophospholipid metabolism.Meanwhile,“gut-liver”axis analysis showed that there was a correlation between gut microbiota and lipid metabolites.Conclusions The results indicated that Lp.plantarum FRT4 improved the laying performance and alleviated FLHS in HELP diet-induced laying hens through regulating“gut-liver”axis.Our findings reveal that glycerophospholipid metabolism could be the underlying mechanism for the anti-FLHS effect of Lp.plantarum FRT4 and for future use of Lp.plantarum FRT4 as an excellent additive for the prevention and mitigation of FLHS in laying hens.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

The collective Unmanned Weapon System-of-Systems(UWSOS)network represents a fundamental element in modern warfare,characterized by a diverse array of unmanned combat platforms interconnected through hetero-geneous network architectures.Despite its strategic importance,the UWSOS network is highly susceptible to hostile infiltrations,which significantly impede its battlefield recovery capabilities.Existing methods to enhance network resilience predominantly focus on basic graph relationships,neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS.To address these limitations,we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network(E-MAGCN),designed to augment the adaptability of UWSOS.Our approach employs BERT for extracting semantic insights from nodes and edges,thereby refining feature representations by leveraging various node and edge categories.Additionally,E-MAGCN integrates a regularization-based multi-layer attention mechanism and a semantic node fusion algo-rithm within the Graph Convolutional Network(GCN)framework.Through extensive simulation experiments,our model demonstrates an enhancement in resilience performance ranging from 1.2% to 7% over existing algorithms.