In a peer-to-peer file-sharing system, a free-rider is a node which downloads files from its peers but does not share files to other nodes. Analyzing the free-riders’ impact on system throughputs is essential in exam...In a peer-to-peer file-sharing system, a free-rider is a node which downloads files from its peers but does not share files to other nodes. Analyzing the free-riders’ impact on system throughputs is essential in examining the performance of peer-to-peer file-sharing systems. We find that the free-riders’ impact largely depends on nodes behavior, including their online time and greed of downloading files. We extend an existing peer-to-peer system model and classify nodes according to their behavior. We focus on two peer-to-peer architectures: centralized indexing and distributed hash tables. We find that when the cooperators in a system are all greedy in downloading files, the system throughput has little room to increase while the cooperators throughput degrade badly with the increasing percent of greedy free-riders in the system. When all the cooperators are non-greedy with long average online time, the system throughput has much room to increase and the cooperators throughput degrade little with a high percent of greedy free-riders in the system. We also find that if a system can tolerate a high percent of greedy free-riders without suffering much throughput degradation, the system must contain some non-greedy cooperators that contribute great idle service capacity to the system.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
Distributed Federated Learning(DFL)technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets,making it a desirable solution for decentralized and privac...Distributed Federated Learning(DFL)technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets,making it a desirable solution for decentralized and privacy-preserving Web3 scenarios.However,DFL faces incentive and security challenges in the decentralized framework.To address these issues,this paper presents a Hierarchical Blockchain-enabled DFL(HBDFL)system,which provides a generic solution framework for the DFL-related applications.The proposed system consists of four major components,including a model contribution-based reward mechanism,a Proof of Elapsed Time and Accuracy(PoETA)consensus algorithm,a Distributed Reputation-based Verification Mechanism(DRTM)and an Accuracy-Dependent Throughput Management(ADTM)mechanism.The model contribution-based rewarding mechanism incentivizes network nodes to train models with their local datasets,while the PoETA consensus algorithm optimizes the tradeoff between the shared model accuracy and system throughput.The DRTM improves the system efficiency in consensus,and the ADTM mechanism guarantees that the throughput performance remains within a predefined range while improving the shared model accuracy.The performance of the proposed HBDFL system is evaluated by numerical simulations,with the results showing that the system improves the accuracy of the shared model while maintaining high throughput and ensuring security.展开更多
The integration of cognitive radio and energy has enhanced the utilization efficiency of the spectrum and promoted the application of green energy.To begin with,this paper presents the architecture of green energy-eff...The integration of cognitive radio and energy has enhanced the utilization efficiency of the spectrum and promoted the application of green energy.To begin with,this paper presents the architecture of green energy-efficient communication and network models.It incorporates the distributed network model and the heterogeneous two-tier network model into the green cognitive radio power control and channel allocation model.The primary focus of this research lies in energy conservation at the physical layer.To mitigate the interference with primary users and address the peak constraint in secondary user power allocation,the article analyzes the system model of the cognitive radio network and subsequently elaborates on the dynamic throughput maximization allocation algorithm.Eventually,through experimental analysis and verification,the distinctiveness and comprehensiveness of the optimal power control for this subject are illustrated.展开更多
In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the e...In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.展开更多
Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single...Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.展开更多
Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could he...Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis,knowledge,and therapy.A complete literature search was undertaken using PubMed,Elsevier,Google Scholar,ScienceDirect,Embase,and NCBI.This review examined the articles published from 2010 to 2025.Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods,like next-generation sequencing and single-cell analysis,have transformed our ability to study tumor complexity and genetic mutations,paving the way for more precise,personalized treatments.At the same time,imaging technologies such as Positron Emission Tomography(PET)and Magnetic Resonance Imaging(MRI)have made detecting tumors early and tracking treatment progress easier,all while improving patient comfort.Artificial intelligence(AI)and machine learning(ML)are having a significant impact by helping to analyze large volumes of data more efficiently and enhancing diagnostic accuracy.Meanwhile,Clustered Regulatory Interspaced Short Palindromic Repeats(CRISPR/Cas9)gene editing is emerging as a promising tool for directly targeting genes related to cancer,providing new possibilities for treatment.By integrating genomic,transcriptomic,proteomic,and metabolomic data,multi-omics approaches provide researchers with a more comprehensive understanding of the molecular mechanisms driving cancer,thereby facilitating the discovery of novel biomarkers and therapeutic targets.Despite these advancements,additional challenges persist,such as data integration,elevated costs,standardisation concerns,and the intricacies of translating findings into clinical practice,which might prevent wider implementation.Research needs to concentrate on improving these developments and encouraging multidisciplinary cooperation going forward to maximize their possibilities.Personalized cancer therapies will become more successful with ongoing developments,therefore enhancing patient outcomes and quality of life.展开更多
The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction perform...The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction performance of channel coding functions as a significant way of optimizing the transmission reliability and efficiency.In this paper,the efficient estimation methods of the block error rate(BLER)performance for rate-compatible polar codes(RCPC)are proposed under several scenarios.Firstly,the BLER performance of RCPC is generally evaluated in the additive white Gaussian noise channels.That is further extended into the Rayleigh fading channel case using an equivalent estimation method.Moreover,with respect to the powerful decoder such as successive cancellation list decoding,the performance estimation is derived analytically based on the polar weight spectrum and BLER upper bounds.Theoretical evaluation and numerical simulation results show that the estimated performance can fit well the practical simulated results of RCPC under the objective conditions,verifying the validity of our proposed performance estimation methods.Furthermore,the application designs of the reliability estimation of RCPC are explored,particularly in the advantages of the signal-to-noise(SNR)estimation and throughput efficiency optimization of polar coded hybrid automatic repeat request.展开更多
To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized pur...To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized purge-and-trap(μP&T) device integrating semiconductor refrigeration storage was developed in this work. Water samples were poured into the purge vessels and purged with purified air generated by an air pump. The VOCs in water samples were then separated and preconcentrated with sorbent tubes. After their complete separation and preconcentration, the tubes were subsequently preserved in the semiconductor refrigeration unit of the μP&T device. Notably, the high integration, small size, light weight, and low power consumption of the device makes it easy to be hand-carried to the field and transport by drone from remote locations, significantly enhancing the flexibility of field sampling. The performances of the device were evaluated by comparing analytical figures of merit for the detection of four cyclic volatile methylsiloxanes(cVMSs) in water. Compared to conventional collection and preservation methods, our proposed device preserved the VOCs more consistently in the sorbent tubes, with less than 5% loss of all analytes, and maintained stability for at least 20 days at 4℃. As a proof-of-concept,10 municipal wastewater samples were pretreated using this device with recoveries ranging from 82.5% to 99.9% for the target VOCs.展开更多
The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This pape...The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.展开更多
In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausti...In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausting.The IoT node has a finite battery to store the harvested energy and a limited buffer for the storage of the unsent data.The energy/-data arrives following a Markov process.Assuming the node has no prior knowledge of the energy/data process and only knows the values of the current time slot,the optimal power allocation problem is modeled as a reinforcement learning task.The state consists of the data in the buffer,the energy stored in the battery,the new coming data amount,the energy harvesting amount and the channel coefficient at time slot t.Then the action is defined as the selected transmitting power.With the growth of the state or action space,it is challenging to visit every state-action pair sufficiently and store all the state-action values,so a deep Q-learning based algorithm is proposed to solve this problem.Simulation results show the advantages of our proposed algorithms,and we also analyze the effect of different system setting parameters.展开更多
Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo developm...Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo development is of paramount importance.In this study,we established a stable porcine trophectoderm cell line expressing dual fluorescent reporter genes driven by the CDX2 and TEAD4 gene promoter segments using lentiviral transfection.Results Three amino acid metabolites—kynurenic acid,taurine,and tryptamine—met the minimum z-score criteria of 2.0 for both luciferase and Renilla luciferase activities and were initially identified as potential metabolites for embryo development,with their beneficial effects validated by qPCR.Given that the identified metabolites are closely related to methionine,arginine,and tryptophan,we selected these three amino acids,using lysine as a standard,and employed response surface methodology combined with our high-throughput screening cell model to efficiently screen and optimize amino acid combination conducive to early embryo development.The optimized candidate amino acid system included lysine(1.87 mmol/L),methionine(0.82 mmol/L),tryptophan(0.23 mmol/L),and arginine(3 mmol/L),with the ratio of 1:0.43:0.12:1.60.In vitro experiments confirmed that this amino acid system enhances the expression of key genes involved in early embryonic development and improves in vitro embryo adhesion.Transcriptomic analysis of blastocysts suggested that candidate amino acid system enhances early embryo development by regulating early embryonic cell cycle and differentiation,as well as improving nutrient absorption.Furthermore,based on response surface methodology,400 sows were used to verify this amino acid system,substituting arginine with the more cost-effective N-carbamoyl glutamate(NCG),a precursor of arginine.The optimal dietary amino acid requirement was predicted to be 0.71%lysine,0.32%methionine,0.22%tryptophan,and 0.10%NCG for sows during early gestation.The optimized amino acid system ratio of the feed,derived from the peripheral release of essential amino acids,was found to be 1:0.45:0.13,which is largely consistent with the results obtained from the cell model optimization.Subsequently,we furtherly verified that this optimal dietary amino acid system significantly increased total litter size,live litter size and litter weight in sows.Conclusions In summary,we successfully established a dual-fluorescent high-throughput screening cell model for the efficient identification of potential nutrients that would promote embryo development and implantation.This innovative approach overcomes the limitations of traditional amino acid nutrition studies in sows,providing a more effective model for enhancing reproductive outcomes.展开更多
Satellite mega-constellations(SMCs)encounter significant operational challenges due to various space environmental effects.While the mechanisms underlying some of these effects have been studied from a physical perspe...Satellite mega-constellations(SMCs)encounter significant operational challenges due to various space environmental effects.While the mechanisms underlying some of these effects have been studied from a physical perspective,their precise impact on the network performance of SMCs remains unclear.To elucidate this further,this study investigates the spatiotemporal distribution characteristics of space environmental effects,such as solar radiation,ionizing radiation,and space debris,and the associated failure mechanisms in the nodes and links of SMCs.In addition,the impacts of solar radiation and single-event effects on performance of SMC system,particularly network throughput capacity,are examined.Results reveal that under the effect of the space environment,the throughput capacity degradation of SMC system varies with different parameters such as orbital altitude and inclination.Most importantly,the results bridge the gap between the physical phenomena of space environmental effects and network-level modeling.Finally,future research directions are prospected,regarding network topology control,constellation architecture,network routing techniques,and so on,to help mitigate network performance degradation due to space environmental effects.展开更多
Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Me...Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.展开更多
Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire b...Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing techniques.However,large brain samples pose major challenges to this approach.展开更多
Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous ...Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.展开更多
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
In this paper, we developed performance assessment criteria to evaluate effects of compliance to ISPS Code＇s requirements on port/terminal operation in Nigeria. The primary data for the study were obtained from copie...In this paper, we developed performance assessment criteria to evaluate effects of compliance to ISPS Code＇s requirements on port/terminal operation in Nigeria. The primary data for the study were obtained from copies ofsurvey questionnaires administered to random sample of port users stratified by areas of specialisation. Hypotheses governing this study were based on the premise that additional port facilities provided and security measures adopted in compliance to ISPS code＇s requirements would have positive spillover effects on port operations. Evidence from data analysis indicated that compliance to ISPS code had positive effects on performance of operational performance of Nigeria ports. Similar effects were also observed inport users＇ satisfaction and profitability. The paper contributes by providing decision support framework for ports and terminals. monitoring and gauging outcomes of ISPS code administration in展开更多
To study the throughput scheduling problem under interference temperature in cognitive radio networks, an immune algorithm-based suboptimal method was proposed based on its NP-hard feature. The problem is modeled as a...To study the throughput scheduling problem under interference temperature in cognitive radio networks, an immune algorithm-based suboptimal method was proposed based on its NP-hard feature. The problem is modeled as a constrained optimization problem to maximize the total throughput of the secondary users（ SUs）. The mapping between the throughput scheduling problems and the immune algorithm is given. Suitable immune operators are designed such as binary antibody encoding, antibody initialization based on pre-knowledge, a proportional clone to its affinity and an adaptive mutation operator associated with the evolutionary generation. The simulation results showthat the proposed algorithm can obtain about 95% of the optimal throughput and operate with much lower liner computational complexity.展开更多
基金the National High Technology Re-search and Development Program (863) of China(No. 2007AA01Z457)the Shanghai Science and Technology Development Funds (No. 07QA14033)
文摘In a peer-to-peer file-sharing system, a free-rider is a node which downloads files from its peers but does not share files to other nodes. Analyzing the free-riders’ impact on system throughputs is essential in examining the performance of peer-to-peer file-sharing systems. We find that the free-riders’ impact largely depends on nodes behavior, including their online time and greed of downloading files. We extend an existing peer-to-peer system model and classify nodes according to their behavior. We focus on two peer-to-peer architectures: centralized indexing and distributed hash tables. We find that when the cooperators in a system are all greedy in downloading files, the system throughput has little room to increase while the cooperators throughput degrade badly with the increasing percent of greedy free-riders in the system. When all the cooperators are non-greedy with long average online time, the system throughput has much room to increase and the cooperators throughput degrade little with a high percent of greedy free-riders in the system. We also find that if a system can tolerate a high percent of greedy free-riders without suffering much throughput degradation, the system must contain some non-greedy cooperators that contribute great idle service capacity to the system.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
文摘Distributed Federated Learning(DFL)technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets,making it a desirable solution for decentralized and privacy-preserving Web3 scenarios.However,DFL faces incentive and security challenges in the decentralized framework.To address these issues,this paper presents a Hierarchical Blockchain-enabled DFL(HBDFL)system,which provides a generic solution framework for the DFL-related applications.The proposed system consists of four major components,including a model contribution-based reward mechanism,a Proof of Elapsed Time and Accuracy(PoETA)consensus algorithm,a Distributed Reputation-based Verification Mechanism(DRTM)and an Accuracy-Dependent Throughput Management(ADTM)mechanism.The model contribution-based rewarding mechanism incentivizes network nodes to train models with their local datasets,while the PoETA consensus algorithm optimizes the tradeoff between the shared model accuracy and system throughput.The DRTM improves the system efficiency in consensus,and the ADTM mechanism guarantees that the throughput performance remains within a predefined range while improving the shared model accuracy.The performance of the proposed HBDFL system is evaluated by numerical simulations,with the results showing that the system improves the accuracy of the shared model while maintaining high throughput and ensuring security.
文摘The integration of cognitive radio and energy has enhanced the utilization efficiency of the spectrum and promoted the application of green energy.To begin with,this paper presents the architecture of green energy-efficient communication and network models.It incorporates the distributed network model and the heterogeneous two-tier network model into the green cognitive radio power control and channel allocation model.The primary focus of this research lies in energy conservation at the physical layer.To mitigate the interference with primary users and address the peak constraint in secondary user power allocation,the article analyzes the system model of the cognitive radio network and subsequently elaborates on the dynamic throughput maximization allocation algorithm.Eventually,through experimental analysis and verification,the distinctiveness and comprehensiveness of the optimal power control for this subject are illustrated.
基金M/s.RASI Seeds Pvt.Ltd.,Attur,Tamil Nadu,India for their generous financial assistance in setting up a MAS study in cotton for genetic improvement of sucking pest resistance.
文摘In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.
基金the financial support of the National Natural Science Foundation of China(No.22168009)。
文摘Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.
文摘Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis,knowledge,and therapy.A complete literature search was undertaken using PubMed,Elsevier,Google Scholar,ScienceDirect,Embase,and NCBI.This review examined the articles published from 2010 to 2025.Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods,like next-generation sequencing and single-cell analysis,have transformed our ability to study tumor complexity and genetic mutations,paving the way for more precise,personalized treatments.At the same time,imaging technologies such as Positron Emission Tomography(PET)and Magnetic Resonance Imaging(MRI)have made detecting tumors early and tracking treatment progress easier,all while improving patient comfort.Artificial intelligence(AI)and machine learning(ML)are having a significant impact by helping to analyze large volumes of data more efficiently and enhancing diagnostic accuracy.Meanwhile,Clustered Regulatory Interspaced Short Palindromic Repeats(CRISPR/Cas9)gene editing is emerging as a promising tool for directly targeting genes related to cancer,providing new possibilities for treatment.By integrating genomic,transcriptomic,proteomic,and metabolomic data,multi-omics approaches provide researchers with a more comprehensive understanding of the molecular mechanisms driving cancer,thereby facilitating the discovery of novel biomarkers and therapeutic targets.Despite these advancements,additional challenges persist,such as data integration,elevated costs,standardisation concerns,and the intricacies of translating findings into clinical practice,which might prevent wider implementation.Research needs to concentrate on improving these developments and encouraging multidisciplinary cooperation going forward to maximize their possibilities.Personalized cancer therapies will become more successful with ongoing developments,therefore enhancing patient outcomes and quality of life.
基金supported by National Natural Science Foundation of China(No.62201596)Research Planning Project of National University of Defense Technology(ZK22-45).
文摘The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction performance of channel coding functions as a significant way of optimizing the transmission reliability and efficiency.In this paper,the efficient estimation methods of the block error rate(BLER)performance for rate-compatible polar codes(RCPC)are proposed under several scenarios.Firstly,the BLER performance of RCPC is generally evaluated in the additive white Gaussian noise channels.That is further extended into the Rayleigh fading channel case using an equivalent estimation method.Moreover,with respect to the powerful decoder such as successive cancellation list decoding,the performance estimation is derived analytically based on the polar weight spectrum and BLER upper bounds.Theoretical evaluation and numerical simulation results show that the estimated performance can fit well the practical simulated results of RCPC under the objective conditions,verifying the validity of our proposed performance estimation methods.Furthermore,the application designs of the reliability estimation of RCPC are explored,particularly in the advantages of the signal-to-noise(SNR)estimation and throughput efficiency optimization of polar coded hybrid automatic repeat request.
基金the National Natural Science Foundation of China (No. 22306146)the PhD Scientific Research Startup Foundation of Xihua University (No. RX2200002003) for their financial support。
文摘To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized purge-and-trap(μP&T) device integrating semiconductor refrigeration storage was developed in this work. Water samples were poured into the purge vessels and purged with purified air generated by an air pump. The VOCs in water samples were then separated and preconcentrated with sorbent tubes. After their complete separation and preconcentration, the tubes were subsequently preserved in the semiconductor refrigeration unit of the μP&T device. Notably, the high integration, small size, light weight, and low power consumption of the device makes it easy to be hand-carried to the field and transport by drone from remote locations, significantly enhancing the flexibility of field sampling. The performances of the device were evaluated by comparing analytical figures of merit for the detection of four cyclic volatile methylsiloxanes(cVMSs) in water. Compared to conventional collection and preservation methods, our proposed device preserved the VOCs more consistently in the sorbent tubes, with less than 5% loss of all analytes, and maintained stability for at least 20 days at 4℃. As a proof-of-concept,10 municipal wastewater samples were pretreated using this device with recoveries ranging from 82.5% to 99.9% for the target VOCs.
文摘The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.
文摘In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausting.The IoT node has a finite battery to store the harvested energy and a limited buffer for the storage of the unsent data.The energy/-data arrives following a Markov process.Assuming the node has no prior knowledge of the energy/data process and only knows the values of the current time slot,the optimal power allocation problem is modeled as a reinforcement learning task.The state consists of the data in the buffer,the energy stored in the battery,the new coming data amount,the energy harvesting amount and the channel coefficient at time slot t.Then the action is defined as the selected transmitting power.With the growth of the state or action space,it is challenging to visit every state-action pair sufficiently and store all the state-action values,so a deep Q-learning based algorithm is proposed to solve this problem.Simulation results show the advantages of our proposed algorithms,and we also analyze the effect of different system setting parameters.
基金supported by National Natural Science Foundation of China (32172747 and 32425052)
文摘Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo development is of paramount importance.In this study,we established a stable porcine trophectoderm cell line expressing dual fluorescent reporter genes driven by the CDX2 and TEAD4 gene promoter segments using lentiviral transfection.Results Three amino acid metabolites—kynurenic acid,taurine,and tryptamine—met the minimum z-score criteria of 2.0 for both luciferase and Renilla luciferase activities and were initially identified as potential metabolites for embryo development,with their beneficial effects validated by qPCR.Given that the identified metabolites are closely related to methionine,arginine,and tryptophan,we selected these three amino acids,using lysine as a standard,and employed response surface methodology combined with our high-throughput screening cell model to efficiently screen and optimize amino acid combination conducive to early embryo development.The optimized candidate amino acid system included lysine(1.87 mmol/L),methionine(0.82 mmol/L),tryptophan(0.23 mmol/L),and arginine(3 mmol/L),with the ratio of 1:0.43:0.12:1.60.In vitro experiments confirmed that this amino acid system enhances the expression of key genes involved in early embryonic development and improves in vitro embryo adhesion.Transcriptomic analysis of blastocysts suggested that candidate amino acid system enhances early embryo development by regulating early embryonic cell cycle and differentiation,as well as improving nutrient absorption.Furthermore,based on response surface methodology,400 sows were used to verify this amino acid system,substituting arginine with the more cost-effective N-carbamoyl glutamate(NCG),a precursor of arginine.The optimal dietary amino acid requirement was predicted to be 0.71%lysine,0.32%methionine,0.22%tryptophan,and 0.10%NCG for sows during early gestation.The optimized amino acid system ratio of the feed,derived from the peripheral release of essential amino acids,was found to be 1:0.45:0.13,which is largely consistent with the results obtained from the cell model optimization.Subsequently,we furtherly verified that this optimal dietary amino acid system significantly increased total litter size,live litter size and litter weight in sows.Conclusions In summary,we successfully established a dual-fluorescent high-throughput screening cell model for the efficient identification of potential nutrients that would promote embryo development and implantation.This innovative approach overcomes the limitations of traditional amino acid nutrition studies in sows,providing a more effective model for enhancing reproductive outcomes.
基金supported in part by the National Natural Science Foundation of China(62495022,62495020,62422114,62461160329,62121001,and 62371360)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST2022QNRC001).
文摘Satellite mega-constellations(SMCs)encounter significant operational challenges due to various space environmental effects.While the mechanisms underlying some of these effects have been studied from a physical perspective,their precise impact on the network performance of SMCs remains unclear.To elucidate this further,this study investigates the spatiotemporal distribution characteristics of space environmental effects,such as solar radiation,ionizing radiation,and space debris,and the associated failure mechanisms in the nodes and links of SMCs.In addition,the impacts of solar radiation and single-event effects on performance of SMC system,particularly network throughput capacity,are examined.Results reveal that under the effect of the space environment,the throughput capacity degradation of SMC system varies with different parameters such as orbital altitude and inclination.Most importantly,the results bridge the gap between the physical phenomena of space environmental effects and network-level modeling.Finally,future research directions are prospected,regarding network topology control,constellation architecture,network routing techniques,and so on,to help mitigate network performance degradation due to space environmental effects.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4604800,2021YFA1202300)the Natural and Science Foundation of China(Grant Nos.52201041,52275331,52205358)+1 种基金the Key Research and Development Program of Hubei Province(Nos.2024BCB091,2022CFA031)the Hong Kong Scholars Program(No.XJ2022014)。
文摘Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.
基金supported by the STI 2030-Major Project(2021ZD0204400,2022ZD0205203,2021ZD0200104,2022ZD0211900)the Shenzhen Science and Technology Program(RCYX20210706092100003,RCBS20221008093311027)+3 种基金the Shenzhen Medical Research Funds(A2303005)the Youth Innovation Promotion Association CAS(2022367)the National Natural Science Foundation of China(32100896)NSFC-Guangdong Joint Fund(U20A6005).
文摘Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing techniques.However,large brain samples pose major challenges to this approach.
基金supported by Shanghai Municipal Commission of Science and Technology,China(Grant No.:19XD1400300)the National Natural Science Foundation of China(Grant Nos.:821040821,82273867,and 82030107).
文摘Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
文摘In this paper, we developed performance assessment criteria to evaluate effects of compliance to ISPS Code＇s requirements on port/terminal operation in Nigeria. The primary data for the study were obtained from copies ofsurvey questionnaires administered to random sample of port users stratified by areas of specialisation. Hypotheses governing this study were based on the premise that additional port facilities provided and security measures adopted in compliance to ISPS code＇s requirements would have positive spillover effects on port operations. Evidence from data analysis indicated that compliance to ISPS code had positive effects on performance of operational performance of Nigeria ports. Similar effects were also observed inport users＇ satisfaction and profitability. The paper contributes by providing decision support framework for ports and terminals. monitoring and gauging outcomes of ISPS code administration in
基金The National Natural Science Foundation of China(No.U150461361202099+2 种基金61201175U1204618)China Postdoctoral Science Foundation(No.2013M541586)
文摘To study the throughput scheduling problem under interference temperature in cognitive radio networks, an immune algorithm-based suboptimal method was proposed based on its NP-hard feature. The problem is modeled as a constrained optimization problem to maximize the total throughput of the secondary users（ SUs）. The mapping between the throughput scheduling problems and the immune algorithm is given. Suitable immune operators are designed such as binary antibody encoding, antibody initialization based on pre-knowledge, a proportional clone to its affinity and an adaptive mutation operator associated with the evolutionary generation. The simulation results showthat the proposed algorithm can obtain about 95% of the optimal throughput and operate with much lower liner computational complexity.
