1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emer...1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges.展开更多
Background Fusion genes play a crucial role in the pathogenesis of acute myeloid leukemia(AML).This study investigated the utility of targeted next-generation sequencing(NGS)of RNA for detecting rare and unknown fusio...Background Fusion genes play a crucial role in the pathogenesis of acute myeloid leukemia(AML).This study investigated the utility of targeted next-generation sequencing(NGS)of RNA for detecting rare and unknown fusion genes in patients with AML.Methods A total of 85 adult AML samples previously identified as fusion gene-negative by multiplex nested reverse transcription-polymerase chain reaction(RT-PCR)were subjected to NGS analysis.Results Fusion genes were detected in 21 of 72(29.2%)patients.Among the 26 primary refractory patients,11(42.3%)exhibited fusion genes,whereas among the 18 relapsed patients,fusion genes were identified in five(27.8%).Notably,lysine methyltransferase 2A(KMT2A)and nucleoporin 98(NUP98)rearrangements were enriched in refractory/relapsed patients.Additionally,recurrent fusion transcripts involving eukaryotic translation initiation factor 4A1(EIF4A1)were identified.The identification of additional fusion genes resulted in an approximate 20.8%(11/53)reclassification of medium-risk karyotypes to the high-risk category,thereby enhancing diagnostic accuracy.Conclusions Targeted NGS may complement conventional methods for identifying novel fusions in refractory/relapsed AML;however,its prognostic value requires validation in prospective controlled trials.展开更多
Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture ...Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.展开更多
The primary problem during the evolvement of next-generation Internet is the contradiction between growing requirements for Internet and the insufficient development of network theory and technology. As the fundamenta...The primary problem during the evolvement of next-generation Internet is the contradiction between growing requirements for Internet and the insufficient development of network theory and technology. As the fundamental principles to guide the developing direction of Internet, the study of Internet architecture is always a focus in the research community. To address the core issue of network scalability, we propose multi-dimension scalable architecture of next-generation Internet, the main idea of which is to extend the single-dimension scalability of traditional Internet on interconnection to multi-dimension scalability of next-generation Internet. The multi-dimension scalability is composed of scale-scalability, performance-scalability, security-scalability, function-scalability, and service-scalability. We suggest five elements, namely, IPv6, authentic IPv6 addressing, scalable processing capacity of routers, end-to-end connectionless Quality-of-Service control, and 4over6 mechanism to realize the multi-dimension scalability. The current research results show that the multi-dimension scalable architecture composed of these five elements will bring great influence on next-generation Internet.展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
Objective and Background Early and accurate diagnosis of spinal infections,including spinal tuberculosis,is pivotal for effective treatment but remains challenging.This study aims to assess the diagnostic yield of met...Objective and Background Early and accurate diagnosis of spinal infections,including spinal tuberculosis,is pivotal for effective treatment but remains challenging.This study aims to assess the diagnostic yield of metagenomic next-generation sequencing(mNGS)compared with that of conventional microbiological tests(CMTs)in identifying pathogens associated with spinal pathologies,with a special focus on infections leading to surgical interventions.Methods We enrolled 85 patients who underwent spinal surgery,comprising 63 patients with clinically diagnosed spinal infections,including patients with spinal tuberculosis,and 22 patients with noninfectious spinal conditions.The procedures involved irrigation and debridement for persistent wound drainage,with subsequent DNA extraction from plasma and joint fluid for mNGS and CMT analysis.Results Significantly increased C-reactive protein(CRP)levels were observed in patients with infections.The mNGS approach showed greater diagnostic sensitivity(92.06%)for detecting pathogens,including Mycobacterium tuberculosis,than did CMTs(36.51%).Despite its low specificity,mNGS had considerable negative predictive value(70.59%),underscoring its utility in ruling out infections.Conclusions The mNGS offers superior sensitivity over CMTs in the diagnosis of a variety of spinal infections,notably spinal tuberculosis.This study highlights the potential of mNGS in enhancing the diagnosis of complex spinal infections,thereby informing targeted treatment strategies.展开更多
The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturi...The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart insurance.In the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and actuators.However,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart healthcare.For these next-generation applications,there is a requirement for far-reaching privacy and security in the IoT.Recently,blockchain systems have emerged as a key technology that changes the way we exchange data.This emerging technology has revealed encouraging implementation scenarios,such as secured digital currencies.As a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those applications.There have been several studies on the integration of blockchain networks and IoT.However,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic framework.This paper reviews blockchain networks and proposes BaaU as one of the enablers.The proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two scenarios.The rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT devices.The second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services.展开更多
The Internet of things has particularly novel implications in the area of public health. This is due to (1) The rapid and widespread adoption of powerful contemporary Smartphone’s;(2) The increasing availability and ...The Internet of things has particularly novel implications in the area of public health. This is due to (1) The rapid and widespread adoption of powerful contemporary Smartphone’s;(2) The increasing availability and use of health and fitness sensors, wearable sensor patches, smart watches, wireless-enabled digital tattoos and ambient sensors;and (3) The nature of public health to implicitly involve connectivity with and the acquisition of data in relation to large numbers of individuals up to population scale. Of particular relevance in relation to the Internet of Things (IoT) and public health is the need for privacy and anonymity of users. It should be noted that IoT capabilities are not inconsistent with maintaining privacy, due to the focus of public health on aggregate data not individual data and broad public health interventions. In addition, public health information systems utilizing IoT capabilities can be constructed to specifically ensure privacy, security and anonymity, as has been developed and evaluated in this work. In this paper we describe the particular characteristics of the IoT that can play a role in enabling emerging public health capabilities;we describe a privacy-preserving IoT-based public health information system architecture;and provide a privacy evaluation.展开更多
BACKGROUND Pepsinogen(PG)and the PG I/II ratio(PGR)are critical indicators for diagnosing Helicobacter pylori infection and chronic atrophic gastritis,and assessing gastric cancer risk.Existing reference intervals(RIs...BACKGROUND Pepsinogen(PG)and the PG I/II ratio(PGR)are critical indicators for diagnosing Helicobacter pylori infection and chronic atrophic gastritis,and assessing gastric cancer risk.Existing reference intervals(RIs)often overlook age,sex,and demographic variations.Partitioned RIs,while considering these factors,fail to capture the gradual age-related physiological changes.Next-generation RIs offer a solution to this limitation.AIM To investigate age-and sex-specific dynamics of PG and establish next-generation RIs for adults and the elderly in northern China.METHODS After screening,708 healthy individuals were included in this observational study.Serum PG was measured using chemiluminescence immunoassay.Age-and sex-related effects on PG were analyzed with a two-way analysis of variance.RI partitioning was determined by the standard deviation ratio(SDR).Traditional RIs were established using a non-parametric approach.Generalized Additive Models for Location,Scale,and Shape(GAMLSS)modeled age-related trends and continuous reference percentiles for PG I and PG II.Reference limit flagging rates for both RI types were compared.RESULTS PG I and PG II levels were influenced by age(P<0.001)and sex(P<0.001),while PGR remained stable.Age-specific RIs were required for PG I(SDR=0.366)and PG II(SDR=0.424).Partitioned RIs were established for PG I and PG II,with a single RI for PGR.GAMLSS modeling revealed distinct age-dependent trajectories:PG I increased from a median of 39.75μg/L at age 20 years to 49.75μg/L at age 60 years,a 25.16%increase,after which it plateaued through age 80 years.In contrast,PG II showed a continuous rise throughout the age range,with the median value increasing from 5.07μg/L at age 20 years to 8.36μg/L at age 80 years,corresponding to a 64.89%increase.Continuous reference percentiles intuitively reflected these trends and were detailed in this study.Next-generation RIs demonstrated superior accuracy compared to partitioned RIs when applied to specific age subgroups.CONCLUSION This study elucidates the age-and sex-specific dynamics of PG and,to our knowledge,is the first to establish next-generation RIs for PG,supporting more individualized interpretation in laboratory medicine.展开更多
BACKGROUND Leuconostoc garlicum is commonly found in fermented foods and very few infected patients have been reported,who typically present symptoms such as fever and fatigue.Conventional clinical examinations often ...BACKGROUND Leuconostoc garlicum is commonly found in fermented foods and very few infected patients have been reported,who typically present symptoms such as fever and fatigue.Conventional clinical examinations often struggle to identify this bacterium,and routine anti-infective treatments are generally ineffective.Both diagnostic challenges and therapeutic limitations pose significant difficulties for clinicians.CASE SUMMARY We report a patient ultimately diagnosed with Leuconostoc garlicum infection.The primary manifestations included persistent fever,cough and fatigue.These symptoms lasted for 2 months.He received anti-infective treatment at a community hospital,but this was ineffective.After inquiring about the patient's medical history and conducting a physical examination,the patient underwent laboratory tests.Complete blood count tests revealed that the patient had a high proportion of neutrophils,C-reactive protein level was 235.9 mg/L,erythrocyte sedimentation rate was 67 mm/h,respiratory pathogen testing was negative,and he was then thought to have an infectious disease.However,conventional anti-infective treatments were ineffective.After excluding infectious neurological diseases,urologic diseases and digestive problems,we ultimately focused our attention on the lungs.A lung computed tomography scan indicated pulmonary inflammation.Bronchoalveolar lavage fluid for next-generation sequencing suggested lung infection with Leuconostoc garlicum.The patient's symptoms gradually improved following treatment with piperacillin tazobactam and linezolid.During the follow-up period,the patient's temperature remained normal.CONCLUSION For patients with suspected bacterial infection and experiencing fever,conventional anti-infective treatment can be ineffective in controlling their symptoms,and an infection due to rare bacteria or drug-resistant bacteria should be considered.Next-generation sequencing enables rapid and precise identification of infection-related pathogens in febrile patients.展开更多
In rice fields,rice plants usually grow alongside wild weeds and are attacked by various invertebrate species.Viruses are abundant in plants and invertebrates,playing crucial ecological roles in controlling microbial ...In rice fields,rice plants usually grow alongside wild weeds and are attacked by various invertebrate species.Viruses are abundant in plants and invertebrates,playing crucial ecological roles in controlling microbial abundance and maintaining community structures.To date,only 16 rice viruses have been documented in rice-growing regions.These viruses pose serious threats to rice production and have traditionally been identified only from rice plants and insect vectors by isolation techniques.Advances in next-generation sequencing(NGS)have made it feasible to discover viruses on a global scale.Recently,numerous viruses have been identified in plants and invertebrates using NGS technologies.In this review,we discuss viral studies in rice plants,invertebrate species,and weeds in rice fields.Many novel viruses have been discovered in rice ecosystems through NGS technologies,with some also detected using metatranscriptomic and small RNA sequencing.These analyses greatly expand our understanding of viruses in rice fields and provide valuable insights for developing efficient strategies to manage insect pests and virus-mediated rice diseases.展开更多
In this study,an amine-reactive poly(pentafluorophenyl acrylate)(PPFPA)platform was developed for advanced surface engineering of next-generation sequencing(NGS)chips.Through post-polymerization modification,PPFPA was...In this study,an amine-reactive poly(pentafluorophenyl acrylate)(PPFPA)platform was developed for advanced surface engineering of next-generation sequencing(NGS)chips.Through post-polymerization modification,PPFPA was functionalized with dual moieties:azide groups for covalent immobilization of DBCO-modified DNA primers via click chemistry and tunable hydrophilic side chains to optimize biocompatibility and surface properties.Systematic screening revealed that hydrophobic azide carriers combined with neutral hydroxyl groups maximized the DNA immobilization efficacy,approaching the performance of commercial polyacrylamide-based polymers.The negatively charged carboxyl groups severely impede DNA primer attachment.Higher molecular weight derivatives further enhance the efficacy of DNA immobilization.In NGS validation,optimized surface modification polymers achieved robust surface density of clustered DNA and high sequencing accuracy,surpassing quality benchmarks and comparable to those of conventional analogs.This platform demonstrates significant potential for tailoring high-sensitivity surfaces for genomic applications,advancing clinical diagnostics,and personalized medicine.展开更多
This study investigates the diversity of gut microbiota in Metaphire peguana,an earthworm species commonly found in agricultural areas of Thailand.Earthworms play a critical role in soil ecosystems by supporting nutri...This study investigates the diversity of gut microbiota in Metaphire peguana,an earthworm species commonly found in agricultural areas of Thailand.Earthworms play a critical role in soil ecosystems by supporting nutrient cycling and breaking down organic matter.Understanding the microbial diversity in their gut is essential for exploring their ecological contributions.Using Next Generation Sequencing(NGS),we analyzed the mycobiome in the gut of M.peguana.Our findings revealed a high diversity of fungal species,primarily belonging to two major phyla:Ascomycota and Basidiomycota.Ascomycota was the most abundant phylum,comprising 40.1% of the total fungal species identified.A total of 33 distinct fungal species were identified,which underscores the richness of microbial life within the earthworm gut.This study successfully created the first genetic database of the microbial community in M.peguana,providing a foundation for future research in agricultural applications.The microbial species identified,particularly siderophoreproducing fungi,could have significant implications for improving soil fertility and promoting sustainable agricultural practices.The use of NGS technology has enabled comprehensive profiling of microbial communities,allowing for precise identification of fungi that may play essential roles in soil health.Furthermore,the study paves the way for future studies on the potential applications of earthworm gut microbiomes in biotechnology,especially in enhancing soil nutrient availability and plant growth.The findings of this research contribute to the broader understanding of the ecological roles of earthworms and their microbiomes in soil ecosystems.展开更多
Cystic echinococcosis (CE) is a prevalent zoonotic disease caused by Echinococcus granulosus, with a cosmopolitan distribution. The parasite is transmitted cyclically between canines and numerous intermediate herbivor...Cystic echinococcosis (CE) is a prevalent zoonotic disease caused by Echinococcus granulosus, with a cosmopolitan distribution. The parasite is transmitted cyclically between canines and numerous intermediate herbivorous livestock animals. Also, other Taeniid tapeworms could infect domestic dogs and they pose significant veterinary and public health concerns worldwide. This study aimed to develop a sensitive molecular method for detecting Echinococcus spp. DNA in dog fecal samples using next-generation sequencing (NGS). A set of PCR primers targeting conserved regions of Taeniid tapeworms’ 18s rRNA genes was designed and tested for amplifying genomic DNA from various tapeworm species. The PCR system demonstrated high sensitivity, amplifying DNA from all tested tapeworm species, with differences observed in amplified band sizes. The primers were adapted for NGS analysis by adding forward and reverse adapters, enabling the sequencing of amplified DNA fragments. Application of the developed PCR system to dog fecal samples collected from Yatta town, Palestine, revealed the presence of E. granulosus DNA in five out of 50 samples. NGS analysis confirmed the specificity of the amplified DNA fragments, showing 98% - 99% similarity with the 18s rDNA gene of E. granulosus. This study demonstrates the utility of NGS-based molecular methods for accurate and sensitive detection of Echinococcus spp. in dog fecal samples, providing valuable insights for epidemiological surveillance and control programs of echinococcosis in endemic regions.展开更多
The improvement of soybean seed carotenoid contents is very important due to the beneficial role of carotenoids in human health and nutrition. However, the genetic architecture underlying soybean carotenoid biosynthes...The improvement of soybean seed carotenoid contents is very important due to the beneficial role of carotenoids in human health and nutrition. However, the genetic architecture underlying soybean carotenoid biosynthesis remains largely unknown. In the present study, we employed next generation sequencing-based bulked-segregant analysis to identify new genomic regions governing seed carotenoids in 1,551 natural soybean accessions. The genomic DNA samples of individual plants with extreme phenotypes were pooled to form two bulks with high(50 accessions) and low(50 accessions) carotenoid contents for Illumina sequencing. A total of 125.09 Gb of clean bases and 89.82% of Q30 were obtained, and the average alignment efficiency was 99.45% with an average coverage depth of 62.20× and 99.75% genome coverage. Based on the G prime statistic algorithm(G') method analysis, 16 candidate genomic loci with a total length 20.41 Mb were found to be related to the trait. Of these loci, the most significant regions displaying the highest elevated G' values were found in chromosome 06 at a position of 18.53–22.67 Mb, and chromosome 19 at genomic region intervals of 8.36–10.94, 12.06–13.79 and 18.45–20.26 Mb. These regions were then used to identify the key candidate genes. In these regions, 250 predicted genes were found and analyzed to obtain 90 significantly enriched(P<0.05) Gene Ontology(GO) terms. Based on ANNOVAR analysis, 50 genes with non-synonymous and stopgained mutations were preferentially selected as potential candidate genes. Of those 50 genes, following their gene annotation functions and high significant haplotype variations in various environments,five genes were identified as the most promising candidate genes regulating soybean seed carotenoid accumulation, and they should be investigated in further functional validation studies. Collectively, understanding the genetic basis of carotenoid pigments and identifying genes underpinning carotenoid accumulation via a bulked-segregant analysis-based sequencing(BSA-seq) approach provide new insights for exploring future molecular breeding efforts to produce soybean cultivars with high carotenoid content.展开更多
Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ...Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.展开更多
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects....The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.展开更多
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ...Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.展开更多
Deploying Large LanguageModel(LLM)-based agents in the Industrial Internet ofThings(IIoT)presents significant challenges,including high latency from cloud-based APIs,data privacy concerns,and the infeasibility of depl...Deploying Large LanguageModel(LLM)-based agents in the Industrial Internet ofThings(IIoT)presents significant challenges,including high latency from cloud-based APIs,data privacy concerns,and the infeasibility of deploying monolithic models on resource-constrained edge devices.While smaller models(SLMs)are suitable for edge deployment,they often lack the reasoning power for complex,multi-step tasks.To address these issues,this paper introduces LEAF,a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge.LEAF employs a novel architecture where multiple expert SLMs—specialized for planning,execution,and interaction—work in concert,decomposing complex problems into manageable sub-tasks.To mitigate the resource overhead of this multi-model approach,LEAF implements an efficient parameter-sharing scheme based on Scalable Low-Rank Adaptation(S-LoRA).We introduce a two-stage training strategy combining Supervised Fine-Tuning(SFT)and Group Relative Policy Optimization(GRPO)to significantly enhance each expert’s capabilities.Furthermore,a Finite StateMachine(FSM)-based decision engine orchestrates the workflow,uniquely balancing deterministic control with intelligent flexibility,making it ideal for industrial environments that demand both reliability and adaptability.Experiments across diverse IIoT scenarios demonstrate that LEAF significantly outperforms baseline methods in both task success rate and user satisfaction.Notably,our fine-tuned 4-billion-parameter model achieves a task success rate over 90%in complex IIoT scenarios,demonstrating LEAF’s ability to deliver powerful and efficient autonomy at the industrial edge.展开更多
TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,th...TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection.展开更多
文摘1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges.
基金supported by the National Natural Science Foundation of China(No.82100164,82302692)the Capital Medical University Research Cultivation Fund(No.PYZ22099)the Guangdong Provincial Medical Science and Technology Research Fund Project(No.A2024190).
文摘Background Fusion genes play a crucial role in the pathogenesis of acute myeloid leukemia(AML).This study investigated the utility of targeted next-generation sequencing(NGS)of RNA for detecting rare and unknown fusion genes in patients with AML.Methods A total of 85 adult AML samples previously identified as fusion gene-negative by multiplex nested reverse transcription-polymerase chain reaction(RT-PCR)were subjected to NGS analysis.Results Fusion genes were detected in 21 of 72(29.2%)patients.Among the 26 primary refractory patients,11(42.3%)exhibited fusion genes,whereas among the 18 relapsed patients,fusion genes were identified in five(27.8%).Notably,lysine methyltransferase 2A(KMT2A)and nucleoporin 98(NUP98)rearrangements were enriched in refractory/relapsed patients.Additionally,recurrent fusion transcripts involving eukaryotic translation initiation factor 4A1(EIF4A1)were identified.The identification of additional fusion genes resulted in an approximate 20.8%(11/53)reclassification of medium-risk karyotypes to the high-risk category,thereby enhancing diagnostic accuracy.Conclusions Targeted NGS may complement conventional methods for identifying novel fusions in refractory/relapsed AML;however,its prognostic value requires validation in prospective controlled trials.
基金supported by the Beijing Natural Science Foundation under Grant 9242003partially supported by the Natural Science Foundation of Chongqing,China under Grant CSTB2023NSCQ-MSX0391+3 种基金partially supported by the National Natural Science Foundation of China under Grant 62471493partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023LZH017,ZR2024MF066supported by the Key Laboratory of Public Opinion Governance and Computational Communication under Grant YQKFYB202501The Research Project on the Development of Social Sciences in Hebei Province in 2024(No.202403150).
文摘Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.
基金This work is supported by the National Natural Science Foundation of China （Grant No. 90104002）, the National Grand Fundamental Research 973 Program of China （Grant No. 2003CB314801）.
文摘The primary problem during the evolvement of next-generation Internet is the contradiction between growing requirements for Internet and the insufficient development of network theory and technology. As the fundamental principles to guide the developing direction of Internet, the study of Internet architecture is always a focus in the research community. To address the core issue of network scalability, we propose multi-dimension scalable architecture of next-generation Internet, the main idea of which is to extend the single-dimension scalability of traditional Internet on interconnection to multi-dimension scalability of next-generation Internet. The multi-dimension scalability is composed of scale-scalability, performance-scalability, security-scalability, function-scalability, and service-scalability. We suggest five elements, namely, IPv6, authentic IPv6 addressing, scalable processing capacity of routers, end-to-end connectionless Quality-of-Service control, and 4over6 mechanism to realize the multi-dimension scalability. The current research results show that the multi-dimension scalable architecture composed of these five elements will bring great influence on next-generation Internet.
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
基金supported by the Hubei Provincial Natural Science Foundation of China(No.2023AFB646)Knowledge Innovation Program of Wuhan(No.2023020201010155)Educational Research Program of Huazhong University of Science and Technology(No.2022135).
文摘Objective and Background Early and accurate diagnosis of spinal infections,including spinal tuberculosis,is pivotal for effective treatment but remains challenging.This study aims to assess the diagnostic yield of metagenomic next-generation sequencing(mNGS)compared with that of conventional microbiological tests(CMTs)in identifying pathogens associated with spinal pathologies,with a special focus on infections leading to surgical interventions.Methods We enrolled 85 patients who underwent spinal surgery,comprising 63 patients with clinically diagnosed spinal infections,including patients with spinal tuberculosis,and 22 patients with noninfectious spinal conditions.The procedures involved irrigation and debridement for persistent wound drainage,with subsequent DNA extraction from plasma and joint fluid for mNGS and CMT analysis.Results Significantly increased C-reactive protein(CRP)levels were observed in patients with infections.The mNGS approach showed greater diagnostic sensitivity(92.06%)for detecting pathogens,including Mycobacterium tuberculosis,than did CMTs(36.51%).Despite its low specificity,mNGS had considerable negative predictive value(70.59%),underscoring its utility in ruling out infections.Conclusions The mNGS offers superior sensitivity over CMTs in the diagnosis of a variety of spinal infections,notably spinal tuberculosis.This study highlights the potential of mNGS in enhancing the diagnosis of complex spinal infections,thereby informing targeted treatment strategies.
基金supported by the Deanship of Scientic Research(DSR),King Abdulaziz University,Jeddah,under Grant No.RG-2-611-41(A.OA.received the gran)。
文摘The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart insurance.In the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and actuators.However,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart healthcare.For these next-generation applications,there is a requirement for far-reaching privacy and security in the IoT.Recently,blockchain systems have emerged as a key technology that changes the way we exchange data.This emerging technology has revealed encouraging implementation scenarios,such as secured digital currencies.As a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those applications.There have been several studies on the integration of blockchain networks and IoT.However,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic framework.This paper reviews blockchain networks and proposes BaaU as one of the enablers.The proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two scenarios.The rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT devices.The second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services.
文摘The Internet of things has particularly novel implications in the area of public health. This is due to (1) The rapid and widespread adoption of powerful contemporary Smartphone’s;(2) The increasing availability and use of health and fitness sensors, wearable sensor patches, smart watches, wireless-enabled digital tattoos and ambient sensors;and (3) The nature of public health to implicitly involve connectivity with and the acquisition of data in relation to large numbers of individuals up to population scale. Of particular relevance in relation to the Internet of Things (IoT) and public health is the need for privacy and anonymity of users. It should be noted that IoT capabilities are not inconsistent with maintaining privacy, due to the focus of public health on aggregate data not individual data and broad public health interventions. In addition, public health information systems utilizing IoT capabilities can be constructed to specifically ensure privacy, security and anonymity, as has been developed and evaluated in this work. In this paper we describe the particular characteristics of the IoT that can play a role in enabling emerging public health capabilities;we describe a privacy-preserving IoT-based public health information system architecture;and provide a privacy evaluation.
文摘BACKGROUND Pepsinogen(PG)and the PG I/II ratio(PGR)are critical indicators for diagnosing Helicobacter pylori infection and chronic atrophic gastritis,and assessing gastric cancer risk.Existing reference intervals(RIs)often overlook age,sex,and demographic variations.Partitioned RIs,while considering these factors,fail to capture the gradual age-related physiological changes.Next-generation RIs offer a solution to this limitation.AIM To investigate age-and sex-specific dynamics of PG and establish next-generation RIs for adults and the elderly in northern China.METHODS After screening,708 healthy individuals were included in this observational study.Serum PG was measured using chemiluminescence immunoassay.Age-and sex-related effects on PG were analyzed with a two-way analysis of variance.RI partitioning was determined by the standard deviation ratio(SDR).Traditional RIs were established using a non-parametric approach.Generalized Additive Models for Location,Scale,and Shape(GAMLSS)modeled age-related trends and continuous reference percentiles for PG I and PG II.Reference limit flagging rates for both RI types were compared.RESULTS PG I and PG II levels were influenced by age(P<0.001)and sex(P<0.001),while PGR remained stable.Age-specific RIs were required for PG I(SDR=0.366)and PG II(SDR=0.424).Partitioned RIs were established for PG I and PG II,with a single RI for PGR.GAMLSS modeling revealed distinct age-dependent trajectories:PG I increased from a median of 39.75μg/L at age 20 years to 49.75μg/L at age 60 years,a 25.16%increase,after which it plateaued through age 80 years.In contrast,PG II showed a continuous rise throughout the age range,with the median value increasing from 5.07μg/L at age 20 years to 8.36μg/L at age 80 years,corresponding to a 64.89%increase.Continuous reference percentiles intuitively reflected these trends and were detailed in this study.Next-generation RIs demonstrated superior accuracy compared to partitioned RIs when applied to specific age subgroups.CONCLUSION This study elucidates the age-and sex-specific dynamics of PG and,to our knowledge,is the first to establish next-generation RIs for PG,supporting more individualized interpretation in laboratory medicine.
文摘BACKGROUND Leuconostoc garlicum is commonly found in fermented foods and very few infected patients have been reported,who typically present symptoms such as fever and fatigue.Conventional clinical examinations often struggle to identify this bacterium,and routine anti-infective treatments are generally ineffective.Both diagnostic challenges and therapeutic limitations pose significant difficulties for clinicians.CASE SUMMARY We report a patient ultimately diagnosed with Leuconostoc garlicum infection.The primary manifestations included persistent fever,cough and fatigue.These symptoms lasted for 2 months.He received anti-infective treatment at a community hospital,but this was ineffective.After inquiring about the patient's medical history and conducting a physical examination,the patient underwent laboratory tests.Complete blood count tests revealed that the patient had a high proportion of neutrophils,C-reactive protein level was 235.9 mg/L,erythrocyte sedimentation rate was 67 mm/h,respiratory pathogen testing was negative,and he was then thought to have an infectious disease.However,conventional anti-infective treatments were ineffective.After excluding infectious neurological diseases,urologic diseases and digestive problems,we ultimately focused our attention on the lungs.A lung computed tomography scan indicated pulmonary inflammation.Bronchoalveolar lavage fluid for next-generation sequencing suggested lung infection with Leuconostoc garlicum.The patient's symptoms gradually improved following treatment with piperacillin tazobactam and linezolid.During the follow-up period,the patient's temperature remained normal.CONCLUSION For patients with suspected bacterial infection and experiencing fever,conventional anti-infective treatment can be ineffective in controlling their symptoms,and an infection due to rare bacteria or drug-resistant bacteria should be considered.Next-generation sequencing enables rapid and precise identification of infection-related pathogens in febrile patients.
基金supported by the National Natural Science Foundation of China(Grant Nos.31972983 and 32072487)the Key Technology R&D Program of Zhejiang Province,China(Grant No.2021C02006)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY23C140001).
文摘In rice fields,rice plants usually grow alongside wild weeds and are attacked by various invertebrate species.Viruses are abundant in plants and invertebrates,playing crucial ecological roles in controlling microbial abundance and maintaining community structures.To date,only 16 rice viruses have been documented in rice-growing regions.These viruses pose serious threats to rice production and have traditionally been identified only from rice plants and insect vectors by isolation techniques.Advances in next-generation sequencing(NGS)have made it feasible to discover viruses on a global scale.Recently,numerous viruses have been identified in plants and invertebrates using NGS technologies.In this review,we discuss viral studies in rice plants,invertebrate species,and weeds in rice fields.Many novel viruses have been discovered in rice ecosystems through NGS technologies,with some also detected using metatranscriptomic and small RNA sequencing.These analyses greatly expand our understanding of viruses in rice fields and provide valuable insights for developing efficient strategies to manage insect pests and virus-mediated rice diseases.
基金financially supported by the Science and Technology Commission of Shanghai Municipality(No.24ZR1401400)Shenzhen Salus Bio Med Company for their strong support in this study。
文摘In this study,an amine-reactive poly(pentafluorophenyl acrylate)(PPFPA)platform was developed for advanced surface engineering of next-generation sequencing(NGS)chips.Through post-polymerization modification,PPFPA was functionalized with dual moieties:azide groups for covalent immobilization of DBCO-modified DNA primers via click chemistry and tunable hydrophilic side chains to optimize biocompatibility and surface properties.Systematic screening revealed that hydrophobic azide carriers combined with neutral hydroxyl groups maximized the DNA immobilization efficacy,approaching the performance of commercial polyacrylamide-based polymers.The negatively charged carboxyl groups severely impede DNA primer attachment.Higher molecular weight derivatives further enhance the efficacy of DNA immobilization.In NGS validation,optimized surface modification polymers achieved robust surface density of clustered DNA and high sequencing accuracy,surpassing quality benchmarks and comparable to those of conventional analogs.This platform demonstrates significant potential for tailoring high-sensitivity surfaces for genomic applications,advancing clinical diagnostics,and personalized medicine.
文摘This study investigates the diversity of gut microbiota in Metaphire peguana,an earthworm species commonly found in agricultural areas of Thailand.Earthworms play a critical role in soil ecosystems by supporting nutrient cycling and breaking down organic matter.Understanding the microbial diversity in their gut is essential for exploring their ecological contributions.Using Next Generation Sequencing(NGS),we analyzed the mycobiome in the gut of M.peguana.Our findings revealed a high diversity of fungal species,primarily belonging to two major phyla:Ascomycota and Basidiomycota.Ascomycota was the most abundant phylum,comprising 40.1% of the total fungal species identified.A total of 33 distinct fungal species were identified,which underscores the richness of microbial life within the earthworm gut.This study successfully created the first genetic database of the microbial community in M.peguana,providing a foundation for future research in agricultural applications.The microbial species identified,particularly siderophoreproducing fungi,could have significant implications for improving soil fertility and promoting sustainable agricultural practices.The use of NGS technology has enabled comprehensive profiling of microbial communities,allowing for precise identification of fungi that may play essential roles in soil health.Furthermore,the study paves the way for future studies on the potential applications of earthworm gut microbiomes in biotechnology,especially in enhancing soil nutrient availability and plant growth.The findings of this research contribute to the broader understanding of the ecological roles of earthworms and their microbiomes in soil ecosystems.
文摘Cystic echinococcosis (CE) is a prevalent zoonotic disease caused by Echinococcus granulosus, with a cosmopolitan distribution. The parasite is transmitted cyclically between canines and numerous intermediate herbivorous livestock animals. Also, other Taeniid tapeworms could infect domestic dogs and they pose significant veterinary and public health concerns worldwide. This study aimed to develop a sensitive molecular method for detecting Echinococcus spp. DNA in dog fecal samples using next-generation sequencing (NGS). A set of PCR primers targeting conserved regions of Taeniid tapeworms’ 18s rRNA genes was designed and tested for amplifying genomic DNA from various tapeworm species. The PCR system demonstrated high sensitivity, amplifying DNA from all tested tapeworm species, with differences observed in amplified band sizes. The primers were adapted for NGS analysis by adding forward and reverse adapters, enabling the sequencing of amplified DNA fragments. Application of the developed PCR system to dog fecal samples collected from Yatta town, Palestine, revealed the presence of E. granulosus DNA in five out of 50 samples. NGS analysis confirmed the specificity of the amplified DNA fragments, showing 98% - 99% similarity with the 18s rDNA gene of E. granulosus. This study demonstrates the utility of NGS-based molecular methods for accurate and sensitive detection of Echinococcus spp. in dog fecal samples, providing valuable insights for epidemiological surveillance and control programs of echinococcosis in endemic regions.
基金financially supported by the National Natural Science Foundation of China (32161143033, 32272178, and 32001574)National Key Research and Development Program of China (2021YFD1201605)the Agricultural Science and Technology Innovation Project of CAAS。
文摘The improvement of soybean seed carotenoid contents is very important due to the beneficial role of carotenoids in human health and nutrition. However, the genetic architecture underlying soybean carotenoid biosynthesis remains largely unknown. In the present study, we employed next generation sequencing-based bulked-segregant analysis to identify new genomic regions governing seed carotenoids in 1,551 natural soybean accessions. The genomic DNA samples of individual plants with extreme phenotypes were pooled to form two bulks with high(50 accessions) and low(50 accessions) carotenoid contents for Illumina sequencing. A total of 125.09 Gb of clean bases and 89.82% of Q30 were obtained, and the average alignment efficiency was 99.45% with an average coverage depth of 62.20× and 99.75% genome coverage. Based on the G prime statistic algorithm(G') method analysis, 16 candidate genomic loci with a total length 20.41 Mb were found to be related to the trait. Of these loci, the most significant regions displaying the highest elevated G' values were found in chromosome 06 at a position of 18.53–22.67 Mb, and chromosome 19 at genomic region intervals of 8.36–10.94, 12.06–13.79 and 18.45–20.26 Mb. These regions were then used to identify the key candidate genes. In these regions, 250 predicted genes were found and analyzed to obtain 90 significantly enriched(P<0.05) Gene Ontology(GO) terms. Based on ANNOVAR analysis, 50 genes with non-synonymous and stopgained mutations were preferentially selected as potential candidate genes. Of those 50 genes, following their gene annotation functions and high significant haplotype variations in various environments,five genes were identified as the most promising candidate genes regulating soybean seed carotenoid accumulation, and they should be investigated in further functional validation studies. Collectively, understanding the genetic basis of carotenoid pigments and identifying genes underpinning carotenoid accumulation via a bulked-segregant analysis-based sequencing(BSA-seq) approach provide new insights for exploring future molecular breeding efforts to produce soybean cultivars with high carotenoid content.
基金funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.
文摘The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.
文摘Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.
文摘Deploying Large LanguageModel(LLM)-based agents in the Industrial Internet ofThings(IIoT)presents significant challenges,including high latency from cloud-based APIs,data privacy concerns,and the infeasibility of deploying monolithic models on resource-constrained edge devices.While smaller models(SLMs)are suitable for edge deployment,they often lack the reasoning power for complex,multi-step tasks.To address these issues,this paper introduces LEAF,a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge.LEAF employs a novel architecture where multiple expert SLMs—specialized for planning,execution,and interaction—work in concert,decomposing complex problems into manageable sub-tasks.To mitigate the resource overhead of this multi-model approach,LEAF implements an efficient parameter-sharing scheme based on Scalable Low-Rank Adaptation(S-LoRA).We introduce a two-stage training strategy combining Supervised Fine-Tuning(SFT)and Group Relative Policy Optimization(GRPO)to significantly enhance each expert’s capabilities.Furthermore,a Finite StateMachine(FSM)-based decision engine orchestrates the workflow,uniquely balancing deterministic control with intelligent flexibility,making it ideal for industrial environments that demand both reliability and adaptability.Experiments across diverse IIoT scenarios demonstrate that LEAF significantly outperforms baseline methods in both task success rate and user satisfaction.Notably,our fine-tuned 4-billion-parameter model achieves a task success rate over 90%in complex IIoT scenarios,demonstrating LEAF’s ability to deliver powerful and efficient autonomy at the industrial edge.
文摘TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection.
