Stable Isotopic Signatures of NO3 in Waste Water Effluent and Los Angeles River

A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary treatment before discharging into the LA River. We will gain a better understanding of the water quality in the LA River and the nitrate load in the watershed system by examining the influence of waste water treatment plants (WWTPs). The goal of this study is to pinpoint the exact source of nitrate in the LA River using the isotope signatures. We have selected sampling locations both upstream and downstream of the WWTP. This serves to monitor nitrate levels, aiding in the assessment of treatment plant effectiveness, pinpointing nitrate pollution sources, and ensuring compliance with environmental regulations. The research explores the isotopic composition of NO3 in relation to atmospheric nitrogen and Vienna Standard Mean Ocean Water, shedding light on the contributions from various sources such as manure, sewage, soil organic nitrogen, and nitrogen fertilizers. Specifically, there is a change in the δ15NAir value between the dry and wet seasons. The isotope values in the Tillman WWTP sample changed between dry and wet seasons. Notably, the presence of nitrate originating from manure and sewage is consistent across seasons, emphasizing the significant impact of anthropogenic and agricultural activities on water quality. This investigation contributes to the broader understanding of nitrogen cycling in urban water bodies, particularly in the context of wastewater effluent discharge. The findings hold implications for water quality management and highlight the need for targeted interventions to mitigate the impact of nitrogen-containing compounds on aquatic ecosystems. Overall, the study provides a valuable framework for future research and environmental stewardship efforts aimed at preserving the health and sustainability of urban water resources. This data informs decisions regarding additional treatment or mitigation actions to safeguard downstream water quality and ecosystem health.

Quantum-Resistant Multi-Feature Attribute-Based Proxy Re-Encryption Scheme for Cloud Services

Cloud-based services have powerful storage functions and can provide accurate computation.However,the question of how to guarantee cloud-based services access control and achieve data sharing security has always been a research highlight.Although the attribute-based proxy re-encryption(ABPRE)schemes based on number theory can solve this problem,it is still difficult to resist quantum attacks and have limited expression capabilities.To address these issues,we present a novel linear secret sharing schemes(LSSS)matrix-based ABPRE scheme with the fine-grained policy on the lattice in the research.Additionally,to detect the activities of illegal proxies,homomorphic signature(HS)technology is introduced to realize the verifiability of re-encryption.Moreover,the non-interactivity,unidirectionality,proxy transparency,multi-use,and anti-quantum attack characteristics of our system are all advantageous.Besides,it can efficiently prevent the loss of processing power brought on by repetitive authorisation and can enable precise and safe data sharing in the cloud.Furthermore,under the standard model,the proposed learning with errors(LWE)-based scheme was proven to be IND-sCPA secure.

Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches

Colorectal cancer(CRC)belongs to the class of significantly malignant tumors found in humans.Recently,dysregulated fatty acid metabolism(FAM)has been a topic of attention due to its modulation in cancer,specifically CRC.However,the regulatory FAM pathways in CRC require comprehensive elucidation.Methods:The clinical and gene expression data of 175 fatty acid metabolic genes(FAMGs)linked with colon adenocarcinoma(COAD)and normal cornerstone genes were gathered through The Cancer Genome Atlas(TCGA)-COAD corroborating with the Molecular Signature Database v7.2(MSigDB).Initially,crucial prognostic genes were selected by uni-and multi-variate Cox proportional regression analyses;then,depending upon these identified signature genes and clinical variables,a nomogram was generated.Lastly,to assess tumor immune characteristics,concomitant evaluation of tumor immune evasion/risk scoring were elucidated.Results:A 8-gene signature,including ACBD4,ACOX1,CD36,CPT2,ELOVL3,ELOVL6,ENO3,and SUCLG2,was generated,and depending upon this,CRC patients were categorized within high-risk(H-R)and low-risk(L-R)cohorts.Furthermore,risk and age-based nomograms indicated moderate discrimination and good calibration.The data confirmed that the 8-gene model efficiently predicted CRC patients’prognosis.Moreover,according to the conjoint analysis of tumor immune evasion and the risk scorings,the H-R cohort had an immunosuppressive tumor microenvironment,which caused a substandard prognosis.Conclusion:This investigation established a FAMGs-based prognostic model with substantially high predictive value,providing the possibility for improved individualized treatment for CRC individuals.

Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Genomic signatures of selection,local adaptation and production type characterisation of East Adriatic sheep breeds

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.

Urgent need for prognostic markers for hepatocellular carcinoma in the light of genomic instability and non-coding RNA signatures

In this editorial,we comment on an original article by Duan et al.Despite ad-vancements in the diagnosis and treatment of hepatocellular carcinoma(HCC),the identification of suitable prognostic factors remains challenging.In their paper,Duan et al identified long non-coding RNAs(LncRNAs)to quantify ge-nomic instability(GI)by combining LncRNA expression and somatic mutation profiles.They confirmed that the GI-derived LncRNA signature(GI-LncSig)could be an independent prognostic factor with the area under the curve of 0.773.Fur-thermore,the authors stated that GI-LncSig may have a better predictive perfor-mance than TP53 mutation status alone.However,studies exploring genetic markers for predicting the prognosis of HCC are crucial for identifying thera-peutic targets and enhancing diagnostic and treatment strategies to mitigate the global burden of liver cancer.

Molecular cascades and cell type-specific signatures in ASD revealed by single-cell genomics

Genomic profiling in postmortem brain from autistic individuals has consistently revealed convergent molecular changes.What drives these changes and how they relate to genetic susceptibility in this complex condition are not well understood.We performed deep single-nucleus RNA sequencing(snRNA-seq)to examine cell composition and transcriptomics,identifying dysregulation of cell type-specific gene regulatory networks(GRNs)in autism spectrum disorder(ASD).

Attribute-based signatures on lattices

Because of its wide application in anonymous authentication and attribute-based messaging, the attribute-based signature scheme has attracted the public attention since it was proposed in 2008. However, most of the existing attribute-based signature schemes are no longer secure in quantum era. Fortunately, lattice-based cryptography offers the hope of withstanding quantum computers. And lattices has elevated it to the status of a promising potential alternative to cryptography based on discrete log and factoring, owing to implementation simplicity, provable security reductions and quantum-immune. In this paper, the first lattice attribute-based signature scheme in random oracle model is proposed, which is proved existential unforgeability and perfect privacy. Compared with the current attribute-based signature schemes, our new attribute-based signature scheme can resist quantum attacks and has much shorter public-key size and signature size. Furthermore, this scheme is extended into an attribute-based signature scheme on number theory research unit（NTRU） lattice, which is also secure even in quantum era and has much higher efficiency than the former.

Morphodynamic signatures derived from daily surface elevation dynamics can explain the morphodynamic development of tidal flats

Understanding the sensitivity of tidal flats to environmental changes is challenging.Currently,most studies rely on process-based models to systematically explain the morphodynamic evolution of tidal flats.In this study,we proposed an alternative empirical approach to explore tidal flat dynamics using statistical indices based on long-term time series of daily surface elevation development.Surface elevation dynamic(SED)indices focus on the magnitude and period of surface elevation changes,while morphodynamic signature(MDS)indices relate sediment dynamics to environmental drivers.The statistical analyses were applied to an intervention site in the Netherlands to determine the effect of recently constructed groynes on the tidal flat.Using these analyses,we were able to(1)detect a reduction in the daily SED and(2)determine that the changes in the daily SED were predominantly caused by the reduction in wave impact between the groynes rather than the reduction in tidal currents.Overall,the presented results showed that the combination of novel statistical indices provides new insights into the trajectories of tidal flats,ecosystem functioning,and sensitivity to physical drivers(wind and tides).Finally,we suggested how the SED and MDS indices may help to explore the future trajectories and climate resilience of intertidal habitats.

Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination

Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT.However,data privacy is potentially risky when data is outsourced to cloud servers or edge services.While data encryption ensures data confidentiality,it can impede data sharing and retrieval.Attribute-based searchable encryption(ABSE)is proposed as an effective technique for enhancing data security and privacy.Nevertheless,ABSE has its limitations,such as single attribute authorization failure,privacy leakage during the search process,and high decryption overhead.This paper presents a novel approach called the blockchain-assisted efficientmulti-authority attribute-based searchable encryption scheme(BEM-ABSE)for cloudedge collaboration scenarios to address these issues.BEM-ABSE leverages a consortium blockchain to replace the central authentication center for global public parameter management.It incorporates smart contracts to facilitate reliable and fair ciphertext keyword search and decryption result verification.To minimize the computing burden on resource-constrained devices,BEM-ABSE adopts an online/offline hybrid mechanism during the encryption process and a verifiable edge-assisted decryption mechanism.This ensures both low computation cost and reliable ciphertext.Security analysis conducted under the random oracle model demonstrates that BEM-ABSE is resistant to indistinguishable chosen keyword attacks(IND-CKA)and indistinguishable chosen plaintext attacks(INDCPA).Theoretical analysis and simulation results confirm that BEM-ABSE significantly improves computational efficiency compared to existing solutions.

Population genomics reveals demographic history and selection signatures of hazelnut (Corylus)

Hazelnut(Corylus spp.)is known as one of the four famous tree nuts in the world due to its pleasant taste and nutritional benefits.However,hazelnut promotion worldwide is increasingly challenged by global climate change,limiting its production to a few regions.Focusing on the eurytopic Section Phyllochlamys,we conducted whole-genome resequencing of 125 diverse accessions from five geo-ecological zones in Eurasia to elucidate the genomic basis of adaptation and improvement.Population structure inference outlined five distinct genetic lineages corresponding to climate conditions and breeding background,and highlighted the differentiation between European and Asian lineages.Demographic dynamics and ecological niche modeling revealed that Pleistocene climatic oscillations dominantly shaped the extant genetic patterns,and multiple environmental factors have contributed to the lineage divergence.Whole-genome scans identified 279,111,and 164 selective sweeps that underlie local adaptation in Corylus heterophylla,Corylus kweichowensis,and Corylus yunnanensis,respectively.Relevant positively selected genes were mainly involved in regulating signaling pathways,growth and development,and stress resistance.The improvement signatures of hybrid hazelnut were concentrated in 312 and 316 selected genes,when compared to C.heterophylla and Corylus avellana,respectively,including those that regulate protein polymerization,photosynthesis,and response to water deprivation.Among these loci,22 candidate genes were highly associated with the regulation of biological quality.Our study provides insights into evolutionary processes and the molecular basis of how sibling species adapt to contrasting environments,and offers valuable resources for future climate-resilient breeding.

Whole-genome resequencing reveals recent signatures of selection in five populations of largemouth bass(Micropterus salmoides)

Largemouth bass(Micropterus salmoides) is an economically important fish species in North America, Europe, and China. Various genetic improvement programs and domestication processes have modified its genome sequence through selective pressure, leaving nucleotide signals that can be detected at the genomic level. In this study,we sequenced 149 largemouth bass fish, including protospecies(imported from the US) and improved breeds(four domestic breeding populations from China). We detected genomic regions harboring certain genes associated with improved traits, which may be useful molecular markers for practical domestication, breeding, and selection. Subsequent analyses of genetic diversity and population structure revealed that the improved breeds have undergone more rigorous genetic changes. Through selective signal analysis, we identified hundreds of putative selective sweep regions in each largemouth bass line. Interestingly, we predicted 103 putative candidate genes potentially subjected to selection,including several associated with growth(psst1 and grb10), early development(klf9, sp4, and sp8), and immune traits(pkn2, sept2, bcl6, and ripk2). These candidate genes represent potential genomic landmarks that could be used to improve important traits of biological and commercial interest. In summary, this study provides a genome-wide map of genetic variations and selection footprints in largemouth bass, which may benefit genetic studies and accelerate genetic improvement of this economically important fish.

Efficient Expressive Attribute-Based Encryption with Keyword Search over Prime-Order Groups

Attribute-based encryption with keyword search(ABEKS)is a novel cryptographic paradigm that can be used to implementfine-grained access control and retrieve ciphertexts without disclosing the sensitive information.It is a perfect combination of attribute-based encryption(ABE)and public key encryption with keyword search(PEKS).Nevertheless,most of the existing ABEKS schemes have limited search capabilities and only support single or simple conjunctive keyword search.Due to the weak search capability and inaccurate search results,it is difficult to apply these schemes to practical applications.In this paper,an effi-cient expressive ABEKS(EABEKS)scheme supporting unbounded keyword uni-verse over prime-order groups is designed,which supplies the expressive keyword search function supporting the logical connectives of“AND”and“OR”.The proposed scheme not only leads to low computation and communica-tion costs,but also supports unbounded keyword universe.In the standard model,the scheme is proven to be secure under the chosen keyword attack and the cho-sen plaintext attack.The comparison analysis and experimental results show that it has better performance than the existing EABEKS schemes in the storage,com-putation and communication costs.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Signature of Superconductivity in Pressurized La_(4)Ni_(3)O_(10)

The discovery of high-temperature superconductivity near 80K in bilayer nickelate La_(3)Ni_(2)O_(7)under high pressures has renewed the exploration of superconducting nickelate in bulk materials.The extension of superconductivity in other nickelates in a broader family is also essential.Here,we report the experimental observation of superconducting signature in trilayer nickelate La_(4)Ni_(3)O_(10)under high pressures.By using a modified solgel method and post-annealing treatment under high oxygen pressure,we successfully obtained polycrystalline La_(4)Ni_(3)O_(10)samples with different transport behaviors at ambient pressure.Then we performed high-pressure electrical resistance measurements on these samples in a diamond-anvil-cell apparatus.Surprisingly,the signature of possible superconducting transition with a maximum transition temperature(T_(c))of about 20K under high pressures is observed,as evidenced by a clear drop of resistance and the suppression of resistance drops under magnetic fields.Although the resistance drop is sample-dependent and relatively small,it appears in all of our measured samples.We argue that the observed superconducting signal is most likely to originate from the main phase of La_(4)Ni_(3)O_(10).Our findings will motivate the exploration of superconductivity in a broader family of nickelates and shed light on the understanding of the underlying mechanisms of high-T_(c) superconductivity in nickelates.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

A quantum blind signature scheme based on dense coding for non-entangled states

In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non-entangled quantum blind signature scheme based on dense encoding. The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources. After receiving particles, the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature. Then the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification.Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection,and higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature. By simultaneously utilizing the secondary distribution and rearrangement of non-entangled quantum states, a non-entangled quantum state representation of three bits of classical information is achieved, reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency while, at the same time, this scheme meets the requirements of unforgeability, non-repudiation, and prevention of information leakage.

A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients

BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours.Inflammation also affects the prognosis of GC patients.Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis.However,the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.AIM To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database.GC patients from the GSE26253 cohort were used for validation.Univariate and multivariate Cox analyses were used to determine the independent prognostic factors,and a prognostic nomogram was established.The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram.The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram.Gene set enrichment analysis was performed to explore the potential regulatory pathways involved.The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT.Finally,we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.RESULTS A prognostic model consisting of three inflammatory-related genes(MRPS17,GUF1,and PDK4)was constructed.Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients.According to the risk score,GC patients were stratified into high-and low-risk groups,and patients in the high-risk group had significantly worse prognoses according to age,sex,TNM stage and Lauren type.Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging.Finally,the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group,indicating a link between inflammation-related genes and drug sensitivity.CONCLUSION In conclusion,we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.

QBIoT:A Quantum Blockchain Framework for IoT with an Improved Proof-of-Authority Consensus Algorithm and a Public-Key Quantum Signature

The Internet of Things(IoT)is a network system that connects physical devices through the Internet,allowing them to interact.Nowadays,IoT has become an integral part of our lives,offering convenience and smart functionality.However,the growing number of IoT devices has brought about a corresponding increase in cybersecurity threats,such as device vulnerabilities,data privacy concerns,and network susceptibilities.Integrating blockchain technology with IoT has proven to be a promising approach to enhance IoT security.Nevertheless,the emergence of quantum computing poses a significant challenge to the security of traditional classical cryptography used in blockchain,potentially exposing it to quantum cyber-attacks.To support the growth of the IoT industry,mitigate quantum threats,and safeguard IoT data,this study proposes a robust blockchain solution for IoT that incorporates both classical and post-quantum security measures.Firstly,we present the Quantum-Enhanced Blockchain Architecture for IoT(QBIoT)to ensure secure data sharing and integrity protection.Secondly,we propose an improved Proof of Authority consensus algorithm called“Proof of Authority with Random Election”(PoARE),implemented within QBIoT for leader selection and new block creation.Thirdly,we develop a publickey quantum signature protocol for transaction verification in the blockchain.Finally,a comprehensive security analysis of QBIoT demonstrates its resilience against cyber threats from both classical and quantum adversaries.In summary,this research introduces an innovative quantum-enhanced blockchain solution to address quantum security concernswithin the realmof IoT.The proposedQBIoT framework contributes to the ongoing development of quantum blockchain technology and offers valuable insights for future research on IoT security.