Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerabl...Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.展开更多
Thin section and argon-ion polishing scanning electron microscope observations were used to analyze the sedimentary and diagenetic environments and main diagenesis of the Permian Fengcheng Formation shales in differen...Thin section and argon-ion polishing scanning electron microscope observations were used to analyze the sedimentary and diagenetic environments and main diagenesis of the Permian Fengcheng Formation shales in different depositional zones of Mahu Sag in the Junggar Basin,and to reconstruct their differential diagenetic evolutional processes.The diagenetic environment of shales in the lake-central zone kept alkaline,which mainly underwent the early stage(Ro<0.5%)dominated by the authigenesis of Na-carbonates and K-feldspar and the late stage(Ro>0.5%)dominated by the replacement of Na-carbonates by reedmergnerite.The shales from the marginal zone underwent a transition from weak alkaline to acidic diagenetic environments,with the early stage dominated by the authigenesis of Mg-bearing clay and silica and the late stage dominated by the dissolution of feldspar and carbonate minerals.The shales from the transitional zone also underwent a transition from an early alkaline diagenetic environment,evidenced by the formation of dolomite and zeolite,to a late acidic diagenetic environment,represented by the reedmergnerite replacement and silicification of feldspar and carbonate minerals.The differences in formation of authigenic minerals during early diagenetic stage determine the fracability of shales.The differences in dissolution of minerals during late diagenetic stage control the content of free shale oil.Dolomitic shale in the transitional zone and siltstone in the marginal zone have relatively high content of free shale oil and strong fracability,and are favorable“sweet spots”for shale oil exploitation and development.展开更多
Alzheimer’s disease is a prominent chronic neurodegenerative condition characterized by a gradual decline in memory leading to dementia.Growing evidence suggests that Alzheimer’s disease is associated with accumulat...Alzheimer’s disease is a prominent chronic neurodegenerative condition characterized by a gradual decline in memory leading to dementia.Growing evidence suggests that Alzheimer’s disease is associated with accumulating various amyloid-βoligomers in the brain,influenced by complex genetic and environmental factors.The memory and cognitive deficits observed during the prodromal and mild cognitive impairment phases of Alzheimer’s disease are believed to primarily result from synaptic dysfunction.Throughout life,environmental factors can lead to enduring changes in gene expression and the emergence of brain disorders.These changes,known as epigenetic modifications,also play a crucial role in regulating the formation of synapses and their adaptability in response to neuronal activity.In this context,we highlight recent advances in understanding the roles played by key components of the epigenetic machinery,specifically DNA methylation,histone modification,and microRNAs,in the development of Alzheimer’s disease,synaptic function,and activity-dependent synaptic plasticity.Moreover,we explore various strategies,including enriched environments,exposure to non-invasive brain stimulation,and the use of pharmacological agents,aimed at improving synaptic function and enhancing long-term potentiation,a process integral to epigenetic mechanisms.Lastly,we deliberate on the development of effective epigenetic agents and safe therapeutic approaches for managing Alzheimer’s disease.We suggest that addressing Alzheimer’s disease may require distinct tailored epigenetic drugs targeting different disease stages or pathways rather than relying on a single drug.展开更多
The timing of fruit maturity is an important trait in sweet cherry production and breeding.Phenotypic variation for phenology of fruit maturity in sweet cherry appears to be under strong genetic control,but that contr...The timing of fruit maturity is an important trait in sweet cherry production and breeding.Phenotypic variation for phenology of fruit maturity in sweet cherry appears to be under strong genetic control,but that control might be complicated by phenotypic instability across environments.Although such genotype-by-environment interaction(G×E)is a common phenomenon in crop plants,knowledge about it is lacking for fruit maturity timing and other sweet cherry traits.In this study,1673 genome-wide SNP markers were used to estimate genomic relationships among 597 weakly pedigree-connected individuals evaluated over two seasons at three locations in Europe and one location in the USA,thus sampling eight‘environments’.The combined dataset enabled a single meta-analysis to investigate the environmental stability of genomic predictions.Linkage disequilibrium among marker loci declined rapidly with physical distance,and ordination of the relationship matrix suggested no strong structure among germplasm.The most parsimonious G×E model allowed heterogeneous genetic variance and pairwise covariances among environments.Narrow-sense genomic heritability was very high(0.60–0.83),as was accuracy of predicted breeding values(>0.62).Average correlation of additive effects among environments was high(0.96)and breeding values were highly correlated across locations.Results indicated that genomic models can be used in cherry to accurately predict date of fruit maturity for untested individuals in new environments.Limited G×E for this trait indicated that phenotypes of individuals will be stable across similar environments.Equivalent analyses for other sweet cherry traits,for which multiple years of data are commonly available among breeders and cultivar testers,would be informative for predicting performance of elite selections and cultivars in new environments.展开更多
An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module ...An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.展开更多
Gas-bearing deposits in the Lower Mingyuefeng Formation of Paleogene, Lishui Sag, East China Sea Shelf Basin consist of shoreface sandstones of the highstand systems tract(HST) and transgressive systems tract(TST)...Gas-bearing deposits in the Lower Mingyuefeng Formation of Paleogene, Lishui Sag, East China Sea Shelf Basin consist of shoreface sandstones of the highstand systems tract(HST) and transgressive systems tract(TST), and deltaic sandstones of the lowstand systems tract(LST) and falling stage systems tract(FSST).Detailed petrographic observations suggest that the diagenetic features and related evolution of these deposits cannot be simply characterized and demonstrated in the depth domain.However, the occurrence of diagenetic minerals systematically depends on the studied interval within the HST, TST, LST, and FSST; therefore, diagenesis in this region can be better constrained when studied in the context of the depositional environments and sequence stratigraphic framework.The eogenetic processes in such settings include:(1) microcrystalline siderite precipitated as concretions in almost all environments and systems tracts, which inhibited further mechanical compaction;(2) grain dissolution and kaolinitization occurred in shoreface HST sandstones and deltaic LST and FSST sandstones;(3) glaucony was locally observed, which did not clearly reflect the controls of facies or sequence stratigraphy; and(4) cementation by pyrite aggregates occurred in the shoreface HST sandstones and deltaic LST sandstones.The mesogenetic diagenesis includes:(1) partial conversion of kaolinite into dickite in deltaic LST sandstones, and minor chlorite cementation in deltaic FSST sandstones;(2) transformation of kaolinite into illite and quartz cementation in deltaic LST and FSST sandstones;(3) frequent precipitation of ankerite and ferroan calcite in shoreface TST sandstones and early HST sandstones, forming baffles and barriers for fluid flow, with common calcite in shoreface HST sandstones as a late diagenetic cement; and(4) formation of dawsonite in the deltaic LST and FSST sandstones, which is interpreted to be a product of the invasion of a CO2-rich fluid, and acts as a good indicator of CO2-bearing reservoirs.This study has thus constructed a reliable conceptual model to describe the spatial and temporal distribution of diagenetic alterations.The results may provide an entirely new conceptual framework and methodology for successful gas exploration in the continental margins of offshore China, thus allowing us to predict and unravel the distribution and quality evolution of clastic reservoirs at a more detailed and reliable scale.展开更多
Marine environments have a considerable influence on the construction of the Chinese 21st Century Maritime Silk Road.Thus,an objective and quantitative risk assessment of marine environments has become a key problem t...Marine environments have a considerable influence on the construction of the Chinese 21st Century Maritime Silk Road.Thus,an objective and quantitative risk assessment of marine environments has become a key problem that must be solved urgently.To deal with the uncertainty in marine environmental risks caused by complex factors and fuzzy mechanisms,a new assessment technique based on a weighted Bayesian network(BN)is proposed.Through risk factor analysis,node selection,structure construc-tion,and parameter learning,we apply the proposed weighted BN-based assessment model for the risk assessment and zonation of marine environments along the Maritime Silk Road.Results show that the model effectively fuses multisource and uncertain envi-ronmental information and provides reasonable risk assessment results,thereby offering technical support for risk prevention and disaster mitigation along the Maritime Silk Road.展开更多
This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distri...This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distributions taking value of 0 or 1, the data transmission model is obtained. Based on state augmentation and stochastic theory, the sufficient condition for robust stability with H ∞ constraints is derived for the filtering error system. The robust filter is designed in terms of feasibility of one certain linear matrix inequality (LMI), which is formed by adopting matrix congruence transformations. A numerical example is given to show the effectiveness of the proposed filtering method.展开更多
The electromagnetic environment of laneways in underground coal mines is an important area for the design of new electronic products,as well as a fundamental space for mine monitoring,surveillance,communications and c...The electromagnetic environment of laneways in underground coal mines is an important area for the design of new electronic products,as well as a fundamental space for mine monitoring,surveillance,communications and control systems.An investigation of electromagnetic interference in coal mines is essential for the enhancement of performances of these systems.In this study,a new field method is provided in which radiated emission tests in coal mine laneways have been carried out.We conclude that:1) the wiring motor vehicles can radiate interference with a bandwidth up to 1 GHz and with an amplitude 10 dBμV/m higher than the background noise;2) the PHS(Personal Handy phone System) mobile communication system can cause interference 40 dBμV/m higher than the background noise;3) an interference 25 dBμV/m higher than the background noise can be generated during the communication at a working bandwidth of 48.8 MHz;and 4) power cables,battery vehicles as well as mechanical and electrical dong rooms have little effect on the electromagnetic radiation environment in coal mine tunnels.展开更多
Understanding the variation for the expressionof genes in different environments is one of themajor goals in qualitative genetics. In this pa-per, the genetic models for quantitative traitsof endosperm in cereal crops...Understanding the variation for the expressionof genes in different environments is one of themajor goals in qualitative genetics. In this pa-per, the genetic models for quantitative traitsof endosperm in cereal crops were used to eval-uate the seed, cytoplasm and maternal geneticeffects as well as the genotype × environment(GE) interaction effects, and to predict thebreeding value of parents and genotypic corre-lation for nutrient quality traits of indica rice (Oryza sativa L.).展开更多
Virtual reality (VR) is a rapidly developing technology that has a wide spectrum of industrial and commercial applications. Networked (distributed or shared) virtual environments (VE) are of growing interest to modern...Virtual reality (VR) is a rapidly developing technology that has a wide spectrum of industrial and commercial applications. Networked (distributed or shared) virtual environments (VE) are of growing interest to modern manufacturing industry; a dominating use of networked virtual manufacturing environments (VMEs) is on-line visualisation and collaborative control of 3D information. This has to be supported by real-time data transfer. To meet a broad range of common requirements for Internet-based VE communications, particularly for virtual manufacturing and collaborative design and control, this paper presents a networked virtual environment system that is designed to support networked virtual design and manufacturing. The system is implemented with manufacturing message specification (MMS) standards so as to integrate a range of manufacturing services into networked VEs over the Internet.展开更多
BACKGROUND Preschoolers become anxious when they are about to undergo anesthesia and surgery,warranting the development of more appropriate and effective interventions.AIM To explore the effect of static cartoons comb...BACKGROUND Preschoolers become anxious when they are about to undergo anesthesia and surgery,warranting the development of more appropriate and effective interventions.AIM To explore the effect of static cartoons combined with dynamic virtual environments on preoperative anxiety and anesthesia induction compliance in preschool-aged children undergoing surgery.METHODS One hundred and sixteen preschool-aged children were selected and assigned to the drug(n=37),intervention(n=40),and control(n=39)groups.All the children received routine preoperative checkups and nursing before being transferred to the preoperative preparation room on the day of the operation.The drug group received 0.5 mg/kg midazolam and the intervention group treatment consisting of static cartoons combined with dynamic virtual environments.The control group received no intervention.The modified Yale Preoperative Anxiety Scale was used to evaluate the children’s anxiety level on the day before surgery(T0),before leaving the preoperative preparation room(T1),when entering the operating room(T2),and at anesthesia induction(T3).Compliance during anesthesia induction(T3)was evaluated using the Induction Compliance Checklist(ICC).Changes in mean arterial pressure(MAP),heart rate(HR),and respiratory rate(RR)were also recorded at each time point.RESULTS The anxiety scores of the three groups increased variously at T1 and T2.At T3,both the drug and intervention groups had similar anxiety scores,both of which were lower than those in the control group.At T1 and T2,MAP,HR,and RR of the three groups increased.The drug and control groups had significantly higher MAP and RR than the intervention group at T2.At T3,the MAP,HR,and RR of the drug group decreased and were significantly lower than those in the control group but were comparable to those in the intervention group.Both the drug and intervention groups had similar ICC scores and duration of anesthesia induction(T3),both of which were higher than those of the control group.CONCLUSION Combining static cartoons with dynamic virtual environments as effective as medication,specifically midazolam,in reducing preoperative anxiety and fear in preschool-aged children.This approach also improve their compliance during anesthesia induction and helped maintain their stable vital signs.展开更多
Barley has been in use as food and fodder for thousands of years. and also used currently as the most important raw material in malting and brewing. The quality of malt barley is not much satisfied with the requiremen...Barley has been in use as food and fodder for thousands of years. and also used currently as the most important raw material in malting and brewing. The quality of malt barley is not much satisfied with the requirement by malting and brewing industry worldwide, although the great effort has been devoted to its improvement through breeding and agronomy. The quality of malt barley is involved in many physical and chemical traits, including hydrolytic enzymes. Of them, kernel protein content, beta-amylase activity, beta-glucan content and kernel plumpness have dramatic influence on malting or brewing quality. The expression of these quality parameters varies greatly in genotypes and is easily changed by diverse environments and agronomic practices. This paper reviewed the important role of various parameters influencing the quality of malt barley and the effect of different factors on them.展开更多
Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article...Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article,we have reviewed the representative neural network accelerators.As an entirety,the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end performance.And we summarize the programming environments of neural network accelerators and optimizations in software stack.Finally,we comment the future trend of neural network accelerator and programming environments.展开更多
Wireless body area networks (WBANs) use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, a...Wireless body area networks (WBANs) use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.展开更多
The aim of this work was to analyze the effect of the magnetic field generated by the household appliances on the airborne microbial surrounding these equipment located on indoor environments with particular interest ...The aim of this work was to analyze the effect of the magnetic field generated by the household appliances on the airborne microbial surrounding these equipment located on indoor environments with particular interest in the environmental fungi.A simultaneous environmental study was carried out in locals of three different geographical places of Havana,Cuba,which have televisions,computers and an electric generator.The air samples were made by a sedimentation method using Malt Extract Agar.The concentration of total aerobic mesophilic as well as fungi and yeasts were determined in rainy and little rainy seasons by applying as factors:exposure time of dishes(5 to 60 min)and distance to the wall(0 and 1 m)at a height of 1 m above the floor.The predominant fungal genera were Cladosporium,Penicillium and Aspergillus.In the dishes that were placed at 0 and 0.5 m from the emitting sources were observed that some bacteria colonies formed inhibition halos,a great diversity of filamentous fungi and an increase in the mycelium pigmentation as well as the pigments excretion.In the rainy season,the highest amounts of fungi were obtained in all samples.In the little rain season the count of the Gram-negative bacilli increased three times the Gram-positive cocci.展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi...Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.展开更多
We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is know...We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.展开更多
As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an ...As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.展开更多
文摘Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.
基金Supported by the National Natural Science Foundation of China(42272117,42002116).
文摘Thin section and argon-ion polishing scanning electron microscope observations were used to analyze the sedimentary and diagenetic environments and main diagenesis of the Permian Fengcheng Formation shales in different depositional zones of Mahu Sag in the Junggar Basin,and to reconstruct their differential diagenetic evolutional processes.The diagenetic environment of shales in the lake-central zone kept alkaline,which mainly underwent the early stage(Ro<0.5%)dominated by the authigenesis of Na-carbonates and K-feldspar and the late stage(Ro>0.5%)dominated by the replacement of Na-carbonates by reedmergnerite.The shales from the marginal zone underwent a transition from weak alkaline to acidic diagenetic environments,with the early stage dominated by the authigenesis of Mg-bearing clay and silica and the late stage dominated by the dissolution of feldspar and carbonate minerals.The shales from the transitional zone also underwent a transition from an early alkaline diagenetic environment,evidenced by the formation of dolomite and zeolite,to a late acidic diagenetic environment,represented by the reedmergnerite replacement and silicification of feldspar and carbonate minerals.The differences in formation of authigenic minerals during early diagenetic stage determine the fracability of shales.The differences in dissolution of minerals during late diagenetic stage control the content of free shale oil.Dolomitic shale in the transitional zone and siltstone in the marginal zone have relatively high content of free shale oil and strong fracability,and are favorable“sweet spots”for shale oil exploitation and development.
基金supported by a grant from the Massachusetts Alzheimer’s Disease Research Center(5P50 AG 005134)(to SL).
文摘Alzheimer’s disease is a prominent chronic neurodegenerative condition characterized by a gradual decline in memory leading to dementia.Growing evidence suggests that Alzheimer’s disease is associated with accumulating various amyloid-βoligomers in the brain,influenced by complex genetic and environmental factors.The memory and cognitive deficits observed during the prodromal and mild cognitive impairment phases of Alzheimer’s disease are believed to primarily result from synaptic dysfunction.Throughout life,environmental factors can lead to enduring changes in gene expression and the emergence of brain disorders.These changes,known as epigenetic modifications,also play a crucial role in regulating the formation of synapses and their adaptability in response to neuronal activity.In this context,we highlight recent advances in understanding the roles played by key components of the epigenetic machinery,specifically DNA methylation,histone modification,and microRNAs,in the development of Alzheimer’s disease,synaptic function,and activity-dependent synaptic plasticity.Moreover,we explore various strategies,including enriched environments,exposure to non-invasive brain stimulation,and the use of pharmacological agents,aimed at improving synaptic function and enhancing long-term potentiation,a process integral to epigenetic mechanisms.Lastly,we deliberate on the development of effective epigenetic agents and safe therapeutic approaches for managing Alzheimer’s disease.We suggest that addressing Alzheimer’s disease may require distinct tailored epigenetic drugs targeting different disease stages or pathways rather than relying on a single drug.
基金supported by the USDA National Institute of Food and Agriculture(NIFA)-Specialty Crop Research Initiative project,‘RosBREED:Combining disease resistance with horticultural quality in new rosaceous cultivars’(grant number 2014-51181-22378).
文摘The timing of fruit maturity is an important trait in sweet cherry production and breeding.Phenotypic variation for phenology of fruit maturity in sweet cherry appears to be under strong genetic control,but that control might be complicated by phenotypic instability across environments.Although such genotype-by-environment interaction(G×E)is a common phenomenon in crop plants,knowledge about it is lacking for fruit maturity timing and other sweet cherry traits.In this study,1673 genome-wide SNP markers were used to estimate genomic relationships among 597 weakly pedigree-connected individuals evaluated over two seasons at three locations in Europe and one location in the USA,thus sampling eight‘environments’.The combined dataset enabled a single meta-analysis to investigate the environmental stability of genomic predictions.Linkage disequilibrium among marker loci declined rapidly with physical distance,and ordination of the relationship matrix suggested no strong structure among germplasm.The most parsimonious G×E model allowed heterogeneous genetic variance and pairwise covariances among environments.Narrow-sense genomic heritability was very high(0.60–0.83),as was accuracy of predicted breeding values(>0.62).Average correlation of additive effects among environments was high(0.96)and breeding values were highly correlated across locations.Results indicated that genomic models can be used in cherry to accurately predict date of fruit maturity for untested individuals in new environments.Limited G×E for this trait indicated that phenotypes of individuals will be stable across similar environments.Equivalent analyses for other sweet cherry traits,for which multiple years of data are commonly available among breeders and cultivar testers,would be informative for predicting performance of elite selections and cultivars in new environments.
基金Projects(61573213,61473174,61473179)supported by the National Natural Science Foundation of ChinaProjects(ZR2015PF009,ZR2014FM007)supported by the Natural Science Foundation of Shandong Province,China+1 种基金Project(2014GGX103038)supported by the Shandong Province Science and Technology Development Program,ChinaProject(2014ZZCX04302)supported by the Special Technological Program of Transformation of Initiatively Innovative Achievements in Shandong Province,China
文摘An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.
基金Financial support was provided by the National Science and Technology Major Project of China (No.2011ZX05023-002-003)
文摘Gas-bearing deposits in the Lower Mingyuefeng Formation of Paleogene, Lishui Sag, East China Sea Shelf Basin consist of shoreface sandstones of the highstand systems tract(HST) and transgressive systems tract(TST), and deltaic sandstones of the lowstand systems tract(LST) and falling stage systems tract(FSST).Detailed petrographic observations suggest that the diagenetic features and related evolution of these deposits cannot be simply characterized and demonstrated in the depth domain.However, the occurrence of diagenetic minerals systematically depends on the studied interval within the HST, TST, LST, and FSST; therefore, diagenesis in this region can be better constrained when studied in the context of the depositional environments and sequence stratigraphic framework.The eogenetic processes in such settings include:(1) microcrystalline siderite precipitated as concretions in almost all environments and systems tracts, which inhibited further mechanical compaction;(2) grain dissolution and kaolinitization occurred in shoreface HST sandstones and deltaic LST and FSST sandstones;(3) glaucony was locally observed, which did not clearly reflect the controls of facies or sequence stratigraphy; and(4) cementation by pyrite aggregates occurred in the shoreface HST sandstones and deltaic LST sandstones.The mesogenetic diagenesis includes:(1) partial conversion of kaolinite into dickite in deltaic LST sandstones, and minor chlorite cementation in deltaic FSST sandstones;(2) transformation of kaolinite into illite and quartz cementation in deltaic LST and FSST sandstones;(3) frequent precipitation of ankerite and ferroan calcite in shoreface TST sandstones and early HST sandstones, forming baffles and barriers for fluid flow, with common calcite in shoreface HST sandstones as a late diagenetic cement; and(4) formation of dawsonite in the deltaic LST and FSST sandstones, which is interpreted to be a product of the invasion of a CO2-rich fluid, and acts as a good indicator of CO2-bearing reservoirs.This study has thus constructed a reliable conceptual model to describe the spatial and temporal distribution of diagenetic alterations.The results may provide an entirely new conceptual framework and methodology for successful gas exploration in the continental margins of offshore China, thus allowing us to predict and unravel the distribution and quality evolution of clastic reservoirs at a more detailed and reliable scale.
基金This study is supported by the Chinese National Natu-ral Science Fundation(Nos.41976188,41775165)the Chinese National Natural Science Fundation of Jiangsu Province(No.BK20161464)the Graduate Research and Innovation Project of Hunan Province(No.CX20200009).
文摘Marine environments have a considerable influence on the construction of the Chinese 21st Century Maritime Silk Road.Thus,an objective and quantitative risk assessment of marine environments has become a key problem that must be solved urgently.To deal with the uncertainty in marine environmental risks caused by complex factors and fuzzy mechanisms,a new assessment technique based on a weighted Bayesian network(BN)is proposed.Through risk factor analysis,node selection,structure construc-tion,and parameter learning,we apply the proposed weighted BN-based assessment model for the risk assessment and zonation of marine environments along the Maritime Silk Road.Results show that the model effectively fuses multisource and uncertain envi-ronmental information and provides reasonable risk assessment results,thereby offering technical support for risk prevention and disaster mitigation along the Maritime Silk Road.
基金supported by National Natural Science Foundation of China (No. 61004088)the Key Foundation for Basic Research from Science and Technology Commission of Shanghai (No. 09JC1408000)the Aeronautic Science Foundation of China (No. 20100157001)
文摘This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distributions taking value of 0 or 1, the data transmission model is obtained. Based on state augmentation and stochastic theory, the sufficient condition for robust stability with H ∞ constraints is derived for the filtering error system. The robust filter is designed in terms of feasibility of one certain linear matrix inequality (LMI), which is formed by adopting matrix congruence transformations. A numerical example is given to show the effectiveness of the proposed filtering method.
基金supported by the National Natural Science Foundation of China (No.50674093)the National Scientific and Technological Support Projects (No.2006BAK03B00) and the Pingdingshan Coal Mine Group
文摘The electromagnetic environment of laneways in underground coal mines is an important area for the design of new electronic products,as well as a fundamental space for mine monitoring,surveillance,communications and control systems.An investigation of electromagnetic interference in coal mines is essential for the enhancement of performances of these systems.In this study,a new field method is provided in which radiated emission tests in coal mine laneways have been carried out.We conclude that:1) the wiring motor vehicles can radiate interference with a bandwidth up to 1 GHz and with an amplitude 10 dBμV/m higher than the background noise;2) the PHS(Personal Handy phone System) mobile communication system can cause interference 40 dBμV/m higher than the background noise;3) an interference 25 dBμV/m higher than the background noise can be generated during the communication at a working bandwidth of 48.8 MHz;and 4) power cables,battery vehicles as well as mechanical and electrical dong rooms have little effect on the electromagnetic radiation environment in coal mine tunnels.
文摘Understanding the variation for the expressionof genes in different environments is one of themajor goals in qualitative genetics. In this pa-per, the genetic models for quantitative traitsof endosperm in cereal crops were used to eval-uate the seed, cytoplasm and maternal geneticeffects as well as the genotype × environment(GE) interaction effects, and to predict thebreeding value of parents and genotypic corre-lation for nutrient quality traits of indica rice (Oryza sativa L.).
文摘Virtual reality (VR) is a rapidly developing technology that has a wide spectrum of industrial and commercial applications. Networked (distributed or shared) virtual environments (VE) are of growing interest to modern manufacturing industry; a dominating use of networked virtual manufacturing environments (VMEs) is on-line visualisation and collaborative control of 3D information. This has to be supported by real-time data transfer. To meet a broad range of common requirements for Internet-based VE communications, particularly for virtual manufacturing and collaborative design and control, this paper presents a networked virtual environment system that is designed to support networked virtual design and manufacturing. The system is implemented with manufacturing message specification (MMS) standards so as to integrate a range of manufacturing services into networked VEs over the Internet.
基金Supported by Hangzhou Medical and Health Technology Project,No.OO20191141。
文摘BACKGROUND Preschoolers become anxious when they are about to undergo anesthesia and surgery,warranting the development of more appropriate and effective interventions.AIM To explore the effect of static cartoons combined with dynamic virtual environments on preoperative anxiety and anesthesia induction compliance in preschool-aged children undergoing surgery.METHODS One hundred and sixteen preschool-aged children were selected and assigned to the drug(n=37),intervention(n=40),and control(n=39)groups.All the children received routine preoperative checkups and nursing before being transferred to the preoperative preparation room on the day of the operation.The drug group received 0.5 mg/kg midazolam and the intervention group treatment consisting of static cartoons combined with dynamic virtual environments.The control group received no intervention.The modified Yale Preoperative Anxiety Scale was used to evaluate the children’s anxiety level on the day before surgery(T0),before leaving the preoperative preparation room(T1),when entering the operating room(T2),and at anesthesia induction(T3).Compliance during anesthesia induction(T3)was evaluated using the Induction Compliance Checklist(ICC).Changes in mean arterial pressure(MAP),heart rate(HR),and respiratory rate(RR)were also recorded at each time point.RESULTS The anxiety scores of the three groups increased variously at T1 and T2.At T3,both the drug and intervention groups had similar anxiety scores,both of which were lower than those in the control group.At T1 and T2,MAP,HR,and RR of the three groups increased.The drug and control groups had significantly higher MAP and RR than the intervention group at T2.At T3,the MAP,HR,and RR of the drug group decreased and were significantly lower than those in the control group but were comparable to those in the intervention group.Both the drug and intervention groups had similar ICC scores and duration of anesthesia induction(T3),both of which were higher than those of the control group.CONCLUSION Combining static cartoons with dynamic virtual environments as effective as medication,specifically midazolam,in reducing preoperative anxiety and fear in preschool-aged children.This approach also improve their compliance during anesthesia induction and helped maintain their stable vital signs.
基金the National Natural Science Foundation of China(30070434,30270779)863 Program of China(2001AA2412)for their support to this research.
文摘Barley has been in use as food and fodder for thousands of years. and also used currently as the most important raw material in malting and brewing. The quality of malt barley is not much satisfied with the requirement by malting and brewing industry worldwide, although the great effort has been devoted to its improvement through breeding and agronomy. The quality of malt barley is involved in many physical and chemical traits, including hydrolytic enzymes. Of them, kernel protein content, beta-amylase activity, beta-glucan content and kernel plumpness have dramatic influence on malting or brewing quality. The expression of these quality parameters varies greatly in genotypes and is easily changed by diverse environments and agronomic practices. This paper reviewed the important role of various parameters influencing the quality of malt barley and the effect of different factors on them.
基金partially supported by the National Key Research and Development Program of China (under Grant 2017YFB1003101, 2018AAA0103300, 2017YFA0700900, 2017YFA0700902, 2017YFA0700901)the National Natural Science Foundation of China (under Grant 61732007, 61432016, 61532016, 61672491, 61602441, 61602446, 61732002, 61702478, and 61732020)+6 种基金Beijing Natural Science Foundation (JQ18013)National Science and Technology Major Project (2018ZX01031102)the Transformation and Transferof Scientific and Technological Achievements of Chinese Academy of Sciences (KFJ-HGZX-013)Key Research Projects in Frontier Science of Chinese Academy of Sciences (QYZDBSSW-JSC001)Strategic Priority Research Program of Chinese Academy of Science (XDB32050200, XDC01020000)Standardization Research Project of Chinese Academy of Sciences (BZ201800001)Beijing Academy of Artificial Intelligence (BAAI) and Beijing Nova Program of Science and Technology (Z191100001119093)
文摘Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article,we have reviewed the representative neural network accelerators.As an entirety,the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end performance.And we summarize the programming environments of neural network accelerators and optimizations in software stack.Finally,we comment the future trend of neural network accelerator and programming environments.
文摘Wireless body area networks (WBANs) use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.
基金the Ministry of Science,Technology and Environment,Cuba(PCA-2118025001)。
文摘The aim of this work was to analyze the effect of the magnetic field generated by the household appliances on the airborne microbial surrounding these equipment located on indoor environments with particular interest in the environmental fungi.A simultaneous environmental study was carried out in locals of three different geographical places of Havana,Cuba,which have televisions,computers and an electric generator.The air samples were made by a sedimentation method using Malt Extract Agar.The concentration of total aerobic mesophilic as well as fungi and yeasts were determined in rainy and little rainy seasons by applying as factors:exposure time of dishes(5 to 60 min)and distance to the wall(0 and 1 m)at a height of 1 m above the floor.The predominant fungal genera were Cladosporium,Penicillium and Aspergillus.In the dishes that were placed at 0 and 0.5 m from the emitting sources were observed that some bacteria colonies formed inhibition halos,a great diversity of filamentous fungi and an increase in the mycelium pigmentation as well as the pigments excretion.In the rainy season,the highest amounts of fungi were obtained in all samples.In the little rain season the count of the Gram-negative bacilli increased three times the Gram-positive cocci.
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
基金supported by the National Science Foundation of China under Grant No.62101467.
文摘Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.
基金Project supported by Indian Institute of Engineering Science and Technology, Shibpur, India
文摘We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.
基金funding from Deanship of Scientific Research in King Faisal University with Grant Number KFU241648.
文摘As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.