Malaysia,as one of the highest producers of palm oil globally and one of the largest exporters,has a huge potential to use palmoil waste to generate electricity since an abundance of waste is produced during the palmo...Malaysia,as one of the highest producers of palm oil globally and one of the largest exporters,has a huge potential to use palmoil waste to generate electricity since an abundance of waste is produced during the palmoil extraction process.In this paper,we have first examined and compared the use of palmoil waste as biomass for electricity generation in different countries with reference to Malaysia.Some areas with default accessibility in rural areas,like those in Sabah and Sarawak,require a cheap and reliable source of electricity.Palm oil waste possesses the potential to be the source.Therefore,this research examines the cost-effective comparison between electricity generated frompalm oil waste and standalone diesel electric generation in Marudi,Sarawak,Malaysia.This research aims to investigate the potential electricity generation using palm oil waste and the feasibility of implementing the technology in rural areas.To implement and analyze the feasibility,a case study has been carried out in a rural area in Sarawak,Malaysia.The finding shows the electricity cost calculation of small towns like Long Lama,Long Miri,and Long Atip,with ten nearby schools,and suggests that using EFB from palm oil waste is cheaper and reduces greenhouse gas emissions.The study also points out the need to conduct further research on power systems,such as energy storage andmicrogrids,to better understand the future of power systems.By collecting data through questionnaires and surveys,an analysis has been carried out to determine the approximate cost and quantity of palm oil waste to generate cheaper renewable energy.We concluded that electricity generation from palm oil waste is cost-effective and beneficial.The infrastructure can be a microgrid connected to the main grid.展开更多
Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physi...Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.展开更多
In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not ...In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments.This paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key technologies.Additionally,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use cases.The paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.展开更多
AIM To identify demographic, clinical, metabolomic, and lifestyle related predictors of relapse in adult ulcerative colitis(UC) patients.METHODS In this prospective pilot study, UC patients in clinical remission were ...AIM To identify demographic, clinical, metabolomic, and lifestyle related predictors of relapse in adult ulcerative colitis(UC) patients.METHODS In this prospective pilot study, UC patients in clinical remission were recruited and followed-up at 12 mo to assess a clinical relapse, or not. At baseline information on demographic and clinical parameters was collected. Serum and urine samples were collected for analysis of metabolomic assays using a combined direct infusion/liquid chromatography tandem mass spectrometry and nuclear magnetic resolution spectroscopy. Stool samples were also collected to measure fecal calprotectin(FCP). Dietary assessment was performed using a validated self-administered food frequency questionnaire. RESULTS Twenty patients were included(mean age: 42.7 ± 14.8 years, females: 55%). Seven patients(35%) experienced a clinical relapse during the follow-up period. While 6 patients(66.7%) with normal body weight developed a clinical relapse, 1 UC patient(9.1%) who was overweight/obese relapsed during the follow-up(P = 0.02). At baseline, poultry intake was significantly higher in patients who were still in remission during follow-up(0.9 oz vs 0.2 oz, P = 0.002). Five patients(71.4%) with FCP > 150 μg/g and 2 patients(15.4%) with normal FCP(≤ 150 μg/g) at baseline relapsed during the follow-up(P = 0.02). Interestingly, baseline urinary and serum metabolomic profiling of UC patients with or without clinical relapse within 12 mo showed a significant difference. The most important metabolites that were responsible for this discrimination were trans-aconitate, cystine and acetamide in urine, and 3-hydroxybutyrate, acetoacetate and acetone in serum. CONCLUSION A combination of baseline dietary intake, fecal calprotectin, and metabolomic factors are associated with risk of UC clinical relapse within 12 mo.展开更多
Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder(MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitiv...Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder(MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia(FES), 125 with MDD, and 237 demographically-matched healthy controls(HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with aone-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD.Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.展开更多
In this paper, the generalized Dodd-Bullough-Mikhailov equation is studied. The existence of periodic wave and unbounded wave solutions is proved by using the method of bifurcation theory of dynamical systems. Under d...In this paper, the generalized Dodd-Bullough-Mikhailov equation is studied. The existence of periodic wave and unbounded wave solutions is proved by using the method of bifurcation theory of dynamical systems. Under different parametric conditions, various sufficient conditions to guarantee the existence of the above solutions are given.Some exact explicit parametric representations of the above travelling solutions are obtained.展开更多
Genetic improvement for drought stress tolerance in rice involves the quantitative nature of the trait, which reflects the additive effects of several genetic loci throughout the genome. Yield components and related t...Genetic improvement for drought stress tolerance in rice involves the quantitative nature of the trait, which reflects the additive effects of several genetic loci throughout the genome. Yield components and related traits under stressed and well-water conditions were assayed in mapping populations derived from crosses of Azucena×IR64 and Azucena×Bala. To find the candidate rice genes underlying Quantitative Trait Loci (QTL) in these populations, we conducted in silico analysis of a candidate region flanked by the genetic markers RM212 and RM319 on chromosome 1, proximal to the semi-dwarf (sd1) locus. A total of 175 annotated genes were identified from this region. These included 48 genes annotated by functional homology to known genes, 23 pseudogenes, 24 ab initio predicted genes supported by an alignment match to an EST (Expressed sequence tag) of unknown function, and 80 hypothetical genes predicted solely by ab initio means. Among these, 16 candidate genes could potentially be involved in drought stress response.展开更多
Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services w...Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.展开更多
In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards ad...In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence（AI） and machine learning（ML） can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements. For this purpose, a survey was conducted in the target group. The software categories presented include vendor-provided software, robotic software frameworks（RSF）, scientific software and in-house developed software（IHDS）. Typical representatives for each category are described in detail, including Smar Act precision tool commander, Math Works Matlab and national instruments Lab VIEW, as well as the robot operating system（ROS）. The identified software categories and their representatives are rated for end user satisfaction based on functional and non-functional requirements, recommendations and learning curves. The paper concludes with a recommendation of ROS as a basis for future work.展开更多
Interconnection networks are hardware fabrics supporting communications between individual processors in multi- computers. The low-dimensional k-ary n-cubes （or torus） with adaptive wormhole switching have attracted...Interconnection networks are hardware fabrics supporting communications between individual processors in multi- computers. The low-dimensional k-ary n-cubes （or torus） with adaptive wormhole switching have attracted significant research efforts to construct high-performance interconnection networks in contemporary multi-computers. The arrival process and destination distribution of messages have great effects on network performance. With the aim of capturing the characteristics of the realistic traffic pattern and obtaining a deep understanding of the performance behaviour of interconneetion networks, this paper presents an analytical model to investigate the message latency in adaptive-routed wormhole-switched torus networks where there exists hot-spot nodes and the message arrivals follow a batch arrival process. Each generated message has a given probability to be directed to the hot-spot node. The average degree of virtual channel multiplexing is computed by the GE/G/1/V queueing system with finite buffer capacity. We compare analytical results of message latency with those obtained through the simulation experiments in order to validate the accuracy of the derived model.展开更多
An unavoidable but reusable waste so as to enhance a more circular waste utilization has been spent potlining(SPL) generated by the aluminum industry.The combustion mechanisms, evolved gasses, and ash properties of SP...An unavoidable but reusable waste so as to enhance a more circular waste utilization has been spent potlining(SPL) generated by the aluminum industry.The combustion mechanisms, evolved gasses, and ash properties of SPL were characterized dynamically in response to the elevated temperature and heating rates.Differential scanning calorimetric(DSC) results indicated an exothermic reaction behavior probably able to meet the energy needs of various industrial applications.The reaction mechanisms for the SPL combustion were best described using the 1.5-, 3-and 2.5-order reaction models.Fluoride volatilization rate of the flue gas was estimated at 2.24%.The SPL combustion emitted CO_(2), HNCO, NO, and NO_(2) but SO_(x).The joint optimization of remaining mass, derivative thermogravimetry, and derivative DSC was achieved with the optimal temperature and heating rate combination of 783.5 ℃, and 5 ℃/min, respectively.Interaction between temperature and heating rate exerted the strongest and weakest impact on DSC and remaining mass, respectively.The fluorine mainly as the formation of substantial NaF and CaF_(2) in the residual ash.Besides,the composition and effect of environment of residual solid were evaluated.The ash slagging tendency and its mineral deposition mechanisms were elucidated in terms of turning SPL waste into a benign input to a circular waste utilization.展开更多
It is claimed that the formula used for calculating the tensile strength of a disk-shaped rock specimen in the Brazilian test is not accurate, because the formula is based on the 2-dimensional elastic theory and only ...It is claimed that the formula used for calculating the tensile strength of a disk-shaped rock specimen in the Brazilian test is not accurate, because the formula is based on the 2-dimensional elastic theory and only suitable for very long or very short cylinders. The Matlab software was used to obtain the 2-dimensional distribution of stress in the rock specimen for Brazilian test. Then the 2-dimensional stress distribution in Brazilian disk was analyzed by the Marc FEM software. It can be found that the results obtained by the two software packages can verify each other. Finally, the 3-dimensional elastic stress in the specimen was calculated. The results demonstrate that the distribution of stress on the cross section of the specimen is similar to that in 2-dimension. However, the value of the stress on the cross section varies along the thickness of the specimen and the stress is bigger when getting closer to the end of the specimen. For the specimen with a height-to-diameter ratio of 1 and a Poisson＇s ratio of 0.25, the tensile strength calculated with the classical 2-D formula is 23.3% smaller than the real strength. Therefore, the classical 2-D formula is too conservative.展开更多
In this paper, the synthesis and implementation of three-qubit SWAP gate is discussed. The three-qubit SWAP gate can be decomposed into product of 2 two-qubit SWAP gates, and it can be realized by 6 CNOT gates. Resear...In this paper, the synthesis and implementation of three-qubit SWAP gate is discussed. The three-qubit SWAP gate can be decomposed into product of 2 two-qubit SWAP gates, and it can be realized by 6 CNOT gates. Research illustrated that although the result is very simple, the current methods of matrix decomposition for multi-qubit gate can not get that. Then the implementation of three-qubit SWAP gate in the three spin system with Ising interaction is investigated and the sequence of control pulse and drift process to implement the gate is given. It needs 23 control pulses and 12 drift processes. Since the interaction can not be switched on and off at will, the realization of three-qubit SWAP gate in specific quantum system also can not simply come down to 2 two-qubit SWAP gates.展开更多
When all the involved data in indefinite quadratic programs change simultaneously, we show the locally Lipschtiz continuity of the KKT set of the quadratic programming problem firstly, then we establish the locally Li...When all the involved data in indefinite quadratic programs change simultaneously, we show the locally Lipschtiz continuity of the KKT set of the quadratic programming problem firstly, then we establish the locally Lipschtiz continuity of the KKT solution set. Finally, the similar conclusion for the corresponding optimal value function is obtained.展开更多
In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency.In Body Sensor Network(BSN)systems,biosenso...In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency.In Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency.When the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting power.However,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the system.Therefore,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is considered.This approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing failures.Finally,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.展开更多
To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is proposed.Firstly,large boxes are used to label a large area covering t...To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is proposed.Firstly,large boxes are used to label a large area covering the range of corrosion,provided that the area is visually continuous and adjacent to corrosion that cannot be clearly divided.Secondly,in each labeling box established in the first step,regions with distinct corrosion and relative independence are labeled to form a second layer of nested boxes.Finally,a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of HAM.The experimental results show that,with the help of HAM,the recall of YOLOv5 increases from 50.79%to 59.41%;the recall of Faster R-CNN+VGG16 increases from 66.50%to 78.94%;the recall of Faster R-CNN+Res101 increases from 78.32%to 84.61%.Therefore,HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.展开更多
In MANETs, traffic may follow certain pattern that is not necessarily spatial or temporal but rather to follow special needs as a part of group for collaboration purposes. The source node tends to communicate with a c...In MANETs, traffic may follow certain pattern that is not necessarily spatial or temporal but rather to follow special needs as a part of group for collaboration purposes. The source node tends to communicate with a certain set of nodes more than others regardless of their location exhibiting traffic locality where this set changes over time. We introduce a traffic locality oriented route discovery algorithm with delay, TLRDA-D. It utilises traffic locality by establishing a neighbourhood that includes the most likely destinations for a particular source node. The source node broadcasts the route request according to the original routing used. However, each intermediate node broadcasts the route request with a delay beyond this boundary to give priority for route requests that are travelling within their own source node’s neighbourhood region. This ap-proach improves the end-to-end delay and packet loss, as it generates less contention throughout the network. TLRDA-D is analysed using simulation to study the effect of adding a delay to route request propagation and to decide on the amount of the added delay.展开更多
The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tum...The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.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
With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machi...With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.展开更多
文摘Malaysia,as one of the highest producers of palm oil globally and one of the largest exporters,has a huge potential to use palmoil waste to generate electricity since an abundance of waste is produced during the palmoil extraction process.In this paper,we have first examined and compared the use of palmoil waste as biomass for electricity generation in different countries with reference to Malaysia.Some areas with default accessibility in rural areas,like those in Sabah and Sarawak,require a cheap and reliable source of electricity.Palm oil waste possesses the potential to be the source.Therefore,this research examines the cost-effective comparison between electricity generated frompalm oil waste and standalone diesel electric generation in Marudi,Sarawak,Malaysia.This research aims to investigate the potential electricity generation using palm oil waste and the feasibility of implementing the technology in rural areas.To implement and analyze the feasibility,a case study has been carried out in a rural area in Sarawak,Malaysia.The finding shows the electricity cost calculation of small towns like Long Lama,Long Miri,and Long Atip,with ten nearby schools,and suggests that using EFB from palm oil waste is cheaper and reduces greenhouse gas emissions.The study also points out the need to conduct further research on power systems,such as energy storage andmicrogrids,to better understand the future of power systems.By collecting data through questionnaires and surveys,an analysis has been carried out to determine the approximate cost and quantity of palm oil waste to generate cheaper renewable energy.We concluded that electricity generation from palm oil waste is cost-effective and beneficial.The infrastructure can be a microgrid connected to the main grid.
基金This research has been supported by the National Science Foundation(under grant#1723596)the National Security Agency(under grant#H98230-17-1-0355).
文摘Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.
基金supported by the National Natural Science Foundation of China(Grants Nos.62473146,62072249 and 62072056)the National Science Foundation of Hunan Province(Grant No.2024JJ3017)+1 种基金the Hunan Provincial Key Research and Development Program(Grant No.2022GK2019)by the Researchers Supporting Project Number(RSP2024R509),King Saud University,Riyadh,Saudi Arabia.
文摘In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments.This paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key technologies.Additionally,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use cases.The paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.
基金Supported by Alberta Innovates-Bio Solutionsa graduate studentship from Alberta Innovates-Health Solutions(to Keshteli AH)
文摘AIM To identify demographic, clinical, metabolomic, and lifestyle related predictors of relapse in adult ulcerative colitis(UC) patients.METHODS In this prospective pilot study, UC patients in clinical remission were recruited and followed-up at 12 mo to assess a clinical relapse, or not. At baseline information on demographic and clinical parameters was collected. Serum and urine samples were collected for analysis of metabolomic assays using a combined direct infusion/liquid chromatography tandem mass spectrometry and nuclear magnetic resolution spectroscopy. Stool samples were also collected to measure fecal calprotectin(FCP). Dietary assessment was performed using a validated self-administered food frequency questionnaire. RESULTS Twenty patients were included(mean age: 42.7 ± 14.8 years, females: 55%). Seven patients(35%) experienced a clinical relapse during the follow-up period. While 6 patients(66.7%) with normal body weight developed a clinical relapse, 1 UC patient(9.1%) who was overweight/obese relapsed during the follow-up(P = 0.02). At baseline, poultry intake was significantly higher in patients who were still in remission during follow-up(0.9 oz vs 0.2 oz, P = 0.002). Five patients(71.4%) with FCP > 150 μg/g and 2 patients(15.4%) with normal FCP(≤ 150 μg/g) at baseline relapsed during the follow-up(P = 0.02). Interestingly, baseline urinary and serum metabolomic profiling of UC patients with or without clinical relapse within 12 mo showed a significant difference. The most important metabolites that were responsible for this discrimination were trans-aconitate, cystine and acetamide in urine, and 3-hydroxybutyrate, acetoacetate and acetone in serum. CONCLUSION A combination of baseline dietary intake, fecal calprotectin, and metabolomic factors are associated with risk of UC clinical relapse within 12 mo.
基金funded by National Nature Science Foundation of China Key Projects(81130024,91332205,and 81630030)the National Key Technology R&D Program of the Ministry of Science and Technology of China(2016YFC0904300)+4 种基金the National Natural Science Foundation of China/Research Grants Council of Hong Kong Joint Research Scheme(8141101084)the Natural Science Foundation of China(8157051859)the Sichuan Science&Technology Department(2015JY0173)the Canadian Institutes of Health Research,Alberta Innovates:Centre for Machine Learningthe Canadian Depression Research&Intervention Network
文摘Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder(MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia(FES), 125 with MDD, and 237 demographically-matched healthy controls(HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with aone-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD.Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.
基金Supported by the NNSF of China(60464001) Guangxi Science Foundation(0575092).
文摘In this paper, the generalized Dodd-Bullough-Mikhailov equation is studied. The existence of periodic wave and unbounded wave solutions is proved by using the method of bifurcation theory of dynamical systems. Under different parametric conditions, various sufficient conditions to guarantee the existence of the above solutions are given.Some exact explicit parametric representations of the above travelling solutions are obtained.
基金Project supported partly by the Rockefeller Foundation thesis dis-sertation training grant and the National Hi-Tech Research and De-velopment Program (863) of China
文摘Genetic improvement for drought stress tolerance in rice involves the quantitative nature of the trait, which reflects the additive effects of several genetic loci throughout the genome. Yield components and related traits under stressed and well-water conditions were assayed in mapping populations derived from crosses of Azucena×IR64 and Azucena×Bala. To find the candidate rice genes underlying Quantitative Trait Loci (QTL) in these populations, we conducted in silico analysis of a candidate region flanked by the genetic markers RM212 and RM319 on chromosome 1, proximal to the semi-dwarf (sd1) locus. A total of 175 annotated genes were identified from this region. These included 48 genes annotated by functional homology to known genes, 23 pseudogenes, 24 ab initio predicted genes supported by an alignment match to an EST (Expressed sequence tag) of unknown function, and 80 hypothetical genes predicted solely by ab initio means. Among these, 16 candidate genes could potentially be involved in drought stress response.
基金supported in part by the National Natural Science Foundation of China (No.62002113)the Natural Science Foundation of Hunan Province (No. 2021JJ40122).
文摘Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.
文摘In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence（AI） and machine learning（ML） can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements. For this purpose, a survey was conducted in the target group. The software categories presented include vendor-provided software, robotic software frameworks（RSF）, scientific software and in-house developed software（IHDS）. Typical representatives for each category are described in detail, including Smar Act precision tool commander, Math Works Matlab and national instruments Lab VIEW, as well as the robot operating system（ROS）. The identified software categories and their representatives are rated for end user satisfaction based on functional and non-functional requirements, recommendations and learning curves. The paper concludes with a recommendation of ROS as a basis for future work.
基金supported by the UK EPSRC research grant(No. EP/C525027/1) Nuffield Foundation (No. NAL/00682/G).
文摘Interconnection networks are hardware fabrics supporting communications between individual processors in multi- computers. The low-dimensional k-ary n-cubes （or torus） with adaptive wormhole switching have attracted significant research efforts to construct high-performance interconnection networks in contemporary multi-computers. The arrival process and destination distribution of messages have great effects on network performance. With the aim of capturing the characteristics of the realistic traffic pattern and obtaining a deep understanding of the performance behaviour of interconneetion networks, this paper presents an analytical model to investigate the message latency in adaptive-routed wormhole-switched torus networks where there exists hot-spot nodes and the message arrivals follow a batch arrival process. Each generated message has a given probability to be directed to the hot-spot node. The average degree of virtual channel multiplexing is computed by the GE/G/1/V queueing system with finite buffer capacity. We compare analytical results of message latency with those obtained through the simulation experiments in order to validate the accuracy of the derived model.
基金supported by the National Natural Science Foundation of China(Nos.51978175,22006015)the Social Science and Technology Development Project of Dongguan(No.2019507140211)the Scientific Research Youth Team of Dongguan University of Technology(No.TDQN2019006)。
文摘An unavoidable but reusable waste so as to enhance a more circular waste utilization has been spent potlining(SPL) generated by the aluminum industry.The combustion mechanisms, evolved gasses, and ash properties of SPL were characterized dynamically in response to the elevated temperature and heating rates.Differential scanning calorimetric(DSC) results indicated an exothermic reaction behavior probably able to meet the energy needs of various industrial applications.The reaction mechanisms for the SPL combustion were best described using the 1.5-, 3-and 2.5-order reaction models.Fluoride volatilization rate of the flue gas was estimated at 2.24%.The SPL combustion emitted CO_(2), HNCO, NO, and NO_(2) but SO_(x).The joint optimization of remaining mass, derivative thermogravimetry, and derivative DSC was achieved with the optimal temperature and heating rate combination of 783.5 ℃, and 5 ℃/min, respectively.Interaction between temperature and heating rate exerted the strongest and weakest impact on DSC and remaining mass, respectively.The fluorine mainly as the formation of substantial NaF and CaF_(2) in the residual ash.Besides,the composition and effect of environment of residual solid were evaluated.The ash slagging tendency and its mineral deposition mechanisms were elucidated in terms of turning SPL waste into a benign input to a circular waste utilization.
基金[This work was financially supported by the Fundamental Science Research Fund of Southwest Jiaotong University (No.2004B13).
文摘It is claimed that the formula used for calculating the tensile strength of a disk-shaped rock specimen in the Brazilian test is not accurate, because the formula is based on the 2-dimensional elastic theory and only suitable for very long or very short cylinders. The Matlab software was used to obtain the 2-dimensional distribution of stress in the rock specimen for Brazilian test. Then the 2-dimensional stress distribution in Brazilian disk was analyzed by the Marc FEM software. It can be found that the results obtained by the two software packages can verify each other. Finally, the 3-dimensional elastic stress in the specimen was calculated. The results demonstrate that the distribution of stress on the cross section of the specimen is similar to that in 2-dimension. However, the value of the stress on the cross section varies along the thickness of the specimen and the stress is bigger when getting closer to the end of the specimen. For the specimen with a height-to-diameter ratio of 1 and a Poisson＇s ratio of 0.25, the tensile strength calculated with the classical 2-D formula is 23.3% smaller than the real strength. Therefore, the classical 2-D formula is too conservative.
基金Supported by the Natural Science Foundation of Jiangsu Education Bureau under Grant No.09KJB140010the Project Prepared for National Natural Science Foundation of Xuzhou Normal University under Grant No.08XLY03
文摘In this paper, the synthesis and implementation of three-qubit SWAP gate is discussed. The three-qubit SWAP gate can be decomposed into product of 2 two-qubit SWAP gates, and it can be realized by 6 CNOT gates. Research illustrated that although the result is very simple, the current methods of matrix decomposition for multi-qubit gate can not get that. Then the implementation of three-qubit SWAP gate in the three spin system with Ising interaction is investigated and the sequence of control pulse and drift process to implement the gate is given. It needs 23 control pulses and 12 drift processes. Since the interaction can not be switched on and off at will, the realization of three-qubit SWAP gate in specific quantum system also can not simply come down to 2 two-qubit SWAP gates.
基金Supported by the National Natural Science Foundation of China(10571141,70971109,71371152)supported by the Talents Fund of Xi’an Polytechnic University(BS1320)the Mathematics Discipline Development Fund of Xi’an Ploytechnic University(107090701)
文摘When all the involved data in indefinite quadratic programs change simultaneously, we show the locally Lipschtiz continuity of the KKT set of the quadratic programming problem firstly, then we establish the locally Lipschtiz continuity of the KKT solution set. Finally, the similar conclusion for the corresponding optimal value function is obtained.
基金supported by the National Natural Science Foundation of China(NSFC)(GrantNo.62172058)the Hunan ProvincialNatural Science Foundation of China(Grant Nos.2022JJ10052,2022JJ30624).
文摘In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency.In Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency.When the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting power.However,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the system.Therefore,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is considered.This approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing failures.Finally,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.
基金The National Key R&D Program of China(No.2018YFC0830200)the Open Research Fund from State Key Laboratory of Smart Grid Protection and Control(No.NARI-T-2-2019189)+1 种基金Rapid Support Project(No.61406190120)the Fundamental Research Funds for the Central Universities(No.2242021k10011).
文摘To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is proposed.Firstly,large boxes are used to label a large area covering the range of corrosion,provided that the area is visually continuous and adjacent to corrosion that cannot be clearly divided.Secondly,in each labeling box established in the first step,regions with distinct corrosion and relative independence are labeled to form a second layer of nested boxes.Finally,a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of HAM.The experimental results show that,with the help of HAM,the recall of YOLOv5 increases from 50.79%to 59.41%;the recall of Faster R-CNN+VGG16 increases from 66.50%to 78.94%;the recall of Faster R-CNN+Res101 increases from 78.32%to 84.61%.Therefore,HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.
文摘In MANETs, traffic may follow certain pattern that is not necessarily spatial or temporal but rather to follow special needs as a part of group for collaboration purposes. The source node tends to communicate with a certain set of nodes more than others regardless of their location exhibiting traffic locality where this set changes over time. We introduce a traffic locality oriented route discovery algorithm with delay, TLRDA-D. It utilises traffic locality by establishing a neighbourhood that includes the most likely destinations for a particular source node. The source node broadcasts the route request according to the original routing used. However, each intermediate node broadcasts the route request with a delay beyond this boundary to give priority for route requests that are travelling within their own source node’s neighbourhood region. This ap-proach improves the end-to-end delay and packet loss, as it generates less contention throughout the network. TLRDA-D is analysed using simulation to study the effect of adding a delay to route request propagation and to decide on the amount of the added delay.
基金the Researchers Supporting Project Number(RSP2023R 509)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the Higher Education Sprout Project from the Ministry of Education(MOE)and National Science and Technology Council,Taiwan,(109-2628-E-224-001-MY3)in part by Isuzu Optics Corporation.Dr.Shih-Yu Chen is the corresponding author.
文摘The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.
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supported by the National Natural Science Foundation of China under Grants No.U1836115,No.61922045,No.61877034,No.61772280the Natural Science Foundation of Jiangsu Province under Grant No.BK20181408+2 种基金the Peng Cheng Laboratory Project of Guangdong Province PCL2018KP004the CICAEET fundthe PAPD fund.
文摘With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.
