Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges.In the past 10 years,the pavement infrastructure strongly supports the rapid development of the global so...Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges.In the past 10 years,the pavement infrastructure strongly supports the rapid development of the global social economy.New theories,new methods,new technologies and new materials related to pavement engineering are emerging.Deterioration of pavement infrastructure is a typical multi-physics problem.Because of actual coupled behaviors of traffic and environmental conditions,predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis.In order to summarize the current and determine the future research of pavement engineering,Journal of Traffic and Transportation Engineering(English Edition)has launched a review paper on the topic of"New innovations in pavement materials and engineering:A review on pavement engineering research 2021".Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering,this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world.The content includes asphalt binder performance and modeling,mixture performance and modeling of pavement materials,multi-scale mechanics,green and sustainable pavement,and intelligent pavement.Overall,this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering.展开更多
In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is foun...In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.展开更多
To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions...To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.展开更多
Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt bat...Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.展开更多
Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless ...Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.展开更多
The paper discusses minimizing the effect of external mechanical vibration on hydraulic valves in different military hydraulic drive systems.The current research work presents an analysis of the potential to reduce vi...The paper discusses minimizing the effect of external mechanical vibration on hydraulic valves in different military hydraulic drive systems.The current research work presents an analysis of the potential to reduce vibration on the valve casing by installing a valve flexibly on a vibrating surface,i.e.,by introducing a material with known stiffness and damping characteristics between the valve casing and the vibrating surface-a steel spring package or special cushions made of elastomer material or of oilresistant rubber.The article also demonstrates that elastomer cushions placed inside the valve casingbetween the casing and the centering springs-can be used as a supplementary or alternative solution in the analyzed method for mitigating the transfer of vibrations.By using materials with appropriately selected elastic and dissipative properties,the effectiveness of vibro-isolation can be increased.The presented theoretical analyzes by linear and non-linear mathematical models have been verified experimentally.展开更多
Objective:Tumor-derived exosomes(TDEs)play crucial roles in intercellular communication.Hypoxia in the tumor microenvironment enhances secretion of TDEs and accelerates tumor metastasis.Jiedu recipe(JR),a traditional ...Objective:Tumor-derived exosomes(TDEs)play crucial roles in intercellular communication.Hypoxia in the tumor microenvironment enhances secretion of TDEs and accelerates tumor metastasis.Jiedu recipe(JR),a traditional Chinese medicinal formula,has demonstrated efficacy in preventing the metastasis of hepatocellular carcinoma(HCC).However,the underlying mechanism remains largely unknown.Methods:Animal experiments were performed to investigate the metastasis-preventing effects of JR.Bioinformatics analysis and in vitro assays were conducted to explore the potential targets and active components of JR.TDEs were assessed using nanoparticle tracking analysis(NTA)and Western blotting(WB).Exosomes derived from normoxic or hypoxic HCC cells(H-TDEs)were collected to establish premetastatic mouse models.JR was intragastrically administered to evaluate its metastasis-preventive effects.WB and lysosomal staining were performed to investigate the effects of JR on lysosomal function and autophagy.Bioinformatics analysis,WB,NTA,and immunofluorescence staining were used to identify the active components and potential targets of JR.Results:JR effectively inhibited subcutaneous-tumor-promoted lung premetastatic niche development and tumor metastasis.It inhibited the release of exosomes from tumor cells under hypoxic condition.JR treatment promoted both lysosomal acidification and suppressed secretory autophagy,which were dysregulated in hypoxic tumor cells.Quercetin was identified as the active component in JR,and the epidermal growth factor receptor(EGFR)was identified as a potential target.Quercetin inhibited EGFR phosphorylation and promoted the nuclear translocation of transcription factor EB(TFEB).Hypoxia-impaired lysosomal function was restored,and secretory autophagy was alleviated by quercetin treatment.Conclusion:JR suppressed HCC metastasis by inhibiting hypoxia-stimulated exosome release,restoring lysosomal function,and suppressing secretory autophagy.Quercetin acted as a key component of JR and regulated TDE release thr展开更多
Leukocyte immunoglobulin‐like receptor B4(LILRB4)significantly impacts immune regulation and the pathogenesis and progression of various cancers.This review discusses LILRB4's structural attributes,expression pat...Leukocyte immunoglobulin‐like receptor B4(LILRB4)significantly impacts immune regulation and the pathogenesis and progression of various cancers.This review discusses LILRB4's structural attributes,expression patterns in immune cells,and molecular mechanisms in modulating immune responses.We describe the influence of LILRB4 on T cells,dendritic cells,NK cells,and macrophages,and its dual role in stimulating and suppressing immune activities.The review discusses the current research on LILRB4's involvement in acute myeloid leukemia,chronic lymphocytic leukemia,and solid tumors,such as colorectal cancer,pancreatic cancer,non‐small cell lung cancer,hepatocellular carcinoma,and extramedullary multiple myeloma.The review also describes LILRB4's role in autoimmune disorders,infectious diseases,and other conditions.We evaluate the recent advancements in targeting LILRB4 using monoclonal antibodies and peptide inhibitors and their therapeutic potential in cancer treatment.Together,these studies underscore the need for further research on LILRB4's interactions in the tumor microenvironment and highlight its importance as a therapeutic target in oncology and for future clinical innovations.展开更多
Increase of indoor temperature compared with outdoor temperature is a major concern in modern house design. Occupants suffer from this uncomfortable condition because of overheating indoor temperature. Poor passive de...Increase of indoor temperature compared with outdoor temperature is a major concern in modern house design. Occupants suffer from this uncomfortable condition because of overheating indoor temperature. Poor passive design causes heat to be trapped, which influences the rise in indoor temperature. The upper part, which covers the area of the roof, is the most critical part of the house that is exposed to heat caused by high solar radiation and high emissivity levels. During daytime, the roof accumulates heat, which increases the indoor temperature and affects the comfort level of the occupants. To maintain the indoor temperature within the comfort level, most house designs usually depend on mechanical means by using fans or air conditioning systems. The dependence on a mechanical ventilation system could lead to additional costs for its installation, operation, and maintenance. Thus, this study concentrates on reviews on passive design and suggests recommendations for future developments. New proposals or strategies are proposed to improve the current passive design through ventilated and cool roof systems. It is possible to achieve the comfort level inside a house throughout the day by reducing the transmitted heat into the indoor environment and eliminating the internal hot air. These recommendations could become attractive strategies in providing a comfortable indoor temperature to the occupants as well as in minimizing energy consumption.展开更多
The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very crit...The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.展开更多
This study involved the analysis and characterization of the multiphase flow phenomenon inside the lower stage cyclone separator used in the clinker burning process.The analysis was performed using both CFD and experi...This study involved the analysis and characterization of the multiphase flow phenomenon inside the lower stage cyclone separator used in the clinker burning process.The analysis was performed using both CFD and experimental research methods.Very few studies are devoted to such types of cyclone separators,which in addition to their basic functions are also responsible for the technological process.Due to the atypical working conditions of these cyclone separators,they are characterized with a complex geometry,which significantly differs from that of the traditional separators.Furthermore,the evaluation of the accuracy and level of reliability of the two models of turbulence closure—k-e RNG and RSM(RANS),and the LES.The results obtained led to the conclusion that for the lower stage cyclone separators,the LES model proved to be the most accurate(both in the case of forecasting the separation efficiency and pressure drop).The performance parameter(in particular the separation efficiency)values obtained for the RSM model were also characterized by high accuracy.The k-e RNG model was characterized by significantly larger deviations.展开更多
The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electri...The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.展开更多
A group of competitive people escaping through an exit could lead to the formation of a deadlock, which significantly increases the evacuation time. Such a phenomenon is called the faster-is-slower effect（FIS） and i...A group of competitive people escaping through an exit could lead to the formation of a deadlock, which significantly increases the evacuation time. Such a phenomenon is called the faster-is-slower effect（FIS） and it has been experimentally verified in different systems of particles flowing through an opening. In this paper, the numerical simulation based on discrete element method（DEM） is adopted to study a group of highly competitive people through an exit of varying widths. The FIS effect is observed for a narrow exit whilst it is not observed for the exit wide enough to accommodate two people through it side-by-side. Experimental validation of such a phenomenon with humans is difficult due to ethical issues. The mouse is a kind of self-driven and soft-body creature and it exhibits selfish behaviour under stressed conditions.Particles flowing through an opening in different systems, such as pedestrian flow, animal flow, silo flow, etc. have similar characteristics. Therefore, experimental study is conducted by driving mice to escape through an exit of different widths at varying levels of stimulus. The escape time through a narrow exit（i.e., 2 cm） increases obviously with the increase of stimulus level but it is quite opposite to a wider exit（i.e., 4 cm）. The FIS effect is avoided for an exit wide enough to accommodate two mice passing through it side-by-side. The study illustrates that FIF effect could be effectively prevented for an exit when its width is twice the size of particles.展开更多
This study aims to investigate the nonlinear added mass moment of inertia and damping moment characteristics of largeamplitude ship roll motion based on transient motion data through the nonparametric system identific...This study aims to investigate the nonlinear added mass moment of inertia and damping moment characteristics of largeamplitude ship roll motion based on transient motion data through the nonparametric system identification method.An inverse problem was formulated to solve the first-kind Volterra-type integral equation using sets of motion signal data.However,this numerical approach leads to solution instability due to noisy data.Regularization is a technique that can overcome the lack of stability;hence,Landweber’s regularization method was employed in this study.The L-curve criterion was used to select regularization parameters(number of iterations)that correspond to the accuracy of the inverse solution.The solution of this method is a discrete moment,which is the summation of nonlinear restoring,nonlinear damping,and nonlinear mass moment of inertia.A zero-crossing detection technique is used in the nonparametric system identification method on a pair of measured data of the angular velocity and angular acceleration of a ship,and the detections are matched with the inverse solution at the same discrete times.The procedure was demonstrated through a numerical model of a full nonlinear free-roll motion system in still water to examine and prove its accuracy.Results show that the method effectively and efficiently identified the functional form of the nonlinear added moment of inertia and damping moment.展开更多
Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directl...Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directly in these big data streams.At the same time,streaming data from several applications results in two major problems such as class imbalance and concept drift.The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming Data.The presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and classification.MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is employed.Besides,Statistical Test of Equal Proportions(STEPD),a CDD technique is also utilized.Finally,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for classification.In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried out.The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam dataset.An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model.The proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.展开更多
A gyro-stabilizer is the interesting system that it can apply to marine vessels for diminishes roll motion.Today it has potentially light weight with no hydrodynamics drag and effective at zero forward speed.The...A gyro-stabilizer is the interesting system that it can apply to marine vessels for diminishes roll motion.Today it has potentially light weight with no hydrodynamics drag and effective at zero forward speed.The twin-gyroscope was chosen.Almost,the modelling for designing the system use linear model that it might not comprehensive mission requirement such as high sea condition.The non-linearity analysis was proved by comparison the results between linear and non-linear model of gyro-stabilizer throughout frequency domain also same wave input,constrains and limitations.Moreover,they were cross checked by simulating in time domain.The comparison of interested of linear and non-linear close loop model in frequency domain has demonstrated the similar characteristics but gave different values at same frequency obviously.The results were confirmed again by simulation in irregular beam sea on time domain and they demonstrate the difference of behavior of both systems while the gyro-stabilizers are switching on and off.From the resulting analysis,the non-linear gyro-stabilizer model gives more real results that correspond to more accuracy in a designing gyro-stabilizer control system for various amplitudes and frequencies operating condition especially high sea condition.展开更多
Background: Promotion of Evidence-Based Practice (EBP) in nursing appears to be developing slowly. Research indicates that nurses’ beliefs in EBP may play an even more significant role than knowledge and resources in...Background: Promotion of Evidence-Based Practice (EBP) in nursing appears to be developing slowly. Research indicates that nurses’ beliefs in EBP may play an even more significant role than knowledge and resources in making implementation feasible. To address this issue, measurement of nurses’ beliefs regarding EBP is paramount. Aims and objectives: This study explores the internal consistency reliability and the construct factor structure of the Norwegian version of the original Evidence-Based Practice Beliefs Scale (EBP-BS). Methods: The study has a Non-experimental exploratory survey design. A Norwegian translation of the EBP-BS was tested in a convenience sample of 118 healthcare professionals (95% nurses) attending a continuing education program at a University College in Norway. The response rate was 95% (n = 112). The internal consistency of the scale was measured by Cronbach’s alpha, and an explorative Principal Component Analysis (PCA) was used to explore the construct structure. Results: The overall internal consistency of the EBP-BS was acceptable. The PCA indicated a four-factor structure. The psychometric properties of two of the factors were too weak for expanding to a four-factor model. Based on our investigation of the EBP-BS, we suggest a two-factor structure model. The factors were named 1) General knowledge and confidence concerning EBP and 2) Task specific beliefs in EBP. This finding differs from previous results that indicated a unidimensional structure. Conclusion: As a starting point, reliable and valid measurement of nurses’ beliefs about EBP is required in order to identify possible obstacles and to optimize implementation in the individual clinical setting. Our results indicate that the EBP-BS has a two-factor structure. Further exploration of the factor structure is needed. Further empirical research may contribute to the resolving of controversies concerning basic understandings of the concept of EBP.展开更多
The objective of traffic accident reconstruction is to recreate the event, which is necessary for analyzing the collision dynamics that is used as evidence in court cases. Traffic accident reconstruction and a demonst...The objective of traffic accident reconstruction is to recreate the event, which is necessary for analyzing the collision dynamics that is used as evidence in court cases. Traffic accident reconstruction and a demonstration of the event require precise data pertaining to scene measurement. However, there are differences between the individual measuring tools and methods related to traffic accident investigation, just as there are differences between the extent of their use and measurement accuracy. The most commonly applied method is the measuring tape, followed by measurements with total stations and laser rangefinders, while photogrammetry is also becoming increasingly important. The advantages and disadvantages of individual tools and methods affect the required number of investigators, portability, measurement range, applicability depending on the amount of light and weather conditions, on the possibility of remote measurement, on data collection time, on the scope, on the option to later process, the collected data and above all on the accuracy of all gathered data. The latter is crucial for proving the guilt or innocence of traffic accident participants at court, as inaccurate data can lead to an unjust sentence. Measurement accuracy using the above mentioned tools and methods also varies depending on which ones are used, as well as on other factors.展开更多
In recent years, it has been difficult for manufactures and suppliers to forecast demand from a market for a given product precisely. Therefore, it has become important for them to cope with fluctuations in demand. Fr...In recent years, it has been difficult for manufactures and suppliers to forecast demand from a market for a given product precisely. Therefore, it has become important for them to cope with fluctuations in demand. From this viewpoint, the problem of planning or scheduling in production systems can be regarded as a mathematical problem with stochastic elements. However, in many previous studies, such problems are formulated without stochastic factors, treating stochastic elements as deterministic variables or parameters. Stochastic programming incorporates such factors into the mathematical formulation. In the present paper, we consider a multi-product, discrete, lotsizing and scheduling problem on parallel machines with stochastic demands. Under certain assumptions, this problem can be formulated as a stochastic integer programming problem. We attempt to solve this problem by a scenario aggregation method proposed by Rockafellar and Wets. The results from computational experiments suggest that our approach is able to solve large-scale problems, and that, under the condition of uncertainty, incorporating stochastic elements into the model gives better results than formulating the problem as a deterministic model.展开更多
基金National Key R&D Program of China(No.2018YFB1600200,2021YFB1600200)National Natural Science Foundation of China(No.51608457,51778038,51808016,51808403,51908057,51908072,51908165,51908331,52008029,52008069,52078018,52078025,52078049,52078209,52108403,52122809,52178417)+9 种基金Marie Sk?odowska-Curie Individual Fellowships of the European Commission’s Horizon 2020 programme(No.101024139)Natural Science Foundation of Heilongjiang Province(No.JJ2020ZD0015)China Postdoctoral Science Foundation funded project(No.BX20180088)Research Capability Enhancement Program for Young Professors of Beijing University of Civil Engineering and Architecture(No.02080921021)Young Scholars of Beijing Talent Program(No.02082721009)Beijing Municipal Natural Science Foundation and Beijing Municipal Education Commission(No.KZ201910016017)German Research Foundation(No.OE 514/15-1(459436571))Fundamental Research Funds for the Central Universities(No.2020kfyXJJS127)Marie Sk?odowska-Curie Individual Fellowships of the European Commission’s Horizon 2020 Programme(No.101030767)Research Fund for High Level Talent Program(No.22120210108)。
文摘Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges.In the past 10 years,the pavement infrastructure strongly supports the rapid development of the global social economy.New theories,new methods,new technologies and new materials related to pavement engineering are emerging.Deterioration of pavement infrastructure is a typical multi-physics problem.Because of actual coupled behaviors of traffic and environmental conditions,predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis.In order to summarize the current and determine the future research of pavement engineering,Journal of Traffic and Transportation Engineering(English Edition)has launched a review paper on the topic of"New innovations in pavement materials and engineering:A review on pavement engineering research 2021".Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering,this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world.The content includes asphalt binder performance and modeling,mixture performance and modeling of pavement materials,multi-scale mechanics,green and sustainable pavement,and intelligent pavement.Overall,this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering.
文摘In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.
基金funding from the EU Smarter project(PEOPLE-2013-IAPP-610675)
文摘To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.
文摘Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.
文摘Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.
文摘The paper discusses minimizing the effect of external mechanical vibration on hydraulic valves in different military hydraulic drive systems.The current research work presents an analysis of the potential to reduce vibration on the valve casing by installing a valve flexibly on a vibrating surface,i.e.,by introducing a material with known stiffness and damping characteristics between the valve casing and the vibrating surface-a steel spring package or special cushions made of elastomer material or of oilresistant rubber.The article also demonstrates that elastomer cushions placed inside the valve casingbetween the casing and the centering springs-can be used as a supplementary or alternative solution in the analyzed method for mitigating the transfer of vibrations.By using materials with appropriately selected elastic and dissipative properties,the effectiveness of vibro-isolation can be increased.The presented theoretical analyzes by linear and non-linear mathematical models have been verified experimentally.
基金supported by the grants from National Natural Science Foundation of China(No.82030117,82074203,82170033,and 82374540)Special Fund for Research on Community Medicine and Health Management in Shanghai(No.2023SQ01)+2 种基金Medical Research Project of Health Commission of Shanghai Hongkou District(No.HW2302-43)Special Medical Basic Research Project of the First Affiliated Hospital of Naval Medical University(No.2021JCMS12)Wild Goose Array Project of Zhengzhou Center of Chinese People’s Liberation Army Joint Logistic Support Force。
文摘Objective:Tumor-derived exosomes(TDEs)play crucial roles in intercellular communication.Hypoxia in the tumor microenvironment enhances secretion of TDEs and accelerates tumor metastasis.Jiedu recipe(JR),a traditional Chinese medicinal formula,has demonstrated efficacy in preventing the metastasis of hepatocellular carcinoma(HCC).However,the underlying mechanism remains largely unknown.Methods:Animal experiments were performed to investigate the metastasis-preventing effects of JR.Bioinformatics analysis and in vitro assays were conducted to explore the potential targets and active components of JR.TDEs were assessed using nanoparticle tracking analysis(NTA)and Western blotting(WB).Exosomes derived from normoxic or hypoxic HCC cells(H-TDEs)were collected to establish premetastatic mouse models.JR was intragastrically administered to evaluate its metastasis-preventive effects.WB and lysosomal staining were performed to investigate the effects of JR on lysosomal function and autophagy.Bioinformatics analysis,WB,NTA,and immunofluorescence staining were used to identify the active components and potential targets of JR.Results:JR effectively inhibited subcutaneous-tumor-promoted lung premetastatic niche development and tumor metastasis.It inhibited the release of exosomes from tumor cells under hypoxic condition.JR treatment promoted both lysosomal acidification and suppressed secretory autophagy,which were dysregulated in hypoxic tumor cells.Quercetin was identified as the active component in JR,and the epidermal growth factor receptor(EGFR)was identified as a potential target.Quercetin inhibited EGFR phosphorylation and promoted the nuclear translocation of transcription factor EB(TFEB).Hypoxia-impaired lysosomal function was restored,and secretory autophagy was alleviated by quercetin treatment.Conclusion:JR suppressed HCC metastasis by inhibiting hypoxia-stimulated exosome release,restoring lysosomal function,and suppressing secretory autophagy.Quercetin acted as a key component of JR and regulated TDE release thr
文摘Leukocyte immunoglobulin‐like receptor B4(LILRB4)significantly impacts immune regulation and the pathogenesis and progression of various cancers.This review discusses LILRB4's structural attributes,expression patterns in immune cells,and molecular mechanisms in modulating immune responses.We describe the influence of LILRB4 on T cells,dendritic cells,NK cells,and macrophages,and its dual role in stimulating and suppressing immune activities.The review discusses the current research on LILRB4's involvement in acute myeloid leukemia,chronic lymphocytic leukemia,and solid tumors,such as colorectal cancer,pancreatic cancer,non‐small cell lung cancer,hepatocellular carcinoma,and extramedullary multiple myeloma.The review also describes LILRB4's role in autoimmune disorders,infectious diseases,and other conditions.We evaluate the recent advancements in targeting LILRB4 using monoclonal antibodies and peptide inhibitors and their therapeutic potential in cancer treatment.Together,these studies underscore the need for further research on LILRB4's interactions in the tumor microenvironment and highlight its importance as a therapeutic target in oncology and for future clinical innovations.
文摘Increase of indoor temperature compared with outdoor temperature is a major concern in modern house design. Occupants suffer from this uncomfortable condition because of overheating indoor temperature. Poor passive design causes heat to be trapped, which influences the rise in indoor temperature. The upper part, which covers the area of the roof, is the most critical part of the house that is exposed to heat caused by high solar radiation and high emissivity levels. During daytime, the roof accumulates heat, which increases the indoor temperature and affects the comfort level of the occupants. To maintain the indoor temperature within the comfort level, most house designs usually depend on mechanical means by using fans or air conditioning systems. The dependence on a mechanical ventilation system could lead to additional costs for its installation, operation, and maintenance. Thus, this study concentrates on reviews on passive design and suggests recommendations for future developments. New proposals or strategies are proposed to improve the current passive design through ventilated and cool roof systems. It is possible to achieve the comfort level inside a house throughout the day by reducing the transmitted heat into the indoor environment and eliminating the internal hot air. These recommendations could become attractive strategies in providing a comfortable indoor temperature to the occupants as well as in minimizing energy consumption.
基金National Nature Science Foundation of China,Grant/Award Number:U1813201the Key Scientific Research Projects of Henan Province,Grant/Award Number:22A413011+2 种基金the Training Program for Young Teachers in Universities of Henan Province,Grant/Award Number:2020GGJS137Henan Province Science and Technology R&D projects,Grant/Award Number:202102210135,212102310547 and 212102210080High‐end foreign expert program of Ministry of Science and Technology,Grant/Award Number:G2021026006L。
文摘The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.
基金the support of the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) University of Warsaw under grant no G71-5
文摘This study involved the analysis and characterization of the multiphase flow phenomenon inside the lower stage cyclone separator used in the clinker burning process.The analysis was performed using both CFD and experimental research methods.Very few studies are devoted to such types of cyclone separators,which in addition to their basic functions are also responsible for the technological process.Due to the atypical working conditions of these cyclone separators,they are characterized with a complex geometry,which significantly differs from that of the traditional separators.Furthermore,the evaluation of the accuracy and level of reliability of the two models of turbulence closure—k-e RNG and RSM(RANS),and the LES.The results obtained led to the conclusion that for the lower stage cyclone separators,the LES model proved to be the most accurate(both in the case of forecasting the separation efficiency and pressure drop).The performance parameter(in particular the separation efficiency)values obtained for the RSM model were also characterized by high accuracy.The k-e RNG model was characterized by significantly larger deviations.
文摘The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.
基金supported by the National Natural Science Foundation of China(Grant Nos.51578464 and 71473207)China Fundamental Research Funds for Central Universities(Grant No.2682016cx082)
文摘A group of competitive people escaping through an exit could lead to the formation of a deadlock, which significantly increases the evacuation time. Such a phenomenon is called the faster-is-slower effect（FIS） and it has been experimentally verified in different systems of particles flowing through an opening. In this paper, the numerical simulation based on discrete element method（DEM） is adopted to study a group of highly competitive people through an exit of varying widths. The FIS effect is observed for a narrow exit whilst it is not observed for the exit wide enough to accommodate two people through it side-by-side. Experimental validation of such a phenomenon with humans is difficult due to ethical issues. The mouse is a kind of self-driven and soft-body creature and it exhibits selfish behaviour under stressed conditions.Particles flowing through an opening in different systems, such as pedestrian flow, animal flow, silo flow, etc. have similar characteristics. Therefore, experimental study is conducted by driving mice to escape through an exit of different widths at varying levels of stimulus. The escape time through a narrow exit（i.e., 2 cm） increases obviously with the increase of stimulus level but it is quite opposite to a wider exit（i.e., 4 cm）. The FIS effect is avoided for an exit wide enough to accommodate two mice passing through it side-by-side. The study illustrates that FIF effect could be effectively prevented for an exit when its width is twice the size of particles.
文摘This study aims to investigate the nonlinear added mass moment of inertia and damping moment characteristics of largeamplitude ship roll motion based on transient motion data through the nonparametric system identification method.An inverse problem was formulated to solve the first-kind Volterra-type integral equation using sets of motion signal data.However,this numerical approach leads to solution instability due to noisy data.Regularization is a technique that can overcome the lack of stability;hence,Landweber’s regularization method was employed in this study.The L-curve criterion was used to select regularization parameters(number of iterations)that correspond to the accuracy of the inverse solution.The solution of this method is a discrete moment,which is the summation of nonlinear restoring,nonlinear damping,and nonlinear mass moment of inertia.A zero-crossing detection technique is used in the nonparametric system identification method on a pair of measured data of the angular velocity and angular acceleration of a ship,and the detections are matched with the inverse solution at the same discrete times.The procedure was demonstrated through a numerical model of a full nonlinear free-roll motion system in still water to examine and prove its accuracy.Results show that the method effectively and efficiently identified the functional form of the nonlinear added moment of inertia and damping moment.
文摘Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directly in these big data streams.At the same time,streaming data from several applications results in two major problems such as class imbalance and concept drift.The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming Data.The presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and classification.MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is employed.Besides,Statistical Test of Equal Proportions(STEPD),a CDD technique is also utilized.Finally,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for classification.In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried out.The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam dataset.An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model.The proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.
文摘A gyro-stabilizer is the interesting system that it can apply to marine vessels for diminishes roll motion.Today it has potentially light weight with no hydrodynamics drag and effective at zero forward speed.The twin-gyroscope was chosen.Almost,the modelling for designing the system use linear model that it might not comprehensive mission requirement such as high sea condition.The non-linearity analysis was proved by comparison the results between linear and non-linear model of gyro-stabilizer throughout frequency domain also same wave input,constrains and limitations.Moreover,they were cross checked by simulating in time domain.The comparison of interested of linear and non-linear close loop model in frequency domain has demonstrated the similar characteristics but gave different values at same frequency obviously.The results were confirmed again by simulation in irregular beam sea on time domain and they demonstrate the difference of behavior of both systems while the gyro-stabilizers are switching on and off.From the resulting analysis,the non-linear gyro-stabilizer model gives more real results that correspond to more accuracy in a designing gyro-stabilizer control system for various amplitudes and frequencies operating condition especially high sea condition.
文摘Background: Promotion of Evidence-Based Practice (EBP) in nursing appears to be developing slowly. Research indicates that nurses’ beliefs in EBP may play an even more significant role than knowledge and resources in making implementation feasible. To address this issue, measurement of nurses’ beliefs regarding EBP is paramount. Aims and objectives: This study explores the internal consistency reliability and the construct factor structure of the Norwegian version of the original Evidence-Based Practice Beliefs Scale (EBP-BS). Methods: The study has a Non-experimental exploratory survey design. A Norwegian translation of the EBP-BS was tested in a convenience sample of 118 healthcare professionals (95% nurses) attending a continuing education program at a University College in Norway. The response rate was 95% (n = 112). The internal consistency of the scale was measured by Cronbach’s alpha, and an explorative Principal Component Analysis (PCA) was used to explore the construct structure. Results: The overall internal consistency of the EBP-BS was acceptable. The PCA indicated a four-factor structure. The psychometric properties of two of the factors were too weak for expanding to a four-factor model. Based on our investigation of the EBP-BS, we suggest a two-factor structure model. The factors were named 1) General knowledge and confidence concerning EBP and 2) Task specific beliefs in EBP. This finding differs from previous results that indicated a unidimensional structure. Conclusion: As a starting point, reliable and valid measurement of nurses’ beliefs about EBP is required in order to identify possible obstacles and to optimize implementation in the individual clinical setting. Our results indicate that the EBP-BS has a two-factor structure. Further exploration of the factor structure is needed. Further empirical research may contribute to the resolving of controversies concerning basic understandings of the concept of EBP.
文摘The objective of traffic accident reconstruction is to recreate the event, which is necessary for analyzing the collision dynamics that is used as evidence in court cases. Traffic accident reconstruction and a demonstration of the event require precise data pertaining to scene measurement. However, there are differences between the individual measuring tools and methods related to traffic accident investigation, just as there are differences between the extent of their use and measurement accuracy. The most commonly applied method is the measuring tape, followed by measurements with total stations and laser rangefinders, while photogrammetry is also becoming increasingly important. The advantages and disadvantages of individual tools and methods affect the required number of investigators, portability, measurement range, applicability depending on the amount of light and weather conditions, on the possibility of remote measurement, on data collection time, on the scope, on the option to later process, the collected data and above all on the accuracy of all gathered data. The latter is crucial for proving the guilt or innocence of traffic accident participants at court, as inaccurate data can lead to an unjust sentence. Measurement accuracy using the above mentioned tools and methods also varies depending on which ones are used, as well as on other factors.
文摘In recent years, it has been difficult for manufactures and suppliers to forecast demand from a market for a given product precisely. Therefore, it has become important for them to cope with fluctuations in demand. From this viewpoint, the problem of planning or scheduling in production systems can be regarded as a mathematical problem with stochastic elements. However, in many previous studies, such problems are formulated without stochastic factors, treating stochastic elements as deterministic variables or parameters. Stochastic programming incorporates such factors into the mathematical formulation. In the present paper, we consider a multi-product, discrete, lotsizing and scheduling problem on parallel machines with stochastic demands. Under certain assumptions, this problem can be formulated as a stochastic integer programming problem. We attempt to solve this problem by a scenario aggregation method proposed by Rockafellar and Wets. The results from computational experiments suggest that our approach is able to solve large-scale problems, and that, under the condition of uncertainty, incorporating stochastic elements into the model gives better results than formulating the problem as a deterministic model.
