Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities.In this study,three theoretical models,including linear,polynomial,and...Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities.In this study,three theoretical models,including linear,polynomial,and exponential models,are proposed to inverse the mining-induced stress through the acquisition and analysis of hydraulic support stress and micro-seismicity in the coal mining face.The distribution of mining-induced stress in the coal seam are graphed by fitting two key stress parameters including hydraulic support stress and peak stress,and two key zones including goaf zone and in situ stress zone.These key stress parameters and zones are defined based on the critical nodes of the model curve.According to the geological background of Mataihao coal mine in Erdos,Inner Mongolia Autonomous Region,China,the contours of mining-induced stress are graphed through the stress calculation of these three inversion theoretical models.The multi-monitoring data of micro-seismicity,drilling chips,advanced borehole stress and bolts axial force are used to verify the key stress parameters and zones of the theoretical models.It shows that the monitoring data are in good agreement with the distribution of inversed results.It should be emphasized that,if the fault structure exists around the mining face,the mining-induced stress decreases obviously when the mining face is passing through the faults,and the location of the peak stress will be closer to the mining face.The results in this study could provide methods for early prevention of extreme mining-induced stress and disaster control in the mining activities.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi...Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Micro-seismic phenomena, occurring when rock masses are subjected to forces and failures, allow the determination of their unstable states and failure zones by analyzing micro-seismic signals. We first present the pri...Micro-seismic phenomena, occurring when rock masses are subjected to forces and failures, allow the determination of their unstable states and failure zones by analyzing micro-seismic signals. We first present the principles of micro-seismic monitoring and location, as well as an underground explosion-proof micro-seismic monitoring system. Given a practical engineering application, we describe the application of micro-seismic monitoring technology in determining the height of a "two-zone" overburden, i.e., a caving zone and a fracture zone, the width of a coal-pillar section and the depth of failure of a floor. The workfaces monitored accomplished safe and highly efficient mining based on our micro-seismic monitoring results and provide direct proof of the reliability and validity of micro-seismic monitoring technology.展开更多
The failure depth of the coal seam floor is one important consideration that must be kept in mind when mining is carried out above a confined aquifer.Determining the floor failure depth is the essential precondition f...The failure depth of the coal seam floor is one important consideration that must be kept in mind when mining is carried out above a confined aquifer.Determining the floor failure depth is the essential precondition for predicting the water-resisting ability of the floor.We have used a high-precision microseismic monitoring technique to overcome the limited amount of data available from field measurements. The failure depth of a coal seam floor,especially an inclined coal seam floor,may be more accurately estimated by monitoring the continuous,dynamic failure of the floor.The monitoring results indicate the failure depth of the coal seam floor near the workface conveyance roadway（the lower crossheading） is deeper and that the failure range is wider here compared to the coal seam floor near the return airway（the upper crossheading）.The results of micro-seismic monitoring show that the dangerous area for water-inrush from the coal seam floor may be identified.This provides an important field measurement that helps ensure safe and highly efficient mining of the inclined coal seam above the confined aquifer at the Taoyuan Coal Mine.展开更多
A three-dimensional local-scale P-velocity model down to 25 km depth around the main shock epicenter region was constructed using 83821 event-to-receiver seismic rays from 5856 aftershocks recorded by a newly deployed...A three-dimensional local-scale P-velocity model down to 25 km depth around the main shock epicenter region was constructed using 83821 event-to-receiver seismic rays from 5856 aftershocks recorded by a newly deployed temporary seismic network. Checkerboard tests show that our tomographic model has lateral and vertical resolution of -2 km. The high-resolution P-velocity model revealed interesting structures in the seismogenic layer： （1） The Guanxian-Anxian fault, Yingxiu-Beichuan fault and Wenchuan-Maoxian fault of the Longmen Shan fault zone are well delineated by sharp upper crustal velocity changes; （2） The Pengguan massif has generally higher velocity than its surrounding areas, and may extend down to at least -10 km from the surface; （3） A sharp lateral velocity variation beneath the Wenchuan-Maoxian fault may indicate that the Pengguan massif＇s western boundary and/or the Wenchuan-Maoxian fault is vertical, and the hypocenter of the Wenchuan earthquake possibly located at the conjunction point of the NW dipping Yingxiu-Beichuan and Guanxian-Anxian faults, and vertical Wenchuan-Maoxian fault; （4） Vicinity along the Yingxiu- Beichuan fault is characterized by very low velocity and low seismicity at shallow depths, possibly due to high content of porosity and fractures; （5） Two blocks of low-velocity anomaly are respectively imaged in the hanging wall and foot wall of the Guanxian-Anxian fault with a -7 km offset with -5 km vertical component.展开更多
Blasting and breaking of hard roof are main inducing causes of rock bursts in coal mines with danger of rock burst,and it is important to find out the frequency spectrum distribution laws of these dynamic stress waves...Blasting and breaking of hard roof are main inducing causes of rock bursts in coal mines with danger of rock burst,and it is important to find out the frequency spectrum distribution laws of these dynamic stress waves and rock burst waves for researching the mechanism of rock burst.In this paper,Fourier transform as a micro-seismic signal conversion method of amplitude-time character to amplitude-frequency character is used to analyze the frequency spectrum characters of micro-seismic signal of blasting,hard roof breaking and rock bursts induced by the dynamic disturbance in order to find out the difference and relativity of different signals.The results indicate that blasting and breaking of hard roof are high frequency signals,and the peak values of dominant frequency of the signals are single.However,the results indicate that the rock bursts induced by the dynamic disturbance are low frequency signals,and there are two obvious peak values in the amplitude-frequency curve witch shows that the signals of rock bursts are superposition of low frequency signals and high frequency signals.The research conclusions prove that dynamic disturbance is necessary condition for rock bursts,and the conclusions provide a new way to research the mechanism of rock bursts.展开更多
An improved multidirectional velocity model was proposed for more accurately locating micro-seismic events in rock engineering. It was assumed that the stress wave propagation velocities from a micro-seismic source to...An improved multidirectional velocity model was proposed for more accurately locating micro-seismic events in rock engineering. It was assumed that the stress wave propagation velocities from a micro-seismic source to three nearest monitoring sensors in a sensor's array arrangement were the same. Since the defined objective function does not require pre-measurement of the stress wave propagation velocity in the field, errors from the velocity measurement can be avoided in comparison to three traditional velocity models. By analyzing 24 different cases, the proposed multidirectional velocity model iterated by the Simplex method is found to be the best option no matter the source is within the region of the sensor's array or not. The proposed model and the adopted iterative algorithm are verified by field data and it is concluded that it can significantly reduce the error of the estimated source location.展开更多
A neural network is applied to high-quality 3-D seismic data during micro-seismic facies analysis to perform the waveform analysis and training on single reflection events. Modeled seismic channels are established and...A neural network is applied to high-quality 3-D seismic data during micro-seismic facies analysis to perform the waveform analysis and training on single reflection events. Modeled seismic channels are established and the real seismic channels are classified. Thus, a distribution of micro-seismic facies having a high precision over a fiat surface was acquired. This method applied to existing geological data allows the distribution of areas rich in coal bed methane to be clearly defined. A distribution map of the micro-seismic facies in the research area is shown. The data accord well with measured methane con- tents, indicating that the analysis using micro-seismic facies is reliable and effective. This method could be applied to coal bed methane exploration and is of great importance to future exploration work and to an increase in the drilling success rate.展开更多
In this study, differences of signal characteristics between mine shocks and coal and gas outbursts in coal mines were examined with the micro-seismic monitoring technique and time-frequency analysis. The duration of ...In this study, differences of signal characteristics between mine shocks and coal and gas outbursts in coal mines were examined with the micro-seismic monitoring technique and time-frequency analysis. The duration of the mine shock is short while the coal and gas outburst lasts longer. The outburst consists of three stages： the pre-shock, secondary shock and main shock stage, respectively. The velocity amplitude of the mine shock is between 10 s and 10-3 m/s, which is higher than that of the outburst with the same energy level. In addition, in both cases, the correlation between the velocity amplitude and energy is positive while the correlation between the signal frequency band distribution and energy is negative. The signal frequency band of the high energy mine shock is distributed between 0 and 50 Hz, and the low energy mine shock is between 50 and 100 Hz. The fractal characteristics of mine shocks were studied based on a fractal theory. The box dimensions of high energy mine shocks are lower than the low energy ones, however, the box dimensions of outbursts are higher than that of mine shocks with the same energy level. The higher box dimensions indicate more dangerous dynamic events.展开更多
The Debao MS4.8 earthquake occurred in western Guangxi on August 5,2021,near where the Jingxi MS5.2 earthquake occurred in 2019.To study the increasing seismicity in western Guangxi,it is necessary to determine whethe...The Debao MS4.8 earthquake occurred in western Guangxi on August 5,2021,near where the Jingxi MS5.2 earthquake occurred in 2019.To study the increasing seismicity in western Guangxi,it is necessary to determine whether there was an anomaly related to the earthquake source near the Pingxiang gravity station,which is located approximately 100 km from the epicenter of the Debao MS4.8 earthquake.In this study,the R-value scoring method was used to analyze the anomaly and evaluate the prediction efficiency of the double frequency(DF)micro-seismic signal vertical displacement(referred to as vertical displacement,VD)and the absolute value of monthly extreme rate(referred to as the monthly rate).Results show that earthquakes larger than MS4.0 in the 350 km range from the Pingxiang station tend to coincide with yearly typhoons,and the VD of micro-seismic signals correspondingly changes from low to high.The Debao MS4.8 earthquake occurred during a gradual VD increase from 0.05×10^(-6)to 0.10×10^(-6)m.When discussing the relationships among R,the rate threshold,and the effective duration of prediction,the rate threshold of the micro-seismic signal converges from 0.00039×10^(-6)to 0.00031×10^(-6)m/month,the effective duration of prediction is approximately 6-10 months,and R also converges from 0.29 to 0.31.By comparing the results of three gPhone gravity stations in Guangxi,we found that the increase of short-term VD before the Debao earthquake was related to the enhancement of the DF micro-seismic signal excited by the typhoon.When the typhoon track was perpendicular to the coastline of China,the possibility of an earthquake occurring was increased.This study provides evidence and reference for the future occurrence period of earthquakes above MS4.0 in western Guangxi.展开更多
Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic event...Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine(GRU;VM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine(SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average(STA/LTA) method with GRU;VM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning fr...Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
基金financially supported by the Independent Research fund of Joint National Local Engineering Research Centre for Safe and Precise Coal Mining(Anhui University of Science and Technology)(Grant No.EC2022001)State Key Research Development Program of China(Grant No.2022YFC3004602)the Fundamental Research Funds for the Central Universities(Grant No.2022YJSLJ08).
文摘Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities.In this study,three theoretical models,including linear,polynomial,and exponential models,are proposed to inverse the mining-induced stress through the acquisition and analysis of hydraulic support stress and micro-seismicity in the coal mining face.The distribution of mining-induced stress in the coal seam are graphed by fitting two key stress parameters including hydraulic support stress and peak stress,and two key zones including goaf zone and in situ stress zone.These key stress parameters and zones are defined based on the critical nodes of the model curve.According to the geological background of Mataihao coal mine in Erdos,Inner Mongolia Autonomous Region,China,the contours of mining-induced stress are graphed through the stress calculation of these three inversion theoretical models.The multi-monitoring data of micro-seismicity,drilling chips,advanced borehole stress and bolts axial force are used to verify the key stress parameters and zones of the theoretical models.It shows that the monitoring data are in good agreement with the distribution of inversed results.It should be emphasized that,if the fault structure exists around the mining face,the mining-induced stress decreases obviously when the mining face is passing through the faults,and the location of the peak stress will be closer to the mining face.The results in this study could provide methods for early prevention of extreme mining-induced stress and disaster control in the mining activities.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金funded by University of Transport and Communications(UTC)under grant number T2025-CN-004.
文摘Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金supported by the National Basic Research Program of China (No. 2010CB226805)the National Natural Science Foundation of China (No. 50874103)+1 种基金the State Laboratory Fund (No. SKLGDUEK0905)the Talent Introduction Fund of Anhui University of Science & Technology
文摘Micro-seismic phenomena, occurring when rock masses are subjected to forces and failures, allow the determination of their unstable states and failure zones by analyzing micro-seismic signals. We first present the principles of micro-seismic monitoring and location, as well as an underground explosion-proof micro-seismic monitoring system. Given a practical engineering application, we describe the application of micro-seismic monitoring technology in determining the height of a "two-zone" overburden, i.e., a caving zone and a fracture zone, the width of a coal-pillar section and the depth of failure of a floor. The workfaces monitored accomplished safe and highly efficient mining based on our micro-seismic monitoring results and provide direct proof of the reliability and validity of micro-seismic monitoring technology.
基金supported by the National Basic Research Program ofChina(No.2010CB202210)the National Natural Science Foundation of China(No.50874103)+1 种基金the Natural Science Foundation of Jiangsu Province(No.KB2008135)as well as by the Qinglan Project of Jiangsu Province
文摘The failure depth of the coal seam floor is one important consideration that must be kept in mind when mining is carried out above a confined aquifer.Determining the floor failure depth is the essential precondition for predicting the water-resisting ability of the floor.We have used a high-precision microseismic monitoring technique to overcome the limited amount of data available from field measurements. The failure depth of a coal seam floor,especially an inclined coal seam floor,may be more accurately estimated by monitoring the continuous,dynamic failure of the floor.The monitoring results indicate the failure depth of the coal seam floor near the workface conveyance roadway（the lower crossheading） is deeper and that the failure range is wider here compared to the coal seam floor near the return airway（the upper crossheading）.The results of micro-seismic monitoring show that the dangerous area for water-inrush from the coal seam floor may be identified.This provides an important field measurement that helps ensure safe and highly efficient mining of the inclined coal seam above the confined aquifer at the Taoyuan Coal Mine.
基金the Basic Research Foundation of the Institute of Geomechanics,CAGS(grant DZLXJK200707)Natural Science Foundation of China grant 40674058)
文摘A three-dimensional local-scale P-velocity model down to 25 km depth around the main shock epicenter region was constructed using 83821 event-to-receiver seismic rays from 5856 aftershocks recorded by a newly deployed temporary seismic network. Checkerboard tests show that our tomographic model has lateral and vertical resolution of -2 km. The high-resolution P-velocity model revealed interesting structures in the seismogenic layer： （1） The Guanxian-Anxian fault, Yingxiu-Beichuan fault and Wenchuan-Maoxian fault of the Longmen Shan fault zone are well delineated by sharp upper crustal velocity changes; （2） The Pengguan massif has generally higher velocity than its surrounding areas, and may extend down to at least -10 km from the surface; （3） A sharp lateral velocity variation beneath the Wenchuan-Maoxian fault may indicate that the Pengguan massif＇s western boundary and/or the Wenchuan-Maoxian fault is vertical, and the hypocenter of the Wenchuan earthquake possibly located at the conjunction point of the NW dipping Yingxiu-Beichuan and Guanxian-Anxian faults, and vertical Wenchuan-Maoxian fault; （4） Vicinity along the Yingxiu- Beichuan fault is characterized by very low velocity and low seismicity at shallow depths, possibly due to high content of porosity and fractures; （5） Two blocks of low-velocity anomaly are respectively imaged in the hanging wall and foot wall of the Guanxian-Anxian fault with a -7 km offset with -5 km vertical component.
基金the National Basic Research Program of China (Nos.2005 CB221504 and 2010CB226805)the Research Fund of the State Key Laboratory of Coal Resources and Mine Safety,CUMT (No.09KF08)the Foundation of the Henan Educational Committee (No.2010 A440003)
文摘Blasting and breaking of hard roof are main inducing causes of rock bursts in coal mines with danger of rock burst,and it is important to find out the frequency spectrum distribution laws of these dynamic stress waves and rock burst waves for researching the mechanism of rock burst.In this paper,Fourier transform as a micro-seismic signal conversion method of amplitude-time character to amplitude-frequency character is used to analyze the frequency spectrum characters of micro-seismic signal of blasting,hard roof breaking and rock bursts induced by the dynamic disturbance in order to find out the difference and relativity of different signals.The results indicate that blasting and breaking of hard roof are high frequency signals,and the peak values of dominant frequency of the signals are single.However,the results indicate that the rock bursts induced by the dynamic disturbance are low frequency signals,and there are two obvious peak values in the amplitude-frequency curve witch shows that the signals of rock bursts are superposition of low frequency signals and high frequency signals.The research conclusions prove that dynamic disturbance is necessary condition for rock bursts,and the conclusions provide a new way to research the mechanism of rock bursts.
基金Project(IRT0950)supported by the Cheung Kong Scholars and the Development Plan of Innovative Team,ChinaProject supported by China Scholarship Council
文摘An improved multidirectional velocity model was proposed for more accurately locating micro-seismic events in rock engineering. It was assumed that the stress wave propagation velocities from a micro-seismic source to three nearest monitoring sensors in a sensor's array arrangement were the same. Since the defined objective function does not require pre-measurement of the stress wave propagation velocity in the field, errors from the velocity measurement can be avoided in comparison to three traditional velocity models. By analyzing 24 different cases, the proposed multidirectional velocity model iterated by the Simplex method is found to be the best option no matter the source is within the region of the sensor's array or not. The proposed model and the adopted iterative algorithm are verified by field data and it is concluded that it can significantly reduce the error of the estimated source location.
基金supported financially by the National Key Project(No. 2008ZX05035-005-003)the National Basic Research Program of China (No. 2009CB219603)
文摘A neural network is applied to high-quality 3-D seismic data during micro-seismic facies analysis to perform the waveform analysis and training on single reflection events. Modeled seismic channels are established and the real seismic channels are classified. Thus, a distribution of micro-seismic facies having a high precision over a fiat surface was acquired. This method applied to existing geological data allows the distribution of areas rich in coal bed methane to be clearly defined. A distribution map of the micro-seismic facies in the research area is shown. The data accord well with measured methane con- tents, indicating that the analysis using micro-seismic facies is reliable and effective. This method could be applied to coal bed methane exploration and is of great importance to future exploration work and to an increase in the drilling success rate.
基金the Key Research Development Program of Jiangsu Province (No.BE2015040)the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Graduate Student Research Innovation Project of Jiangsu Province (No.KYLX_1403)
文摘In this study, differences of signal characteristics between mine shocks and coal and gas outbursts in coal mines were examined with the micro-seismic monitoring technique and time-frequency analysis. The duration of the mine shock is short while the coal and gas outburst lasts longer. The outburst consists of three stages： the pre-shock, secondary shock and main shock stage, respectively. The velocity amplitude of the mine shock is between 10 s and 10-3 m/s, which is higher than that of the outburst with the same energy level. In addition, in both cases, the correlation between the velocity amplitude and energy is positive while the correlation between the signal frequency band distribution and energy is negative. The signal frequency band of the high energy mine shock is distributed between 0 and 50 Hz, and the low energy mine shock is between 50 and 100 Hz. The fractal characteristics of mine shocks were studied based on a fractal theory. The box dimensions of high energy mine shocks are lower than the low energy ones, however, the box dimensions of outbursts are higher than that of mine shocks with the same energy level. The higher box dimensions indicate more dangerous dynamic events.
基金supported by grants from the Seismological Science and Technology Spark Program of China Seismological Bureau(grant number XH23026C)the National Natural Science Foundation of China(grant number41204058)。
文摘The Debao MS4.8 earthquake occurred in western Guangxi on August 5,2021,near where the Jingxi MS5.2 earthquake occurred in 2019.To study the increasing seismicity in western Guangxi,it is necessary to determine whether there was an anomaly related to the earthquake source near the Pingxiang gravity station,which is located approximately 100 km from the epicenter of the Debao MS4.8 earthquake.In this study,the R-value scoring method was used to analyze the anomaly and evaluate the prediction efficiency of the double frequency(DF)micro-seismic signal vertical displacement(referred to as vertical displacement,VD)and the absolute value of monthly extreme rate(referred to as the monthly rate).Results show that earthquakes larger than MS4.0 in the 350 km range from the Pingxiang station tend to coincide with yearly typhoons,and the VD of micro-seismic signals correspondingly changes from low to high.The Debao MS4.8 earthquake occurred during a gradual VD increase from 0.05×10^(-6)to 0.10×10^(-6)m.When discussing the relationships among R,the rate threshold,and the effective duration of prediction,the rate threshold of the micro-seismic signal converges from 0.00039×10^(-6)to 0.00031×10^(-6)m/month,the effective duration of prediction is approximately 6-10 months,and R also converges from 0.29 to 0.31.By comparing the results of three gPhone gravity stations in Guangxi,we found that the increase of short-term VD before the Debao earthquake was related to the enhancement of the DF micro-seismic signal excited by the typhoon.When the typhoon track was perpendicular to the coastline of China,the possibility of an earthquake occurring was increased.This study provides evidence and reference for the future occurrence period of earthquakes above MS4.0 in western Guangxi.
基金supported by National Key R&D Program of China(Grant No.2018YFB1501803,2019YFC1804805-4)China Geological Survey Project(Grant No.DD2019135)。
文摘Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine(GRU;VM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine(SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average(STA/LTA) method with GRU;VM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the National Natural Science Foundation of China(32370703)the CAMS Innovation Fund for Medical Sciences(CIFMS)(2022-I2M-1-021,2021-I2M-1-061)the Major Project of Guangzhou National Labora-tory(GZNL2024A01015).
文摘Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
