Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
A deep-sea mining riser is a crucial component of the system used to lift seafloor mineral resources to the vessel.It is prone to damage and failure because of harsh environmental conditions and internal fluid erosion...A deep-sea mining riser is a crucial component of the system used to lift seafloor mineral resources to the vessel.It is prone to damage and failure because of harsh environmental conditions and internal fluid erosion.Furthermore,damage can impact the response characteristics of the riser,but varying environmental loadings easily mask it.Thus,distin-guishing between riser damage and environmental effects poses a considerable challenge.To address this issue,a cantilevered model is created for a deep-sea mining riser via the concentrated mass method,and a time-domain analytical strategy is developed.The vortex-induced vibration(VIV)response characteristics of the riser are initially examined,considering various damage conditions and flow velocities.The study results revealed four primary observations:(a)effective tension can serve as a reliable indicator for identifying damage at lower velocities;(b)there are noticeable differences in displacement between the healthy and damaged risers in the in-line direction rather than the cross-flow direction;(c)frequency characteristics can more effectively distinguish the damage conditions at high flow velocities,with the mean square frequency and frequency variance being more effective than the centroid frequency and root variance frequency;(d)displacement differences are more sensitive to damage occurring near the top and bottom of the riser,while both velocity variations and structural damage can influence displacements,especially in regions between modal nodes.The vibrational behavior and damage indicators are clarified for structural health monitoring of deep-sea mining risers during lifting operations.展开更多
1.Introduction Changes in land use are key factors promoting global climate change,and the side effects of mining activity that destroy the soil,vegetation,and biodiversity lead to imbalanced carbon cycling in terrest...1.Introduction Changes in land use are key factors promoting global climate change,and the side effects of mining activity that destroy the soil,vegetation,and biodiversity lead to imbalanced carbon cycling in terrestrial ecosystems.展开更多
Copper smelting is the main source of arsenic pollution in the environment,and China is the largest country for copper smelting.Taking 2022 as an example,this study analyzes the distribution and fate of arsenic across...Copper smelting is the main source of arsenic pollution in the environment,and China is the largest country for copper smelting.Taking 2022 as an example,this study analyzes the distribution and fate of arsenic across the copper mining,beneficiation,and smelting processes using a life-cycle approach,providing important insights for arsenic pollution prevention and the resource utilization of arsenic-bearing solid waste.The results show that the amount of As in waste rock,tailing and concentrate are 53483 t,86632 t,76162 t,respectively.After smelting treatment,the amount of arsenic in different types of solid waste,wastewater,waste gas and products are 76128 t,1 t,31 t and 2 t,respectively,and the proportion in arsenic sulfide slag is the highest(55%).The amount of emission to the environment is 32 t,accounting for only 0.04%of total amount.In the future,key considerations are to improve the resource utilization rate of arsenic-containing solid waste(tailing,smelting slag),especially arsenic sulfide slag,and to digest its environmental risk.展开更多
Aseptic osteonecrosis of the femoral head is defined as the death of bone cells in the femoral epiphysis due to an interruption of blood supply. Most cases are linked to trauma, but non-traumatic cases also occur and ...Aseptic osteonecrosis of the femoral head is defined as the death of bone cells in the femoral epiphysis due to an interruption of blood supply. Most cases are linked to trauma, but non-traumatic cases also occur and can be associated with several known risk factors. This study aims to describe these risk factors identified in the former Katanga province, a region with significant mining activity. Method and Patients: This is a descriptive cross-sectional study conducted over a seven-year period (2017-2024), including all cases of aseptic osteonecrosis of the femoral head diagnosed in the orthopedic department of Medpark Clinic in Lubumbashi. The investigation of risk factors was based on the analysis of sociodemographic, clinical, radiological, and biological data. Results: Our study included a total of 110 patients with a mean age of 47.5 years. Among them, there were 46 women (41.82%) and 64 men (58.18%). Twenty-five patients (27.5%) reported a family history of osteonecrosis, and 24% were diagnosed with sickle cell disease. Chronic alcoholism was noted in 14 patients (12.73%), while diabetes was present in 8 (7.2%). Four patients (3.64%) were obese, and three were HIV-positive (2.72%). The use of nonsteroidal anti-inflammatory drugs (NSAIDs) was common, and prolonged corticosteroid use was documented in 5 patients (4.5%). Abnormally high cholesterol levels were found in 26 patients (23.6%). One patient had gout, and two suffered from acute rheumatic fever (1.8%). Regarding inflammatory markers, C-reactive protein levels and erythrocyte sedimentation rates were within normal limits for almost all patients. Electrolyte levels and phosphocalcic profiles showed no abnormalities. Furthermore, 33 patients (30%) did not exhibit any of the previously mentioned risk factors. Most of these patients lived in the regions of Kolwezi, Likasi, and Lubumbashi. Among this group, 25 patients reported performing physically demanding labor, particularly in mining operations. Conclusion: Our study highlighted well-known risk factors for osteonecrosis of the femoral head (ONFH). However, it also identified a significant number of cases without any identifiable risk factors, classified as idiopathic. Among these cases, some patients engaged in intense physical labor, often linked to mining exposure.展开更多
Rising demand for minerals and metals in high-tech and new energy industries has led to a great interest in exploration of seabed mineral resources.Such resources,including polymetallic nodule(PMN),polymetallic sulphi...Rising demand for minerals and metals in high-tech and new energy industries has led to a great interest in exploration of seabed mineral resources.Such resources,including polymetallic nodule(PMN),polymetallic sulphide(PMS),and cobalt-rich ferromanganese crust(CFC),are considered as an alternative source of metals to terrestrial deposits.Although a considerable number of sea trials of deep-sea mining have been carried out,the deep-sea mining does not achieve the commercial exploitation due to the complexity of deep-sea mining system and deep-sea mining environment.In fact,to achieve commercial deep-sea mining,the technology and equipment of deep-sea mining are the key points.Therefore,the present study presents the development of the technology and equipment of deep-sea mining.It commences with a requirement of technology and equipment for deep-sea mining,including environmental impact,reliability,energy cost,efficiency,etc.Then,a historical perspective and present-day effort related to deep-sea mining vehicles are given,which highlights the evolution of collection mechanism and walking mode of deep-sea mining vehicle.Subsequently,the present study discusses the operation of subsea lifting system and surface support system,shedding light on the crucial equipment and processes.The challenges and prospects in the deep-sea mining are presented in final,including environmental protection,self-propelled crawler,hydraulic pipeline lifting,and intelligent equipment,etc.展开更多
Shear strain energy is a pivotal physical quantity in the occurrence of earthquakes and rockbursts during deep mining operations.This research is focused on understanding the changes in shear strain energy in the cont...Shear strain energy is a pivotal physical quantity in the occurrence of earthquakes and rockbursts during deep mining operations.This research is focused on understanding the changes in shear strain energy in the context of retreating longwall mining,which is essential for the optimized design and mitigation of rockbursts and seismic events.Through the application of innovative analytical models,this study expands its analytical range to include the variations in shear strain energy caused by fault coseismic slip.An integrated methodology is utilized,taking into account the changes in coseismic and fault friction parameters as well as enhancements in mining-induced stress and existing background stresses.Our numerical investigation highlights the significance of mining location and fault characteristics as key determinants of shear strain energy modifications.The analysis demonstrates significant spatial variability in shear strain energy,especially noting that fault slip near the mining face greatly increases the likelihood of rockburst.This finding emphasizes the need to integrate fault coseismic slip dynamics into the triggering factors of rock(coal)bursts,thus broadening the theoretical foundation for addressing geological hazards in deep mining operations.The results are further corroborated by observational data from the vicinity of the F16 fault zone,introducing the concept of mining-induced fault coseismic slip as an essential element in the theoretical framework for understanding rockburst triggers.展开更多
An application programming interface (API) usage specifcation, which includes the conditions, calling sequences, and semantic relationships of the API, is important for verifying its correct usage, which is in turn cr...An application programming interface (API) usage specifcation, which includes the conditions, calling sequences, and semantic relationships of the API, is important for verifying its correct usage, which is in turn critical for ensur-ingthe security and availability of the target program. However, existing techniques either mine the co-occurring relationships of multiple APIs without considering their semantic relationships, or they use data fow and control fow information to extract semantic beliefs on API pairs but difcult to incorporate when mining specifcations for mul-tipleAPIs. Hence, we propose an API specifcation mining approach that efciently extracts a relatively complete list of the API combinations and semantic relationships between APIs. This approach analyzes a target program in two stages. The frst stage uses frequent API set mining based on frequent common API identifcation and fltra-tionto extract the maximal set of frequent context-sensitive API sequences. In the second stage, the API relationship graph is constructed using three semantic relationships extracted from the symbolic path information, and the speci-fcationscontaining semantic relationships for multiple APIs are mined. The experimental results on six popular open-source code bases of diferent scales show that the proposed two-stage approach not only yields better results than existing typical approaches, but also can efectively discover the specifcations along with the semantic rela-tionshipsfor multiple APIs. Instance analysis shows that the analysis of security-related API call violations can assist in the cause analysis and patch of software vulnerabilities.展开更多
Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineerin...Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineering challenges.This study establishes a multiscale modelling framework coupling the discrete element method(DEM)with multi-body dynamics(MBD)to investigate track-seabed dynamic interactions across three operational modes:flat terrain,slope climbing,and ditch surmounting.The simulation framework,validated against laboratory experiments,systematically evaluates the influence of grouser geometry(involute,triangular,and pin-type)and traveling speed(0.2–1.0 m/s)on traction performance,slip rate,and ground pressure distribution.Results reveal rate-dependent traction mechanisms governed by soil microstructural responses:higher speeds enhance peak traction but exacerbate slip instability on complex terrain.Critical operational thresholds are established—0.7 m/s for flat terrain,≤0.5 m/s for slopes and ditches—with distinct grouser optimization strategies:involute grousers achieve 35%–40%slip reduction on slopes through progressive soil engagement,while triangular grousers provide optimal impact resistance during ditch crossing with 30%–35%performance improvement.These findings provide quantitative design criteria and operational guidelines for optimizing TMV structural parameters and control strategies,offering a robust theoretical foundation for enhancing the performance,safety,and reliability of deep-sea mining equipment in complex submarine environments.展开更多
Feng Zhenyuan is a merchant selling knives and scissors in Yangjiang City,Guangdong Province.After over a decade of experience in the industry,he operates his own production facilities and distributes through multiple...Feng Zhenyuan is a merchant selling knives and scissors in Yangjiang City,Guangdong Province.After over a decade of experience in the industry,he operates his own production facilities and distributes through multiple e-commerce platforms including Pinduoduo,a Chinese online retailer whose main appeal is its shockingly low prices.“Pinduoduo has been relentlessly seeking low prices,”said Feng.“Many products claiming to be Yangjiang knives are priced 20 to 30 percent lower than genuine ones,leaving local merchants grappling with‘Gresham’s law,’which is about bad products driving out the good.”Feng added that about 30 percent of the factories in the Yangjiang knife and scissors sector have closed down,causing significant harm to this major pillar supporting local traditional industries.展开更多
Deep Underground Science and Engineering(DUSE)is pleased to present this special issue on Groundwater and Stability in Deep Mining.As mining operations progress to greater depths to meet the growing global demand for ...Deep Underground Science and Engineering(DUSE)is pleased to present this special issue on Groundwater and Stability in Deep Mining.As mining operations progress to greater depths to meet the growing global demand for mineral resources and energy,the challenges associated with groundwater control and rock mass stability have grown increasingly critical.These challenges are exacerbated by complex geological conditions,structural heterogeneity,and intense mining-induced disturbances.This special issue seeks to address these challenges by showcasing cutting-edge research and technological advancements in the field.展开更多
In the digital control centre of the Chambishi copper mine’s southeast deposit in Zambia,a massive screen displays the status of various mining activities in real time:extraction,digging,machine operation,and transpo...In the digital control centre of the Chambishi copper mine’s southeast deposit in Zambia,a massive screen displays the status of various mining activities in real time:extraction,digging,machine operation,and transport.Although machinery sounds can still be heard in the underground galleries,this“digital mine”relies more on an integrated computerised system than on traditional manual labour.“We can observe and understand underground activities in detail without going underground,”explained Dean Mwelwa,an executive at NFC Africa Mining(NFCA),pointing to the control screen.展开更多
Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected ...Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.展开更多
Enhancing the mining speed of a working face has become the primary approach to achieve high production and efficiency in coal mines,thereby further improving the production capacity.However,the problem of rock bursts...Enhancing the mining speed of a working face has become the primary approach to achieve high production and efficiency in coal mines,thereby further improving the production capacity.However,the problem of rock bursts resulting from this approach has become increasingly serious.Therefore,to implement coal mine safety and efficient extraction,the impact of deformation pressure caused by different mining speeds should be considered,and a reasonable mining speed of the working face should be determined.The influence of mining speed on overlying rock breaking in the stope is analyzed by establishing a key layer block rotation and subsidence model.Results show that with the increasing mining speed,the compression amount of gangue in the goaf decreases,and the rotation and subsidence amount of rock block B above goaf decreases,forcing the rotation and subsidence amount of rock block A above roadway to increase.Consequently,the contact mode between rock block A and rock block B changes from line contact to point contact,and the horizontal thrust and shear force between blocks increase.The increase in rotation and subsidence of rock block A intensifies the compression degree of coal and rock mass below the key layer,thereby increasing the stress concentration degree of coal and rock mass as well as the total energy accumulation.In addition,due to the insufficient compression of gangue in the goaf,the bending and subsidence space of the far-field key layer are limited,the length of the suspended roof increases,and the influence range of mining stress and the energy accumulation range expand.Numerical test results and underground microseismic monitoring results verify the correlation between mining speed and stope energy,and high-energy events generally appear 1-2 d after the change in mining speed.On this basis,the statistical principle confirms that the maximum mining speed of the working face at 6 m/d is reasonable.展开更多
Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various domains.The image retrieval sy...Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various domains.The image retrieval system mainly relies on the efficiency and accuracy of the classification models.This research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and CBIR.The novelty of the proposed EffNet-CNN model includes the integration of different techniques and modifications.The model includes the Mahalanobis distance metric for feature matching,which enhances the similarity measurements.The model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature representations.The EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 datasets.The model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior performance.The results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.展开更多
The publisher regrets that the article type for this publication was incorrectly labeled as a Research Article.The correct designation should be Review Article.This correction does not affect the content or conclusion...The publisher regrets that the article type for this publication was incorrectly labeled as a Research Article.The correct designation should be Review Article.This correction does not affect the content or conclusions of the article.The publisher apologizes for any inconvenience caused.展开更多
Mineral resources exploitation moving deeper into the earth is an inevitable trend with economic and social development.However,the deep high temperature poses a significant challenge to the safety and efficiency of h...Mineral resources exploitation moving deeper into the earth is an inevitable trend with economic and social development.However,the deep high temperature poses a significant challenge to the safety and efficiency of human and machine.The prevention of potential thermal risks in deep mining is critical.Here,the key and difficult issues of humanmachine-environment temperature monitoring are discussed according to the characteristics of deep hightemperature environment.Then,a monitoring and analysis method of human-machine-environment temperature field suitable for deep high-temperature mining areas is proposed.This method covers humanmachine-environment temperature monitoring,data storage and transmission,data processing,results visualization,and thermal risks warning.The monitoring sensor networks are constructed to collect real-time data of miners,machines,and environments.The data is transmitted to the central processing system for storage and analysis using both wired and wireless transmission technologies.Moreover,digital filtering and Kriging interpolation algorithms are applied to denoise and handle outliers in the monitored data,as well as to calculate the temperature field.The temperature prediction model is constructed using Long Short-Term Memory(LSTM)method.Finally,potential thermal risks are identified by combining real-time monitoring and prediction results,thereby guiding management personnels and miners to take appropriate measures.The proposed monitoring and analysis method can be applied to deep mines that affected by high temperature.It not only provides data and methodological support for assessing thermal risks in mines,but also offers scientific basis for optimizing mining operations and implementing safety measures.展开更多
The advancement of intelligent mining in open-pit operations has imposed higher demands on geological transparency,aiming to provide a robust foundation for intelligent drilling and charging.In this study,a linear arr...The advancement of intelligent mining in open-pit operations has imposed higher demands on geological transparency,aiming to provide a robust foundation for intelligent drilling and charging.In this study,a linear array of 120 nodal seismometers was deployed along the surfaces of the C8 and C9 platforms at Fenghuang Mountain to investigate cavities within the rock mass and prevent improper intelligent charging.The seismometers were 1 m apart along measurement lines,with a 2-m spacing between lines,and the monitoring time for each line was set at 2 h.This deployment was paired with spatial autocorrelation and station autocorrelation to analyze ambient noise seismic data and image the velocity and structure within the rock mass.The results demonstrate that the locations and sizes of cavities or loose structures can be accurately identified at the prepared excavation site.Compared with traditional geological exploration methods for openpit mines,the approach in this study off ers higher accuracy,greater efficiency,reduced labor intensity,and insensitivity to water conditions.Ambient noise seismic imaging for detecting adverse geological conditions in open-pit mines provides critical insights and references for intelligent mining advancements.展开更多
Identifying potential hazards is crucial for maintaining the structural stability of opencast mining area.To address the limitations of irregular structure and sparse microseismic events in opencast mining monitoring,...Identifying potential hazards is crucial for maintaining the structural stability of opencast mining area.To address the limitations of irregular structure and sparse microseismic events in opencast mining monitoring,this paper proposes an active-source imaging method for identifying potential hazards precisely based on velocity structure.This method innovatively divides the irregular structure into unstructured grids and introduces a damping and smoothing regularization operator into the inversion process,mitigating the ill-posedness caused by the sparse distribution of events and rays.Numerical and laboratory experiments were conducted to verify the reliability and effectiveness of the proposed method.The results demonstrate the competitive performance of the method in identifying hazard areas of varying sizes and numbers.The proposed method shows potential for meeting hazard identification requirements in the complex opencast mining structure.Furthermore,field experiments were conducted on an rare earth mine slope.It confirms that the proposed method provides a more concrete and intuitive scheme for stability monitoring for the microseismic monitoring system.This paper not only demonstrates the application of acoustic structure velocity imaging technology in detecting unstructured potential hazard regions but also provides valuable insights into the construction and maintenance of stable opencast mining area.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFC2811600)the National Natural Science Foundation of China(Grant Nos.52301349 and 52088102)+1 种基金the Qingdao Post-Doctorate Science Fund(No.QDBSH20220202070)the Major Scientific and Technological Innovation Project of Shandong Province(Grant No.2019JZZY010820).
文摘A deep-sea mining riser is a crucial component of the system used to lift seafloor mineral resources to the vessel.It is prone to damage and failure because of harsh environmental conditions and internal fluid erosion.Furthermore,damage can impact the response characteristics of the riser,but varying environmental loadings easily mask it.Thus,distin-guishing between riser damage and environmental effects poses a considerable challenge.To address this issue,a cantilevered model is created for a deep-sea mining riser via the concentrated mass method,and a time-domain analytical strategy is developed.The vortex-induced vibration(VIV)response characteristics of the riser are initially examined,considering various damage conditions and flow velocities.The study results revealed four primary observations:(a)effective tension can serve as a reliable indicator for identifying damage at lower velocities;(b)there are noticeable differences in displacement between the healthy and damaged risers in the in-line direction rather than the cross-flow direction;(c)frequency characteristics can more effectively distinguish the damage conditions at high flow velocities,with the mean square frequency and frequency variance being more effective than the centroid frequency and root variance frequency;(d)displacement differences are more sensitive to damage occurring near the top and bottom of the riser,while both velocity variations and structural damage can influence displacements,especially in regions between modal nodes.The vibrational behavior and damage indicators are clarified for structural health monitoring of deep-sea mining risers during lifting operations.
基金supported by the National Natural Science Foun dation of China(52374170 and 51974313)the National Key Research and Development Plan Project(2022YFF1303300).
文摘1.Introduction Changes in land use are key factors promoting global climate change,and the side effects of mining activity that destroy the soil,vegetation,and biodiversity lead to imbalanced carbon cycling in terrestrial ecosystems.
文摘Copper smelting is the main source of arsenic pollution in the environment,and China is the largest country for copper smelting.Taking 2022 as an example,this study analyzes the distribution and fate of arsenic across the copper mining,beneficiation,and smelting processes using a life-cycle approach,providing important insights for arsenic pollution prevention and the resource utilization of arsenic-bearing solid waste.The results show that the amount of As in waste rock,tailing and concentrate are 53483 t,86632 t,76162 t,respectively.After smelting treatment,the amount of arsenic in different types of solid waste,wastewater,waste gas and products are 76128 t,1 t,31 t and 2 t,respectively,and the proportion in arsenic sulfide slag is the highest(55%).The amount of emission to the environment is 32 t,accounting for only 0.04%of total amount.In the future,key considerations are to improve the resource utilization rate of arsenic-containing solid waste(tailing,smelting slag),especially arsenic sulfide slag,and to digest its environmental risk.
文摘Aseptic osteonecrosis of the femoral head is defined as the death of bone cells in the femoral epiphysis due to an interruption of blood supply. Most cases are linked to trauma, but non-traumatic cases also occur and can be associated with several known risk factors. This study aims to describe these risk factors identified in the former Katanga province, a region with significant mining activity. Method and Patients: This is a descriptive cross-sectional study conducted over a seven-year period (2017-2024), including all cases of aseptic osteonecrosis of the femoral head diagnosed in the orthopedic department of Medpark Clinic in Lubumbashi. The investigation of risk factors was based on the analysis of sociodemographic, clinical, radiological, and biological data. Results: Our study included a total of 110 patients with a mean age of 47.5 years. Among them, there were 46 women (41.82%) and 64 men (58.18%). Twenty-five patients (27.5%) reported a family history of osteonecrosis, and 24% were diagnosed with sickle cell disease. Chronic alcoholism was noted in 14 patients (12.73%), while diabetes was present in 8 (7.2%). Four patients (3.64%) were obese, and three were HIV-positive (2.72%). The use of nonsteroidal anti-inflammatory drugs (NSAIDs) was common, and prolonged corticosteroid use was documented in 5 patients (4.5%). Abnormally high cholesterol levels were found in 26 patients (23.6%). One patient had gout, and two suffered from acute rheumatic fever (1.8%). Regarding inflammatory markers, C-reactive protein levels and erythrocyte sedimentation rates were within normal limits for almost all patients. Electrolyte levels and phosphocalcic profiles showed no abnormalities. Furthermore, 33 patients (30%) did not exhibit any of the previously mentioned risk factors. Most of these patients lived in the regions of Kolwezi, Likasi, and Lubumbashi. Among this group, 25 patients reported performing physically demanding labor, particularly in mining operations. Conclusion: Our study highlighted well-known risk factors for osteonecrosis of the femoral head (ONFH). However, it also identified a significant number of cases without any identifiable risk factors, classified as idiopathic. Among these cases, some patients engaged in intense physical labor, often linked to mining exposure.
基金the National Science Fund for Distinguished Young Scholars(Grant No.52225107)National Key Research and Development Program of China(Grant No.2021YFC2801503)for funding provided to this work.
文摘Rising demand for minerals and metals in high-tech and new energy industries has led to a great interest in exploration of seabed mineral resources.Such resources,including polymetallic nodule(PMN),polymetallic sulphide(PMS),and cobalt-rich ferromanganese crust(CFC),are considered as an alternative source of metals to terrestrial deposits.Although a considerable number of sea trials of deep-sea mining have been carried out,the deep-sea mining does not achieve the commercial exploitation due to the complexity of deep-sea mining system and deep-sea mining environment.In fact,to achieve commercial deep-sea mining,the technology and equipment of deep-sea mining are the key points.Therefore,the present study presents the development of the technology and equipment of deep-sea mining.It commences with a requirement of technology and equipment for deep-sea mining,including environmental impact,reliability,energy cost,efficiency,etc.Then,a historical perspective and present-day effort related to deep-sea mining vehicles are given,which highlights the evolution of collection mechanism and walking mode of deep-sea mining vehicle.Subsequently,the present study discusses the operation of subsea lifting system and surface support system,shedding light on the crucial equipment and processes.The challenges and prospects in the deep-sea mining are presented in final,including environmental protection,self-propelled crawler,hydraulic pipeline lifting,and intelligent equipment,etc.
文摘Shear strain energy is a pivotal physical quantity in the occurrence of earthquakes and rockbursts during deep mining operations.This research is focused on understanding the changes in shear strain energy in the context of retreating longwall mining,which is essential for the optimized design and mitigation of rockbursts and seismic events.Through the application of innovative analytical models,this study expands its analytical range to include the variations in shear strain energy caused by fault coseismic slip.An integrated methodology is utilized,taking into account the changes in coseismic and fault friction parameters as well as enhancements in mining-induced stress and existing background stresses.Our numerical investigation highlights the significance of mining location and fault characteristics as key determinants of shear strain energy modifications.The analysis demonstrates significant spatial variability in shear strain energy,especially noting that fault slip near the mining face greatly increases the likelihood of rockburst.This finding emphasizes the need to integrate fault coseismic slip dynamics into the triggering factors of rock(coal)bursts,thus broadening the theoretical foundation for addressing geological hazards in deep mining operations.The results are further corroborated by observational data from the vicinity of the F16 fault zone,introducing the concept of mining-induced fault coseismic slip as an essential element in the theoretical framework for understanding rockburst triggers.
文摘An application programming interface (API) usage specifcation, which includes the conditions, calling sequences, and semantic relationships of the API, is important for verifying its correct usage, which is in turn critical for ensur-ingthe security and availability of the target program. However, existing techniques either mine the co-occurring relationships of multiple APIs without considering their semantic relationships, or they use data fow and control fow information to extract semantic beliefs on API pairs but difcult to incorporate when mining specifcations for mul-tipleAPIs. Hence, we propose an API specifcation mining approach that efciently extracts a relatively complete list of the API combinations and semantic relationships between APIs. This approach analyzes a target program in two stages. The frst stage uses frequent API set mining based on frequent common API identifcation and fltra-tionto extract the maximal set of frequent context-sensitive API sequences. In the second stage, the API relationship graph is constructed using three semantic relationships extracted from the symbolic path information, and the speci-fcationscontaining semantic relationships for multiple APIs are mined. The experimental results on six popular open-source code bases of diferent scales show that the proposed two-stage approach not only yields better results than existing typical approaches, but also can efectively discover the specifcations along with the semantic rela-tionshipsfor multiple APIs. Instance analysis shows that the analysis of security-related API call violations can assist in the cause analysis and patch of software vulnerabilities.
基金financially supported by the National Key Research and Development Program of China-Young Scientist Project(No.2024YFC2815400)the National Natural Science Foundation of China(No.52588202).
文摘Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineering challenges.This study establishes a multiscale modelling framework coupling the discrete element method(DEM)with multi-body dynamics(MBD)to investigate track-seabed dynamic interactions across three operational modes:flat terrain,slope climbing,and ditch surmounting.The simulation framework,validated against laboratory experiments,systematically evaluates the influence of grouser geometry(involute,triangular,and pin-type)and traveling speed(0.2–1.0 m/s)on traction performance,slip rate,and ground pressure distribution.Results reveal rate-dependent traction mechanisms governed by soil microstructural responses:higher speeds enhance peak traction but exacerbate slip instability on complex terrain.Critical operational thresholds are established—0.7 m/s for flat terrain,≤0.5 m/s for slopes and ditches—with distinct grouser optimization strategies:involute grousers achieve 35%–40%slip reduction on slopes through progressive soil engagement,while triangular grousers provide optimal impact resistance during ditch crossing with 30%–35%performance improvement.These findings provide quantitative design criteria and operational guidelines for optimizing TMV structural parameters and control strategies,offering a robust theoretical foundation for enhancing the performance,safety,and reliability of deep-sea mining equipment in complex submarine environments.
文摘Feng Zhenyuan is a merchant selling knives and scissors in Yangjiang City,Guangdong Province.After over a decade of experience in the industry,he operates his own production facilities and distributes through multiple e-commerce platforms including Pinduoduo,a Chinese online retailer whose main appeal is its shockingly low prices.“Pinduoduo has been relentlessly seeking low prices,”said Feng.“Many products claiming to be Yangjiang knives are priced 20 to 30 percent lower than genuine ones,leaving local merchants grappling with‘Gresham’s law,’which is about bad products driving out the good.”Feng added that about 30 percent of the factories in the Yangjiang knife and scissors sector have closed down,causing significant harm to this major pillar supporting local traditional industries.
文摘Deep Underground Science and Engineering(DUSE)is pleased to present this special issue on Groundwater and Stability in Deep Mining.As mining operations progress to greater depths to meet the growing global demand for mineral resources and energy,the challenges associated with groundwater control and rock mass stability have grown increasingly critical.These challenges are exacerbated by complex geological conditions,structural heterogeneity,and intense mining-induced disturbances.This special issue seeks to address these challenges by showcasing cutting-edge research and technological advancements in the field.
文摘In the digital control centre of the Chambishi copper mine’s southeast deposit in Zambia,a massive screen displays the status of various mining activities in real time:extraction,digging,machine operation,and transport.Although machinery sounds can still be heard in the underground galleries,this“digital mine”relies more on an integrated computerised system than on traditional manual labour.“We can observe and understand underground activities in detail without going underground,”explained Dean Mwelwa,an executive at NFC Africa Mining(NFCA),pointing to the control screen.
基金Supported by National Natural Science Foundation of ChinaShanxi Coalbased Low-Carbon Joint Fund(Grant No.U1910211)。
文摘Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.
基金supported by Technology Innovation Fund of China Coal Research Institute(2022CX-I-04)Science and Technology Innovation Venture Capital Project of China Coal Technology Engineering Group(2020-2-TD-CXY005)。
文摘Enhancing the mining speed of a working face has become the primary approach to achieve high production and efficiency in coal mines,thereby further improving the production capacity.However,the problem of rock bursts resulting from this approach has become increasingly serious.Therefore,to implement coal mine safety and efficient extraction,the impact of deformation pressure caused by different mining speeds should be considered,and a reasonable mining speed of the working face should be determined.The influence of mining speed on overlying rock breaking in the stope is analyzed by establishing a key layer block rotation and subsidence model.Results show that with the increasing mining speed,the compression amount of gangue in the goaf decreases,and the rotation and subsidence amount of rock block B above goaf decreases,forcing the rotation and subsidence amount of rock block A above roadway to increase.Consequently,the contact mode between rock block A and rock block B changes from line contact to point contact,and the horizontal thrust and shear force between blocks increase.The increase in rotation and subsidence of rock block A intensifies the compression degree of coal and rock mass below the key layer,thereby increasing the stress concentration degree of coal and rock mass as well as the total energy accumulation.In addition,due to the insufficient compression of gangue in the goaf,the bending and subsidence space of the far-field key layer are limited,the length of the suspended roof increases,and the influence range of mining stress and the energy accumulation range expand.Numerical test results and underground microseismic monitoring results verify the correlation between mining speed and stope energy,and high-energy events generally appear 1-2 d after the change in mining speed.On this basis,the statistical principle confirms that the maximum mining speed of the working face at 6 m/d is reasonable.
基金The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University,Kingdom of Saudi Arabia,for funding this work through the Small Research Group Project under Grant Number RGP.1/316/45.
文摘Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various domains.The image retrieval system mainly relies on the efficiency and accuracy of the classification models.This research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and CBIR.The novelty of the proposed EffNet-CNN model includes the integration of different techniques and modifications.The model includes the Mahalanobis distance metric for feature matching,which enhances the similarity measurements.The model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature representations.The EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 datasets.The model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior performance.The results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.
文摘The publisher regrets that the article type for this publication was incorrectly labeled as a Research Article.The correct designation should be Review Article.This correction does not affect the content or conclusions of the article.The publisher apologizes for any inconvenience caused.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(Grant No.52425403)。
文摘Mineral resources exploitation moving deeper into the earth is an inevitable trend with economic and social development.However,the deep high temperature poses a significant challenge to the safety and efficiency of human and machine.The prevention of potential thermal risks in deep mining is critical.Here,the key and difficult issues of humanmachine-environment temperature monitoring are discussed according to the characteristics of deep hightemperature environment.Then,a monitoring and analysis method of human-machine-environment temperature field suitable for deep high-temperature mining areas is proposed.This method covers humanmachine-environment temperature monitoring,data storage and transmission,data processing,results visualization,and thermal risks warning.The monitoring sensor networks are constructed to collect real-time data of miners,machines,and environments.The data is transmitted to the central processing system for storage and analysis using both wired and wireless transmission technologies.Moreover,digital filtering and Kriging interpolation algorithms are applied to denoise and handle outliers in the monitored data,as well as to calculate the temperature field.The temperature prediction model is constructed using Long Short-Term Memory(LSTM)method.Finally,potential thermal risks are identified by combining real-time monitoring and prediction results,thereby guiding management personnels and miners to take appropriate measures.The proposed monitoring and analysis method can be applied to deep mines that affected by high temperature.It not only provides data and methodological support for assessing thermal risks in mines,but also offers scientific basis for optimizing mining operations and implementing safety measures.
基金National science and technology signifi cant special(No.2024ZD1003406)Natural Science Research Project of Colleges and Universities in Anhui Province(No.2024AH050374)National Natural Science Foundation of China(Grant No.52274071).
文摘The advancement of intelligent mining in open-pit operations has imposed higher demands on geological transparency,aiming to provide a robust foundation for intelligent drilling and charging.In this study,a linear array of 120 nodal seismometers was deployed along the surfaces of the C8 and C9 platforms at Fenghuang Mountain to investigate cavities within the rock mass and prevent improper intelligent charging.The seismometers were 1 m apart along measurement lines,with a 2-m spacing between lines,and the monitoring time for each line was set at 2 h.This deployment was paired with spatial autocorrelation and station autocorrelation to analyze ambient noise seismic data and image the velocity and structure within the rock mass.The results demonstrate that the locations and sizes of cavities or loose structures can be accurately identified at the prepared excavation site.Compared with traditional geological exploration methods for openpit mines,the approach in this study off ers higher accuracy,greater efficiency,reduced labor intensity,and insensitivity to water conditions.Ambient noise seismic imaging for detecting adverse geological conditions in open-pit mines provides critical insights and references for intelligent mining advancements.
基金Project(2021YFC2900500)supported by the National Key Research and Development Program of China。
文摘Identifying potential hazards is crucial for maintaining the structural stability of opencast mining area.To address the limitations of irregular structure and sparse microseismic events in opencast mining monitoring,this paper proposes an active-source imaging method for identifying potential hazards precisely based on velocity structure.This method innovatively divides the irregular structure into unstructured grids and introduces a damping and smoothing regularization operator into the inversion process,mitigating the ill-posedness caused by the sparse distribution of events and rays.Numerical and laboratory experiments were conducted to verify the reliability and effectiveness of the proposed method.The results demonstrate the competitive performance of the method in identifying hazard areas of varying sizes and numbers.The proposed method shows potential for meeting hazard identification requirements in the complex opencast mining structure.Furthermore,field experiments were conducted on an rare earth mine slope.It confirms that the proposed method provides a more concrete and intuitive scheme for stability monitoring for the microseismic monitoring system.This paper not only demonstrates the application of acoustic structure velocity imaging technology in detecting unstructured potential hazard regions but also provides valuable insights into the construction and maintenance of stable opencast mining area.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
