In this work,the combined addition of strontium/indium(Sr/In)to the magnesium anode for Mg-Air Cells is investigated to improve discharge performance by modifying the anode/electrolyte interface.Indium exists as solid...In this work,the combined addition of strontium/indium(Sr/In)to the magnesium anode for Mg-Air Cells is investigated to improve discharge performance by modifying the anode/electrolyte interface.Indium exists as solid solution atoms in theα-Mg matrix without its second-phase generation,and at the same time facilitates grain refinement,dendritic segregation and Mg17Sr2-phases precipitation.During discharge operation,Sr modifies the film composition via its compounds and promoted the redeposition of In at the substrate/film interface;their co-deposition behavior on the anodic reaction surface enhances anode reaction kinetics,suppresses the negative difference effect(NDE)and mitigates the“chunk effect”(CE),which is contributed to uniform dissolution and low self-corrosion hydrogen evolution rate(HER).Therefore,Mg-Sr-xIn alloy anodes show excellent discharge performance,e.g.,0.5Sr-1.0In shows an average discharge voltage of 1.4234 V and a specific energy density of 1990.71 Wh kg^(-1)at 10 mA cm^(-2).Furthermore,the decisive factor(CE and self-discharge HE)for anodic efficiency are quantitively analyzed,the self-discharge is the main factor of cell efficiency loss.Surprisingly,all Mg-Sr-xIn anodes show anodic efficiency greater than 60%at high current density(≥10 mA cm^(-2)),making them excellent candidate anodes for Mg-Air cells at high-power output.展开更多
This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
7039 Al alloys are widely used in armor vehicles,given the material’s high specific strength and fracture toughness.However,laminar tearing in the thickness plane of the base metal(BM),specifically in the normal dire...7039 Al alloys are widely used in armor vehicles,given the material’s high specific strength and fracture toughness.However,laminar tearing in the thickness plane of the base metal(BM),specifically in the normal direction(ND)and rolling direction(RD)plane,was occasionally observed after the welding of thick plates,resulting in premature material failure.A vertically metal-inert gas(MIG)-welded laminar tearing component of a 30 mm thick plate was analyzed to determine the factors associated with this phenomenon.The texture,residual stress,microhardness,and tensile properties were also investigated.The results indicated that the crack extended along the RD as a transcrystalline fracture and terminated at the BM.The grains near the crack grew preferentially in the(001)crystal direction.Furthermore,the tensile strength(83 MPa)and elongation(6.8%)in the RD were relatively higher than those in the ND.In particular,the primary factors for crack initiation include stronger texture,higher dislocation density,increased Al_(7)Cu_(2)Fe phases,lower proportion of small-angle grain boundaries,and varying grain sizes in different regions,leading to the fragile microstructure.The higher residual stress of the BM promotes the formation and extension of cracks.The restraining force due to fixation and welding shrinkage force transformed the crack into laminar tearing.Preventive measures of laminar tearing were also proposed.展开更多
Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction an...Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.展开更多
This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs...This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs)through the blood vessels of the human body.The rheology of gold-blood nanofluid is treated as magnetohydrodynamic(MHD)flow with ferromagnetic properties.The AuNPs take different shapes as bricks,cylinders,and platelets which are considered in changing the nanofluid flow behavior.Physiologically,the blood is circulated under the kinetics of the peristaltic action.The mixed properties of the slip flow,the gravity,the space porosity,the transverse ferromagnetic field,the thermal radiation,the nanoparticles shape factors,the peristaltic amplitude ratio,and the concentration of the AuNPs are interacted and analyzed for the gold-blood circulation in the inclined tube.The appropriate model for the thermal conductivity of the nanofluid is chosen to be the effective Hamilton-Crosser model.The undertaken nanofluid can be treated as incompressible non-Newtonian ferromagnetic fluid.The solutions of the partial differential governing equations of the MHD nanofluid flow are executed by the strategy of perturbation approach under the assumption of long wavelength and low Reynolds number.Graphs for the streamwise velocity distributions,temperature distributions,pressure gradients,pressure drops,and streamlines are presented under the influences of the pertinent properties.The practical implementation of this research finds application in treating cancer through a technique known as photothermal therapy(PTT).The results indicate the control role of the magnetism,the heat generation,the shape factors of the AuNPs,and its concentration on the enhancement of the thermal properties and the streamwise velocity of the nanofluid.The results reveal a marked enhancement in the temperature profiles of the nanofluid,prominently influenced by both the intensified heat source and the heightened volume fractions of the nanoparticles.Furthermore,the platelet shape is regarded as most advantageous for heat conduction owing to its highest effective thermal conductivity.AuNPs proved strong efficiency in delivering and targeting the drug to reach the affected area with tumors.These results offer valuable insights into evaluating the effectiveness of PTT in addressing diverse cancer conditions and regulating their progression.展开更多
AZ31 magnesium alloy was used as the object of study to fabricate an alloy with the bimodal grain structure using singlepass hot rolling,and to explore how this structure enhances the strength and plasticity of the al...AZ31 magnesium alloy was used as the object of study to fabricate an alloy with the bimodal grain structure using singlepass hot rolling,and to explore how this structure enhances the strength and plasticity of the alloy.The results show that the formation of the bimodal grain structure is more pronounced at rolling temperatures ranging from 350°C to 450°C,especially under conditions of large reduction(≥40%).The optimized proportion and distribution of the bimodal grain structure play a pivotal role in simultaneously enhancing the strength and ductility of the alloy,significantly impacting the mechanical properties.The rolled sheet with the bimodal grain structure achieves an ultimate tensile strength of 258.3 MPa and an elongation of 17.1%under a rolling reduction of 40%with the rolling rate of 75 m/min and rolling temperature of 400°C.Adjusting rolling parameters,including temperature,reduction ratio and rolling rate,is crucial for optimizing the bimodal grain structure,thereby achieving a balance between plasticity improvement and high strength maintenance.展开更多
Interstitial oxygen(O)contamination remains a substantial challenge for metal injection molding(MIM)of titanium alloys.Herein,this critical problem is successfully addressed by regulating the thermal debinding tempera...Interstitial oxygen(O)contamination remains a substantial challenge for metal injection molding(MIM)of titanium alloys.Herein,this critical problem is successfully addressed by regulating the thermal debinding temperature and incorporating the oxygen scavenger LaB_(6).Results indicate that the surface oxide layer(with a thickness of(13.4±0.5)nm)of Ti_(6)Al4V powder begins to dissolve into the Ti matrix within the temperature range of 663–775℃.O contamination in MIM Ti alloys can be effectively mitigated by lowering the thermal debinding temperature and adding LaB6powder.As a result of reduced dissolved O content,the slips of mixedanddislocations are effectively accelerated,leading to improved ductility.Moreover,grain refinement,along with the in situ formation of Ti B whiskers and second-phase La_(2)O_(3)particles,enhances the strength of the material.The fabricated MIM Ti6Al4V sample exhibits excellent mechanical properties,achieving an ultimate tensile strength of(967±5)MPa,a yield strength of(866±8)MPa,and an elongation of 21.4%±0.7%.These tensile properties represent some of the best results reported in the literature for MIM Ti_(6)Al4V alloys.This study offers valuable insights into the development of high-performance MIM Ti alloys and other metal materials.展开更多
The gas kick represents a major risk in deepwater oil and gas exploration.Understanding the dynamics of gas kick evolution and the associated pressure response characteristics is critical for effective well control.In...The gas kick represents a major risk in deepwater oil and gas exploration.Understanding the dynamics of gas kick evolution and the associated pressure response characteristics is critical for effective well control.In this paper,we introduce a transient wellbore multiphase flow model specifically developed to simulate gas kick in deepwater dual-gradient drilling,incorporating a downhole separator.The model accounts for the variable mass flow within the annulus and heat exchange between the annular fluid and the formation.Using this model,we analyzed the multiphase flow and thermodynamic behavior during the gas kick.Simulation results reveal a progressive increase in bottom-hole temperature,underscoring its potential as a key indicator for gas kick early detection.Additionally,variable gradient parameters affect not only the annular equivalent circulating density(ECD)profile but also the evolution of the gas kick.The inclusion of a downhole separator alters the annular ECD profile,creating a“broken line”shape,which enhances adaptability to the multi-pressure systems typically encountered in deepwater forma-tion.By adjusting factors such as hollow sphere concentration,separator position,and separation effi-ciency,the annular ECD profile can be effectively customized.This study provides important theoretical insights and practical applications for utilizing dual-gradient drilling technology to address challenges in deepwater formation drilling.展开更多
Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplit...Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplitude modulation(ISAM)based on sparse feature adaptive convolution(SFAC)is proposed to enhance the fault features under variable speed conditions.First,an optimal bi-damped wavelet construction method is proposed to learn signal impulse features,which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization.Second,a convolutional basis pursuit denoising model based on an optimal bi-damped wavelet is proposed for resolving sparse impulses.A model regularization parameter selection method based on weighted fault characteristic amplitude ratio assistance is proposed.Then,an ISAM method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal.Finally,the type of variable speed faults is determined by order spectrum analysis.Various experimental results,such as spectral amplitude modulation and Morlet wavelet matching,verify the effectiveness and advantages of the ISAM-SFAC method.展开更多
The MIG welding of in-situ generated nano-Al_(2)O_(3)powder metallurgy 7A52(PM 7A52)aluminum alloy was investigated.The microstructure was characterized using EBSD and TEM,while macrotexture and internal residual stre...The MIG welding of in-situ generated nano-Al_(2)O_(3)powder metallurgy 7A52(PM 7A52)aluminum alloy was investigated.The microstructure was characterized using EBSD and TEM,while macrotexture and internal residual stresses were analyzed with a self-developed SWXRD technique.The results revealed that PM 7A52 aluminum alloy effectively reduced the grain size,dislocation density,and texture strength in the post-weld microstructure.Furthermore,the residual stress in the weld zone(WZ)of PM 7A52 aluminum alloy was reduced by 38 MPa compared to that of the conventional melt-cast 7A52(CM 7A52)aluminum alloy.Notably,the tensile strength and elongation of welded joints in PM 7A52 aluminum alloy were increased by approximately 15%and 26%,respectively.The improvement in joint tensile strength was primarily attributed to grain boundary strengthening and dispersion strengthening caused byγ-Al_(2)O_(3)particles entering the WZ.展开更多
In the present study,the mechanical and ballistic properties of friction stir welded(FSW)aluminum alloy(AA5754)samples were investigated,both untreated and cryogenically treated,when impacted by a 7.62 mm armour-pierc...In the present study,the mechanical and ballistic properties of friction stir welded(FSW)aluminum alloy(AA5754)samples were investigated,both untreated and cryogenically treated,when impacted by a 7.62 mm armour-piercing(AP)bullet at an impact velocity of 682±20 m/s.The FSW technique was used to prepare the welded samples for AA5754,with an axial force of 7 kN,a feed rate of 20 mm/min,and a speed of 1200 rpm.The cryogenic treatments performed after welding,including deep cryogenic treatment(DCT)at196℃ and shallow cryogenic treatment(SCT)at80℃,for 6 and 72 h,respectively.The microstructure and mechanical characteristics of cryogenically treated and untreated joints were examined.The cryogenic treatment refined the grain size(1.05 μm)and enhanced the microhardness(93 Hv).Moreover,DCT-FSW significantly improved the tensile strength(13.93%)and impact strength(8.45%)compared to untreated FSW sample.Additionally,in untreated FSW samples,the fracture behaviour varied:the impact fracture mode primarily exhibited ductile failure,while the tensile fracture exhibited a mixed fracture mode.In contrast,the tensile and impact fracture modes of the DCT-FSWwere dominated by a ductile failure mode.The DCT-FSW target demonstrated a lower depth of penetration(DOP)of 31 mm compared to the SCT-FSWand untreated FSW targets.Post-ballistic SEM analysis in the crater region of all three zones revealed the formation of frictional grooves,small cracks,and adiabatic shear bands(ASBs).展开更多
A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehens...A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.展开更多
The article"Assessment of superior mesenteric vascular flow quantitation in children using four-dimensional flow magnetic resonance imaging"suggests to use of four-dimensional(4D)flow magnetic resonance imag...The article"Assessment of superior mesenteric vascular flow quantitation in children using four-dimensional flow magnetic resonance imaging"suggests to use of four-dimensional(4D)flow magnetic resonance imaging(MRI)which is also to measure the blood flow in the superior mesenteric vein(SMV)in pediatric patients over the traditional method.The study focuses on assessing the potential of SMV and superior mesenteric artery(SMA)flow quantification in children utilizing 4D flow MRI.It included 9 pediatric patients aged 18 years and below where 5 were male and 4 were female patients,on whom magnetic resonance enterorrhaphy(MRE)with 4D flow MRI protocol was used.Statistical analysis was performed using MedCalc.Measurements of SMV and SMA between two readers were calculated using Bland-Altman analysis.The results stated that six patients showed no MRE evidence of active inflammatory bowel disease,two patients showed unmarkable bowel appearance on MRI and one patient showed normal MRE without endoscopy performed at the same timeframe.The study utilized available 4D flow MRI sequences in this study aiming to show the feasibility of 4D flow quantitation of SMA and SMV flow in pediatric patients.The study also discovered good agreement for both peak velocity and peak speed measurements of SMA and SMV.展开更多
This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques strug...This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex,nonlinear natures.The Sperm Swarm Optimization(SSO)algorithm,which mimics the sperm’s movement to reach an egg,is one such technique.To improve SSO,researchers combined it with three strategies:opposition-based learning(OBL),Cauchy mutation(CM),and position clamping.OBL introduces diversity to SSO by exploring opposite solutions,speeding up convergence.CM enhances both exploration and exploitation capabilities throughout the optimization process.This combined approach,RSSO,has been rigorously tested on standard benchmark functions,real-world engineering problems,and through statistical analysis(Wilcoxon test).The results demonstrate that RSSO significantly outperforms other optimization algorithms,achieving faster convergence and better solutions.The paper details the RSSO algorithm,discusses its implementation,and presents comparative results that validate its effectiveness in solving complex engineering design challenges.展开更多
Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing ...Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing and quadruplexing Substrate-Integrated Waveguide (SIW) cavities. The diplexing structure incorporates two V-shaped slots, while the quadruplexing structure advances this concept by combining the slots to form a cross-shaped configuration within the cavity. The widths and lengths of the slots are carefully tuned to achieve variations in the respective operating frequencies without affecting the others. The proposed diplexing antenna resonates at 8.48 and 9.2 GHz, with a frequency ratio of 1.08, while the quadruplexing antenna operates at 6.9, 7.1, 7.48, and 8.2GHz. Both designs exhibit isolation levels well below –20dB and achieve a simulated peak gain of 5.6 dBi at the highest frequency, with a compact cavity area of 0.56 λg^(2). The proposed antennas operate within the NR bands (n12, n18, n26), making them suitable for modern high-speed wireless communication systems. Moreover, the properties like multiband operation, compactness, high isolation, low loss, and low interference make the antenna favorable for the high-speed railway communication systems.展开更多
The behavior of single-phase flow and conjugate heat transfer in micro-channel heat sinks(MCHS)subjected to auniform heat flux is investigated by means of numerical simulations.Various geometrical configurations areex...The behavior of single-phase flow and conjugate heat transfer in micro-channel heat sinks(MCHS)subjected to auniform heat flux is investigated by means of numerical simulations.Various geometrical configurations areexamined,particularly,the combinations of rectangular solid and perforated blocks,used to create a disturbancein the flow.The analysis focuses on several key aspects and related metrics,including the temperature distribution,the mean Fanning friction factor,the pressure drop,the Nusselt number,and the overall heat transfer coefficientacross a range of Reynolds numbers(80–870).It is shown that the introduction of such blocks significantlyenhances the heat transfer performances of the MCHS compared to the straight-through flow channel.Specifically,a case is found where the Nusselt number increases by 2.3 times relative to the reference case.The integrationof perforated blocks facilitates the generation of vorticity within the channel,promoting the mixing of coldand hot fluids.Notably,MCHS incorporating perforated rectangular blocks exhibit more pronounced heat transferbenefits at Reynolds numbers smaller than 400.展开更多
Duplicate bug reporting is a critical problem in the software repositories’mining area.Duplicate bug reports can lead to redundant efforts,wasted resources,and delayed software releases.Thus,their accurate identifica...Duplicate bug reporting is a critical problem in the software repositories’mining area.Duplicate bug reports can lead to redundant efforts,wasted resources,and delayed software releases.Thus,their accurate identification is essential for streamlining the bug triage process mining area.Several researchers have explored classical information retrieval,natural language processing,text and data mining,and machine learning approaches.The emergence of large language models(LLMs)(ChatGPT and Huggingface)has presented a new line of models for semantic textual similarity(STS).Although LLMs have shown remarkable advancements,there remains a need for longitudinal studies to determine whether performance improvements are due to the scale of the models or the unique embeddings they produce compared to classical encoding models.This study systematically investigates this issue by comparing classical word embedding techniques against LLM-based embeddings for duplicate bug detection.In this study,we have proposed an amalgamation of models to detect duplicate bug reports using textual and non-textual information about bug reports.The empirical evaluation has been performed on the open-source datasets and evaluated based on established metrics using the mean reciprocal rank(MRR),mean average precision(MAP),and recall rate.The experimental results have shown that combined LLMs can outperform(recall-rate@k 68%–74%)other individual=models for duplicate bug detection.These findings highlight the effectiveness of amalgamating multiple techniques in improving the duplicate bug report detection accuracy.展开更多
Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are ...Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.展开更多
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
Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is availabl...Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.展开更多
文摘In this work,the combined addition of strontium/indium(Sr/In)to the magnesium anode for Mg-Air Cells is investigated to improve discharge performance by modifying the anode/electrolyte interface.Indium exists as solid solution atoms in theα-Mg matrix without its second-phase generation,and at the same time facilitates grain refinement,dendritic segregation and Mg17Sr2-phases precipitation.During discharge operation,Sr modifies the film composition via its compounds and promoted the redeposition of In at the substrate/film interface;their co-deposition behavior on the anodic reaction surface enhances anode reaction kinetics,suppresses the negative difference effect(NDE)and mitigates the“chunk effect”(CE),which is contributed to uniform dissolution and low self-corrosion hydrogen evolution rate(HER).Therefore,Mg-Sr-xIn alloy anodes show excellent discharge performance,e.g.,0.5Sr-1.0In shows an average discharge voltage of 1.4234 V and a specific energy density of 1990.71 Wh kg^(-1)at 10 mA cm^(-2).Furthermore,the decisive factor(CE and self-discharge HE)for anodic efficiency are quantitively analyzed,the self-discharge is the main factor of cell efficiency loss.Surprisingly,all Mg-Sr-xIn anodes show anodic efficiency greater than 60%at high current density(≥10 mA cm^(-2)),making them excellent candidate anodes for Mg-Air cells at high-power output.
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
基金supported by the National Key Research and Development Program of China(No.SQ2021YFF 0600011).
文摘7039 Al alloys are widely used in armor vehicles,given the material’s high specific strength and fracture toughness.However,laminar tearing in the thickness plane of the base metal(BM),specifically in the normal direction(ND)and rolling direction(RD)plane,was occasionally observed after the welding of thick plates,resulting in premature material failure.A vertically metal-inert gas(MIG)-welded laminar tearing component of a 30 mm thick plate was analyzed to determine the factors associated with this phenomenon.The texture,residual stress,microhardness,and tensile properties were also investigated.The results indicated that the crack extended along the RD as a transcrystalline fracture and terminated at the BM.The grains near the crack grew preferentially in the(001)crystal direction.Furthermore,the tensile strength(83 MPa)and elongation(6.8%)in the RD were relatively higher than those in the ND.In particular,the primary factors for crack initiation include stronger texture,higher dislocation density,increased Al_(7)Cu_(2)Fe phases,lower proportion of small-angle grain boundaries,and varying grain sizes in different regions,leading to the fragile microstructure.The higher residual stress of the BM promotes the formation and extension of cracks.The restraining force due to fixation and welding shrinkage force transformed the crack into laminar tearing.Preventive measures of laminar tearing were also proposed.
基金supported by the National Key Research and Development Plan(Grant No.2023YFB3712400)the National Key Research and Development Plan(Grant No.2020YFB1713600).
文摘Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.
文摘This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs)through the blood vessels of the human body.The rheology of gold-blood nanofluid is treated as magnetohydrodynamic(MHD)flow with ferromagnetic properties.The AuNPs take different shapes as bricks,cylinders,and platelets which are considered in changing the nanofluid flow behavior.Physiologically,the blood is circulated under the kinetics of the peristaltic action.The mixed properties of the slip flow,the gravity,the space porosity,the transverse ferromagnetic field,the thermal radiation,the nanoparticles shape factors,the peristaltic amplitude ratio,and the concentration of the AuNPs are interacted and analyzed for the gold-blood circulation in the inclined tube.The appropriate model for the thermal conductivity of the nanofluid is chosen to be the effective Hamilton-Crosser model.The undertaken nanofluid can be treated as incompressible non-Newtonian ferromagnetic fluid.The solutions of the partial differential governing equations of the MHD nanofluid flow are executed by the strategy of perturbation approach under the assumption of long wavelength and low Reynolds number.Graphs for the streamwise velocity distributions,temperature distributions,pressure gradients,pressure drops,and streamlines are presented under the influences of the pertinent properties.The practical implementation of this research finds application in treating cancer through a technique known as photothermal therapy(PTT).The results indicate the control role of the magnetism,the heat generation,the shape factors of the AuNPs,and its concentration on the enhancement of the thermal properties and the streamwise velocity of the nanofluid.The results reveal a marked enhancement in the temperature profiles of the nanofluid,prominently influenced by both the intensified heat source and the heightened volume fractions of the nanoparticles.Furthermore,the platelet shape is regarded as most advantageous for heat conduction owing to its highest effective thermal conductivity.AuNPs proved strong efficiency in delivering and targeting the drug to reach the affected area with tumors.These results offer valuable insights into evaluating the effectiveness of PTT in addressing diverse cancer conditions and regulating their progression.
基金Corresponding author:Jiang Haitao,Ph.D.,Professor,Institute of Engineering Technology,University of Science and Technology Beijing,Beijing 102206,P.R.China,Tel:0086-10-62332598,E-mail:jianght@ustb.edu.cn。
文摘AZ31 magnesium alloy was used as the object of study to fabricate an alloy with the bimodal grain structure using singlepass hot rolling,and to explore how this structure enhances the strength and plasticity of the alloy.The results show that the formation of the bimodal grain structure is more pronounced at rolling temperatures ranging from 350°C to 450°C,especially under conditions of large reduction(≥40%).The optimized proportion and distribution of the bimodal grain structure play a pivotal role in simultaneously enhancing the strength and ductility of the alloy,significantly impacting the mechanical properties.The rolled sheet with the bimodal grain structure achieves an ultimate tensile strength of 258.3 MPa and an elongation of 17.1%under a rolling reduction of 40%with the rolling rate of 75 m/min and rolling temperature of 400°C.Adjusting rolling parameters,including temperature,reduction ratio and rolling rate,is crucial for optimizing the bimodal grain structure,thereby achieving a balance between plasticity improvement and high strength maintenance.
基金financially supported by the National Natural Science Foundation of China(Nos.52274359 and 52304379)Beijing Natural Science Foundation,China(No.L212021)+4 种基金China National Postdoctoral Program for Innovative Talents(No.BX20220034)China Postdoctoral Science Foundation(No.2022M720403)Fundamental Research Funds for the Central Universities,China(Nos.FRFTP-19005C1Z and 00007718)AECC University Research Cooperation Project,China(No.HFZL2021CXY021)State Key Lab of Advanced Metals and Materials,University of Science and Technology Beijing,China(Nos.2021Z-03 and 2022Z-14)。
文摘Interstitial oxygen(O)contamination remains a substantial challenge for metal injection molding(MIM)of titanium alloys.Herein,this critical problem is successfully addressed by regulating the thermal debinding temperature and incorporating the oxygen scavenger LaB_(6).Results indicate that the surface oxide layer(with a thickness of(13.4±0.5)nm)of Ti_(6)Al4V powder begins to dissolve into the Ti matrix within the temperature range of 663–775℃.O contamination in MIM Ti alloys can be effectively mitigated by lowering the thermal debinding temperature and adding LaB6powder.As a result of reduced dissolved O content,the slips of mixedanddislocations are effectively accelerated,leading to improved ductility.Moreover,grain refinement,along with the in situ formation of Ti B whiskers and second-phase La_(2)O_(3)particles,enhances the strength of the material.The fabricated MIM Ti6Al4V sample exhibits excellent mechanical properties,achieving an ultimate tensile strength of(967±5)MPa,a yield strength of(866±8)MPa,and an elongation of 21.4%±0.7%.These tensile properties represent some of the best results reported in the literature for MIM Ti_(6)Al4V alloys.This study offers valuable insights into the development of high-performance MIM Ti alloys and other metal materials.
基金supported by the Postdoctoral Fellow-ship Program of CPSF(Grant No.GZC20233105)the Science Foundation of China University of Petroleum,Beijing(Grant No.2462024XKBH006)+2 种基金the China Postdoctoral Science Foundation(Grant No.2024M753615)the Major Scientific Research Instrument Development Program of National Natural Science Foundation of China(Grant No.52227804)the Youth Science Foundation Program of National Natural Science Foundation of China(Grant No.52404012).
文摘The gas kick represents a major risk in deepwater oil and gas exploration.Understanding the dynamics of gas kick evolution and the associated pressure response characteristics is critical for effective well control.In this paper,we introduce a transient wellbore multiphase flow model specifically developed to simulate gas kick in deepwater dual-gradient drilling,incorporating a downhole separator.The model accounts for the variable mass flow within the annulus and heat exchange between the annular fluid and the formation.Using this model,we analyzed the multiphase flow and thermodynamic behavior during the gas kick.Simulation results reveal a progressive increase in bottom-hole temperature,underscoring its potential as a key indicator for gas kick early detection.Additionally,variable gradient parameters affect not only the annular equivalent circulating density(ECD)profile but also the evolution of the gas kick.The inclusion of a downhole separator alters the annular ECD profile,creating a“broken line”shape,which enhances adaptability to the multi-pressure systems typically encountered in deepwater forma-tion.By adjusting factors such as hollow sphere concentration,separator position,and separation effi-ciency,the annular ECD profile can be effectively customized.This study provides important theoretical insights and practical applications for utilizing dual-gradient drilling technology to address challenges in deepwater formation drilling.
基金funded by the National Natural Science Foundation of China(grant nos.52475084 and 52375076)the Postdoctoral Fellowship Program of CPSF(grant no.GZC20230202).
文摘Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplitude modulation(ISAM)based on sparse feature adaptive convolution(SFAC)is proposed to enhance the fault features under variable speed conditions.First,an optimal bi-damped wavelet construction method is proposed to learn signal impulse features,which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization.Second,a convolutional basis pursuit denoising model based on an optimal bi-damped wavelet is proposed for resolving sparse impulses.A model regularization parameter selection method based on weighted fault characteristic amplitude ratio assistance is proposed.Then,an ISAM method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal.Finally,the type of variable speed faults is determined by order spectrum analysis.Various experimental results,such as spectral amplitude modulation and Morlet wavelet matching,verify the effectiveness and advantages of the ISAM-SFAC method.
基金supported by the National Key Research and Development Program of China(No.SQ2021YFF0600011)。
文摘The MIG welding of in-situ generated nano-Al_(2)O_(3)powder metallurgy 7A52(PM 7A52)aluminum alloy was investigated.The microstructure was characterized using EBSD and TEM,while macrotexture and internal residual stresses were analyzed with a self-developed SWXRD technique.The results revealed that PM 7A52 aluminum alloy effectively reduced the grain size,dislocation density,and texture strength in the post-weld microstructure.Furthermore,the residual stress in the weld zone(WZ)of PM 7A52 aluminum alloy was reduced by 38 MPa compared to that of the conventional melt-cast 7A52(CM 7A52)aluminum alloy.Notably,the tensile strength and elongation of welded joints in PM 7A52 aluminum alloy were increased by approximately 15%and 26%,respectively.The improvement in joint tensile strength was primarily attributed to grain boundary strengthening and dispersion strengthening caused byγ-Al_(2)O_(3)particles entering the WZ.
文摘In the present study,the mechanical and ballistic properties of friction stir welded(FSW)aluminum alloy(AA5754)samples were investigated,both untreated and cryogenically treated,when impacted by a 7.62 mm armour-piercing(AP)bullet at an impact velocity of 682±20 m/s.The FSW technique was used to prepare the welded samples for AA5754,with an axial force of 7 kN,a feed rate of 20 mm/min,and a speed of 1200 rpm.The cryogenic treatments performed after welding,including deep cryogenic treatment(DCT)at196℃ and shallow cryogenic treatment(SCT)at80℃,for 6 and 72 h,respectively.The microstructure and mechanical characteristics of cryogenically treated and untreated joints were examined.The cryogenic treatment refined the grain size(1.05 μm)and enhanced the microhardness(93 Hv).Moreover,DCT-FSW significantly improved the tensile strength(13.93%)and impact strength(8.45%)compared to untreated FSW sample.Additionally,in untreated FSW samples,the fracture behaviour varied:the impact fracture mode primarily exhibited ductile failure,while the tensile fracture exhibited a mixed fracture mode.In contrast,the tensile and impact fracture modes of the DCT-FSWwere dominated by a ductile failure mode.The DCT-FSW target demonstrated a lower depth of penetration(DOP)of 31 mm compared to the SCT-FSWand untreated FSW targets.Post-ballistic SEM analysis in the crater region of all three zones revealed the formation of frictional grooves,small cracks,and adiabatic shear bands(ASBs).
文摘A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.
文摘The article"Assessment of superior mesenteric vascular flow quantitation in children using four-dimensional flow magnetic resonance imaging"suggests to use of four-dimensional(4D)flow magnetic resonance imaging(MRI)which is also to measure the blood flow in the superior mesenteric vein(SMV)in pediatric patients over the traditional method.The study focuses on assessing the potential of SMV and superior mesenteric artery(SMA)flow quantification in children utilizing 4D flow MRI.It included 9 pediatric patients aged 18 years and below where 5 were male and 4 were female patients,on whom magnetic resonance enterorrhaphy(MRE)with 4D flow MRI protocol was used.Statistical analysis was performed using MedCalc.Measurements of SMV and SMA between two readers were calculated using Bland-Altman analysis.The results stated that six patients showed no MRE evidence of active inflammatory bowel disease,two patients showed unmarkable bowel appearance on MRI and one patient showed normal MRE without endoscopy performed at the same timeframe.The study utilized available 4D flow MRI sequences in this study aiming to show the feasibility of 4D flow quantitation of SMA and SMV flow in pediatric patients.The study also discovered good agreement for both peak velocity and peak speed measurements of SMA and SMV.
文摘This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex,nonlinear natures.The Sperm Swarm Optimization(SSO)algorithm,which mimics the sperm’s movement to reach an egg,is one such technique.To improve SSO,researchers combined it with three strategies:opposition-based learning(OBL),Cauchy mutation(CM),and position clamping.OBL introduces diversity to SSO by exploring opposite solutions,speeding up convergence.CM enhances both exploration and exploitation capabilities throughout the optimization process.This combined approach,RSSO,has been rigorously tested on standard benchmark functions,real-world engineering problems,and through statistical analysis(Wilcoxon test).The results demonstrate that RSSO significantly outperforms other optimization algorithms,achieving faster convergence and better solutions.The paper details the RSSO algorithm,discusses its implementation,and presents comparative results that validate its effectiveness in solving complex engineering design challenges.
文摘Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing and quadruplexing Substrate-Integrated Waveguide (SIW) cavities. The diplexing structure incorporates two V-shaped slots, while the quadruplexing structure advances this concept by combining the slots to form a cross-shaped configuration within the cavity. The widths and lengths of the slots are carefully tuned to achieve variations in the respective operating frequencies without affecting the others. The proposed diplexing antenna resonates at 8.48 and 9.2 GHz, with a frequency ratio of 1.08, while the quadruplexing antenna operates at 6.9, 7.1, 7.48, and 8.2GHz. Both designs exhibit isolation levels well below –20dB and achieve a simulated peak gain of 5.6 dBi at the highest frequency, with a compact cavity area of 0.56 λg^(2). The proposed antennas operate within the NR bands (n12, n18, n26), making them suitable for modern high-speed wireless communication systems. Moreover, the properties like multiband operation, compactness, high isolation, low loss, and low interference make the antenna favorable for the high-speed railway communication systems.
基金funded by the Project of the Hubei Provincial Department of Science and Technology(Grant No.2022CFB957)the Project of Hubei Engineering University of Teaching Research(Grant No.JY2024032)+1 种基金Ministry of Education University-Industry Cooperation Collaborative Education Project(Grant No.220903584161245)College Students’Innovation and Entrepreneurship Training Program(Grant Nos.DC2024031,DC2024032).
文摘The behavior of single-phase flow and conjugate heat transfer in micro-channel heat sinks(MCHS)subjected to auniform heat flux is investigated by means of numerical simulations.Various geometrical configurations areexamined,particularly,the combinations of rectangular solid and perforated blocks,used to create a disturbancein the flow.The analysis focuses on several key aspects and related metrics,including the temperature distribution,the mean Fanning friction factor,the pressure drop,the Nusselt number,and the overall heat transfer coefficientacross a range of Reynolds numbers(80–870).It is shown that the introduction of such blocks significantlyenhances the heat transfer performances of the MCHS compared to the straight-through flow channel.Specifically,a case is found where the Nusselt number increases by 2.3 times relative to the reference case.The integrationof perforated blocks facilitates the generation of vorticity within the channel,promoting the mixing of coldand hot fluids.Notably,MCHS incorporating perforated rectangular blocks exhibit more pronounced heat transferbenefits at Reynolds numbers smaller than 400.
文摘Duplicate bug reporting is a critical problem in the software repositories’mining area.Duplicate bug reports can lead to redundant efforts,wasted resources,and delayed software releases.Thus,their accurate identification is essential for streamlining the bug triage process mining area.Several researchers have explored classical information retrieval,natural language processing,text and data mining,and machine learning approaches.The emergence of large language models(LLMs)(ChatGPT and Huggingface)has presented a new line of models for semantic textual similarity(STS).Although LLMs have shown remarkable advancements,there remains a need for longitudinal studies to determine whether performance improvements are due to the scale of the models or the unique embeddings they produce compared to classical encoding models.This study systematically investigates this issue by comparing classical word embedding techniques against LLM-based embeddings for duplicate bug detection.In this study,we have proposed an amalgamation of models to detect duplicate bug reports using textual and non-textual information about bug reports.The empirical evaluation has been performed on the open-source datasets and evaluated based on established metrics using the mean reciprocal rank(MRR),mean average precision(MAP),and recall rate.The experimental results have shown that combined LLMs can outperform(recall-rate@k 68%–74%)other individual=models for duplicate bug detection.These findings highlight the effectiveness of amalgamating multiple techniques in improving the duplicate bug report detection accuracy.
文摘Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.
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supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.
