Raw water temperature can fluctuate significantly throughout the year,with peaks above 30℃in summer and below 15℃in winter.Traditional desalination systems(e.g.,reverse osmosis,RO)face challenges under these varying...Raw water temperature can fluctuate significantly throughout the year,with peaks above 30℃in summer and below 15℃in winter.Traditional desalination systems(e.g.,reverse osmosis,RO)face challenges under these varying temperature conditions.Specifically,while the RO system performs well under high temperatures,its efficiency decreases sharply at lower temperatures.Membrane capacitive deionization(MCDI)is considered as an emergent and promising technology for brackish water desalination.While plenty of studies have been devoted to investigating the impacts of raw water properties(e.g.,salinity,coexisting ions,and natural organic matter)on MCDI performance,the role of water temperatures during the desalination remains under-explored.In this study,we first tested and determined the optimized MCDI operation parameters,such as the cell voltage and feedwater flow rate.Key findings showed that MCDI’s salt removal efficiency remains unaffected by feedwater temperature fluctuations.However,as feedwater temperature increases from 15℃to 40℃,the specific energy consumption for desalination slightly rises by 16.3%,and current efficiency drops by 14.1%.Compared to RO systems,the resilience of MCDI to temperature fluctuations makes it a preferable choice for brackish water treatment in areas with a large temperature difference.展开更多
Using multi-directional forging temperature as the independent variable and adopting the dual-mode phase field crystal model,the nucleation modes,reaction mechanisms,and interactions between grain boundaries and dislo...Using multi-directional forging temperature as the independent variable and adopting the dual-mode phase field crystal model,the nucleation modes,reaction mechanisms,and interactions between grain boundaries and dislocations at different temperatures were investigated.Results show that a mapping relationship between process parameters and grain refinement/coarsening is established,and the optimal processing temperature coefficient is 0.23.Compared with the cases with processing temperature coefficient of 0.19,0.20,0.21,0.25,and 0.27,the refinement effect increases by 256.0%,146.0%,113.0%,6.7%,and 52.4%,respectively.Excessively high temperatures lead to grain coarsening due to dislocation annihilation,and the application of strain can reduce the actual melting point of materials.Even if the processing temperature does not exceed the theoretical melting point,remelting and crystallization may still occur,resulting in an overburning phenomenon that reduces internal defects and increases overall grain size.This research is of great significance for the actual forging process design.展开更多
To address the challenges of complex metallic film coating processes and low integration in single-parameter detection for existing photonic crystal fiber surface plasmon resonance(PCF-SPR)sensors,a dual-parameter sen...To address the challenges of complex metallic film coating processes and low integration in single-parameter detection for existing photonic crystal fiber surface plasmon resonance(PCF-SPR)sensors,a dual-parameter sensor based on gold nanowire-integrated bias-core PCF-SPR is proposed.Unlike conventional in-hole coatings or metallic film structures,the gold nanowires are directly attached to the fiber cladding via chemical vapor deposition(CVD),eliminating uneven coating issues and significantly simplifying fabrica-tion.By optimizing the asymmetric bias-core fiber structure and leveraging the strong localized field en-hancement of gold nanowires,the sensor achieves high-sensitivity synchronous detection of temperature(25−60℃)and refractive index(1.31−1.40)in dual-polarization modes.The simulation results demonstrate that the x-polarization mode can achieve 1.31−1.40 refractive index detection with maximum wavelength sensitivity and amplitude sensitivity of 14800 nm/RIU and−1724.25 RIU^(−1),and maximum refractive index resolution of 6.75×10^(−6)RIU.The y-polarization mode achieves refractive index detection range of 1.34−1.40,and the maximum wavelength sensitivity and amplitude sensitivity are 28400 nm/RIU and−1298.93 RIU^(−1),and the maximum refractive index resolution is 3.52×10^(−6)RIU.For temperature sensing,the sensor exhibits a wavelength sensitivity of 7.8 nm/℃and a high resolution of 1.38×10^(−6)℃over the range of 25−60℃.This design synergizes gold nanowires and the bias-core architecture to simplify fabrication while enabling multi-parameter detection.The proposed sensor offers new insights for integrated applications in biochemical mon-itoring,environmental sensing,and related fields.展开更多
The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localiza...The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
Platinum group metals have high melting points,strong corrosion resistance,stable chemical properties,and low oxygen permeability in high-temperature oxygen-containing environments.As thermal protective coating materi...Platinum group metals have high melting points,strong corrosion resistance,stable chemical properties,and low oxygen permeability in high-temperature oxygen-containing environments.As thermal protective coating materials,they have gained essential applications in the aerospace field and have excellent prospects for application in frontier military fields,such as protecting hot-end components of hypersonic aircraft.This research reviewed the latest research progress of platinum group metal coatings with hightemperature oxidation resistance,including coating preparation techniques,oxidation failure,and alloying modification.The leading preparation techniques of current platinum group metal coatings were discussed,as well as the advantages and disadvantages of various existing preparation techniques.Besides,the intrinsic properties,failure forms,and failure mechanisms of coatings of single platinum group metal in high-temperature oxygen-containing environments were analyzed.On this basis,the necessity,main methods,and main achievements of alloying modification of platinum group metals were summarized.Finally,the future development of platinum group coatings with high-temperature oxidation resistance was discussed and prospected.展开更多
Lin Wei is a hiking enthusiast.At six o'clock on the last Saturday morning,the temperature at the foot of the mountain was only 2℃,so she put on her thickest fleece jacket.However,after only half an hour of climb...Lin Wei is a hiking enthusiast.At six o'clock on the last Saturday morning,the temperature at the foot of the mountain was only 2℃,so she put on her thickest fleece jacket.However,after only half an hour of climbing,the heat left her drenched in sweat,making her feel very cold.By midday,the temperature was approaching 20℃,and her heavy jacket had to be tied around her waist,becoming a burden during her hike.This outdoor adventure allowed her to appreciate the beautiful scenery,but also subjected her to repeated changes in temperature.展开更多
Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often ...Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often faces a scarcity of labelled data.To address this challenge,we propose a novel semi-supervised anomaly detection method,DASAD(Deviation-Guided Attention for Semi-Supervised Anomaly Detection),which integrates deviation-guided attention with contrastive regularisation to reduce the unreliability of pseudo-labels.Specifically,a deviation-guided attention mechanism is designed to combine three types of deviations:latent embeddings,residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively,thereby enhancing the credibility of pseudo-labels for unlabelled samples.Furthermore,a class-asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies.Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state-of-the-art methods and exhibits strong generalisation across 6 anomaly detection domains.展开更多
The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly ...The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly detection.Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations,improving energy efficiency,and supporting grid reliability.This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems.Expanding upon previous benchmark frameworks,we introduce additional features such as oil price indices and solar cycle indicators,including sunset and sunrise times,to enhance the contextual understanding of consumption patterns.Our comparative modeling approach encompasses an extensive suite of algorithms,including KNeighborsUnif,KNeighborsDist,LightGBMXT,LightGBM,RandomForestMSE,CatBoost,ExtraTreesMSE,NeuralNetFastAI,XGBoost,NeuralNetTorch,and LightGBMLarge.Data preprocessing includes rigorous handling of missing values and normalization,while feature engineering focuses on temporal,environmental,and value-change attributes.The models are evaluated on a comprehensive dataset of smart meter readings,with performance assessed using metrics such as the Area Under the Receiver Operating Characteristic Curve(AUC-ROC).The results demonstrate that the integration of diverse exogenous variables and a hybrid ensemble of traditional tree-based and neural network models can significantly improve anomaly detection performance.This work provides new insights into the design of robust,scalable,and generalizable frameworks for energy anomaly detection in complex,real-world settings.展开更多
As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where...As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where traffic data is collected and analyzed in plaintext.This assumption introduces inherent privacy risks,as privacy-sensitive information may be exposed if the server is compromised or misused.To address this limitation,privacy-preserving anomaly detection approaches have been actively studied,enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data.While these approaches offer strong confidentiality guarantees,they suffer from significant drawbacks,including substantial computational overhead,high latency,and degraded detection accuracy.To overcome these limitations,we propose a privacy-aware anomaly detection(PAAD)model that adaptively applies homomorphic encryption based on the privacy sensitivity of incoming traffic.Instead of encrypting all data indiscriminately,PAAD dynamically determines whether traffic should be processed in plaintext or ciphertext and performs homomorphic inference only for privacy-sensitive data.This selective encryption strategy effectively balances privacy protection and system efficiency.Extensive experiments conducted under diverse network environments demonstrate that the proposed PAAD model significantly outperforms conventional anomaly detection models.In particular,PAAD improves detection accuracy by up to 73%,reduces latency by up to 8.6 times,and achieves negligible information leakage,highlighting its practicality for real-world privacy-sensitive network monitoring scenarios.展开更多
In recent years,the research on superconductivity in one-dimensional(1D)materials has been attracting increasing attention due to its potential applications in low-dimensional nanodevices.However,the critical temperat...In recent years,the research on superconductivity in one-dimensional(1D)materials has been attracting increasing attention due to its potential applications in low-dimensional nanodevices.However,the critical temperature(T_(c))of 1D superconductors is low.In this work,we theoretically investigate the possible high T_(c) superconductivity of(5,5)carbon nanotube(CNT).The pristine(5,5)CNT is a Dirac semimetal and can be modulated into a semiconductor by full hydrogenation.Interestingly,by further hole doping,it can be regulated into a metallic state with the sp^(3)-hybridized σ electrons metalized,and a giant Kohn anomaly appears in the optical phonons.The two factors together enhance the electron–phonon coupling,and lead to high-T_(c) superconductivity.When the hole doping concentration of hydrogenated-(5,5)CNT is 2.5 hole/cell,the calculated T_(c) is 82.3 K,exceeding the boiling point of liquid nitrogen.Therefore,the predicted hole-doped hydrogenated-(5,5)CNT provides a new platform for 1D high-T_(c) superconductivity and may have potential applications in 1D nanodevices.展开更多
The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attentio...The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learni...Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
The technology of locating magnetic anomaly targets via geomagnetic eld measurements has been increasingly widely applied,with multiple magnetic anomaly target localization emerging as a critical research direction.Ho...The technology of locating magnetic anomaly targets via geomagnetic eld measurements has been increasingly widely applied,with multiple magnetic anomaly target localization emerging as a critical research direction.However,when two magnetic anomaly targets are horizontally close but vertically separated,traditional clustering-based localization methods tend to omit the deeper target.To address this issue,we propose an improved clustering-based localization method for multiple magnetic anomaly targets,which integrates two core innovations:the introduction of a reference target to establish a benchmark for normal magnetic moment distribution,and the utilization of spatial distribution characteristics of magnetic moment estimates to judge the presence of omitted targets.Simulation results demonstrate that the proposed method not only achieves accurate localization of conventional targets but also eectively identies the omission of deeper targets,providing a reliable basis for determining whether supplementary localization steps are required.展开更多
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ...With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.展开更多
Maintaining constant mold level variations during the continuous casting process is essential to guarantee the effectiveness and quality of steel production. An unsupervised deep learning-based mold level anomaly dete...Maintaining constant mold level variations during the continuous casting process is essential to guarantee the effectiveness and quality of steel production. An unsupervised deep learning-based mold level anomaly detection (MLAD) model for real-time monitoring of mold level fluctuations under varying operating conditions was proposed. The MLAD framework employs a two-stage encoder-decoder structure with adversarial training to accurately reconstruct time-series mold level data. In the first stage, the model learns long-term trends by reconstructing input windows, while in the second stage, it employs reconstruction errors as focus scores to capture short-term anomaly patterns. A transformer-based architecture, incorporating multi-head attention mechanisms and positional encoding, enables MLAD to capture both local and global temporal dependencies. In addition, a novel multi-threshold strategy, based on extreme value theory, is implemented to enhance the model’s ability and to adapt to varying operating conditions, including startup, steady-state, and shutdown phases. The model was validated with over 240-h real data from a steel factory. The results demonstrate its superior performance in anomaly detection compared to popular methods, with a precision of 0.9937, recall of 0.9932, and a low false alarm rate of 0.0038. MLAD represents a significant advancement in the detection of nonlinear and nonstationary anomalies in industrial processes, offering an efficient solution for smart manufacturing systems. Therefore, the established model could be used for online anomaly detection of mold level with real-time data.展开更多
Spikelet filling characteristics in early-season rice in southern China may be distinctive due to its exposure to high temperatures during the ripening period.However,limited information is currently available on thes...Spikelet filling characteristics in early-season rice in southern China may be distinctive due to its exposure to high temperatures during the ripening period.However,limited information is currently available on these characteristics.This study aimed to characterize spikelet filling in early-season rice and identify the key factors contributing to its improvement.Field experiments were conducted over two years(2021 and 2022)to mainly investigate the proportions of fully-filled,partially-filled,and empty spikelets,along with the biomass-fertilized spikelet ratio and harvest index,in 11 early-season rice varieties.The results revealed significant varietal variation in spikelet filling,with the proportion of fully-filled spikelets ranging from 60.6%to 81.1%in 2021 and from 66.3%to 79.2%in 2022.Among the 11 varieties,Liangyou 42,Lingliangyou 942,and Liangyou 287 exhibited relatively superior performance in spikelet filling.Linear regression revealed that,although a significant negative relationship existed between the proportion of fully-filled spikelets and both partially-filled and empty spikelets,the relationship with partially-filled spikelets was stronger.Additionally,the proportion of fully-filled spikelets showed a significant positive relationship with the harvest index but not with the biomass-fertilized spikelet ratio.These findings indicate that increasing the harvest index and reducing the occurrence of partially-filled grains are essential strategies for improving spikelet filling in early-season rice.展开更多
With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52370090,52300016)China Postdoctoral Science Foundation(Nos.2023M733379,2024M753122).
文摘Raw water temperature can fluctuate significantly throughout the year,with peaks above 30℃in summer and below 15℃in winter.Traditional desalination systems(e.g.,reverse osmosis,RO)face challenges under these varying temperature conditions.Specifically,while the RO system performs well under high temperatures,its efficiency decreases sharply at lower temperatures.Membrane capacitive deionization(MCDI)is considered as an emergent and promising technology for brackish water desalination.While plenty of studies have been devoted to investigating the impacts of raw water properties(e.g.,salinity,coexisting ions,and natural organic matter)on MCDI performance,the role of water temperatures during the desalination remains under-explored.In this study,we first tested and determined the optimized MCDI operation parameters,such as the cell voltage and feedwater flow rate.Key findings showed that MCDI’s salt removal efficiency remains unaffected by feedwater temperature fluctuations.However,as feedwater temperature increases from 15℃to 40℃,the specific energy consumption for desalination slightly rises by 16.3%,and current efficiency drops by 14.1%.Compared to RO systems,the resilience of MCDI to temperature fluctuations makes it a preferable choice for brackish water treatment in areas with a large temperature difference.
基金National Natural Science Foundation of China(52375394,52275390,U23A20628,52305429)Major Project of Science and Technology in Shanxi(202301050201004)Natural Science Foundation of Shanxi Province(202403021222132)。
文摘Using multi-directional forging temperature as the independent variable and adopting the dual-mode phase field crystal model,the nucleation modes,reaction mechanisms,and interactions between grain boundaries and dislocations at different temperatures were investigated.Results show that a mapping relationship between process parameters and grain refinement/coarsening is established,and the optimal processing temperature coefficient is 0.23.Compared with the cases with processing temperature coefficient of 0.19,0.20,0.21,0.25,and 0.27,the refinement effect increases by 256.0%,146.0%,113.0%,6.7%,and 52.4%,respectively.Excessively high temperatures lead to grain coarsening due to dislocation annihilation,and the application of strain can reduce the actual melting point of materials.Even if the processing temperature does not exceed the theoretical melting point,remelting and crystallization may still occur,resulting in an overburning phenomenon that reduces internal defects and increases overall grain size.This research is of great significance for the actual forging process design.
文摘To address the challenges of complex metallic film coating processes and low integration in single-parameter detection for existing photonic crystal fiber surface plasmon resonance(PCF-SPR)sensors,a dual-parameter sensor based on gold nanowire-integrated bias-core PCF-SPR is proposed.Unlike conventional in-hole coatings or metallic film structures,the gold nanowires are directly attached to the fiber cladding via chemical vapor deposition(CVD),eliminating uneven coating issues and significantly simplifying fabrica-tion.By optimizing the asymmetric bias-core fiber structure and leveraging the strong localized field en-hancement of gold nanowires,the sensor achieves high-sensitivity synchronous detection of temperature(25−60℃)and refractive index(1.31−1.40)in dual-polarization modes.The simulation results demonstrate that the x-polarization mode can achieve 1.31−1.40 refractive index detection with maximum wavelength sensitivity and amplitude sensitivity of 14800 nm/RIU and−1724.25 RIU^(−1),and maximum refractive index resolution of 6.75×10^(−6)RIU.The y-polarization mode achieves refractive index detection range of 1.34−1.40,and the maximum wavelength sensitivity and amplitude sensitivity are 28400 nm/RIU and−1298.93 RIU^(−1),and the maximum refractive index resolution is 3.52×10^(−6)RIU.For temperature sensing,the sensor exhibits a wavelength sensitivity of 7.8 nm/℃and a high resolution of 1.38×10^(−6)℃over the range of 25−60℃.This design synergizes gold nanowires and the bias-core architecture to simplify fabrication while enabling multi-parameter detection.The proposed sensor offers new insights for integrated applications in biochemical mon-itoring,environmental sensing,and related fields.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No. HC-CN-20221107001。
文摘The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
文摘Platinum group metals have high melting points,strong corrosion resistance,stable chemical properties,and low oxygen permeability in high-temperature oxygen-containing environments.As thermal protective coating materials,they have gained essential applications in the aerospace field and have excellent prospects for application in frontier military fields,such as protecting hot-end components of hypersonic aircraft.This research reviewed the latest research progress of platinum group metal coatings with hightemperature oxidation resistance,including coating preparation techniques,oxidation failure,and alloying modification.The leading preparation techniques of current platinum group metal coatings were discussed,as well as the advantages and disadvantages of various existing preparation techniques.Besides,the intrinsic properties,failure forms,and failure mechanisms of coatings of single platinum group metal in high-temperature oxygen-containing environments were analyzed.On this basis,the necessity,main methods,and main achievements of alloying modification of platinum group metals were summarized.Finally,the future development of platinum group coatings with high-temperature oxidation resistance was discussed and prospected.
文摘Lin Wei is a hiking enthusiast.At six o'clock on the last Saturday morning,the temperature at the foot of the mountain was only 2℃,so she put on her thickest fleece jacket.However,after only half an hour of climbing,the heat left her drenched in sweat,making her feel very cold.By midday,the temperature was approaching 20℃,and her heavy jacket had to be tied around her waist,becoming a burden during her hike.This outdoor adventure allowed her to appreciate the beautiful scenery,but also subjected her to repeated changes in temperature.
基金supported by the National Natural Science Foundation of China under Grant U24A20279.
文摘Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often faces a scarcity of labelled data.To address this challenge,we propose a novel semi-supervised anomaly detection method,DASAD(Deviation-Guided Attention for Semi-Supervised Anomaly Detection),which integrates deviation-guided attention with contrastive regularisation to reduce the unreliability of pseudo-labels.Specifically,a deviation-guided attention mechanism is designed to combine three types of deviations:latent embeddings,residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively,thereby enhancing the credibility of pseudo-labels for unlabelled samples.Furthermore,a class-asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies.Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state-of-the-art methods and exhibits strong generalisation across 6 anomaly detection domains.
文摘The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data,creating new opportunities and challenges for energy anomaly detection.Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations,improving energy efficiency,and supporting grid reliability.This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems.Expanding upon previous benchmark frameworks,we introduce additional features such as oil price indices and solar cycle indicators,including sunset and sunrise times,to enhance the contextual understanding of consumption patterns.Our comparative modeling approach encompasses an extensive suite of algorithms,including KNeighborsUnif,KNeighborsDist,LightGBMXT,LightGBM,RandomForestMSE,CatBoost,ExtraTreesMSE,NeuralNetFastAI,XGBoost,NeuralNetTorch,and LightGBMLarge.Data preprocessing includes rigorous handling of missing values and normalization,while feature engineering focuses on temporal,environmental,and value-change attributes.The models are evaluated on a comprehensive dataset of smart meter readings,with performance assessed using metrics such as the Area Under the Receiver Operating Characteristic Curve(AUC-ROC).The results demonstrate that the integration of diverse exogenous variables and a hybrid ensemble of traditional tree-based and neural network models can significantly improve anomaly detection performance.This work provides new insights into the design of robust,scalable,and generalizable frameworks for energy anomaly detection in complex,real-world settings.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry[grant number RS-2024-00415520]supervised by the Korea Institute for Advancement of Technology(KIAT)Ministry of Science and ICT(MSIT)under the ICAN(ICT Challenge and Advanced Network of HRD)program[grant number IITP-2022-RS-2022-00156310]+1 种基金National Research Foundation of Korea(NRF)grant[RS-2025-00518150]the Information Security Core Technology Development program[grant number RS-2024-00437252]supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where traffic data is collected and analyzed in plaintext.This assumption introduces inherent privacy risks,as privacy-sensitive information may be exposed if the server is compromised or misused.To address this limitation,privacy-preserving anomaly detection approaches have been actively studied,enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data.While these approaches offer strong confidentiality guarantees,they suffer from significant drawbacks,including substantial computational overhead,high latency,and degraded detection accuracy.To overcome these limitations,we propose a privacy-aware anomaly detection(PAAD)model that adaptively applies homomorphic encryption based on the privacy sensitivity of incoming traffic.Instead of encrypting all data indiscriminately,PAAD dynamically determines whether traffic should be processed in plaintext or ciphertext and performs homomorphic inference only for privacy-sensitive data.This selective encryption strategy effectively balances privacy protection and system efficiency.Extensive experiments conducted under diverse network environments demonstrate that the proposed PAAD model significantly outperforms conventional anomaly detection models.In particular,PAAD improves detection accuracy by up to 73%,reduces latency by up to 8.6 times,and achieves negligible information leakage,highlighting its practicality for real-world privacy-sensitive network monitoring scenarios.
基金supported by the National Natural Science Foundation of China (Grant Nos.12074213 and 11574108)the Major Basic Program of Natural Science Foundation of Shandong Province (Grant No.ZR2021ZD01)the Natural Science Foundation of Shandong Province (Grant No.ZR2023MA082)。
文摘In recent years,the research on superconductivity in one-dimensional(1D)materials has been attracting increasing attention due to its potential applications in low-dimensional nanodevices.However,the critical temperature(T_(c))of 1D superconductors is low.In this work,we theoretically investigate the possible high T_(c) superconductivity of(5,5)carbon nanotube(CNT).The pristine(5,5)CNT is a Dirac semimetal and can be modulated into a semiconductor by full hydrogenation.Interestingly,by further hole doping,it can be regulated into a metallic state with the sp^(3)-hybridized σ electrons metalized,and a giant Kohn anomaly appears in the optical phonons.The two factors together enhance the electron–phonon coupling,and lead to high-T_(c) superconductivity.When the hole doping concentration of hydrogenated-(5,5)CNT is 2.5 hole/cell,the calculated T_(c) is 82.3 K,exceeding the boiling point of liquid nitrogen.Therefore,the predicted hole-doped hydrogenated-(5,5)CNT provides a new platform for 1D high-T_(c) superconductivity and may have potential applications in 1D nanodevices.
文摘The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported in part by the National Natural Science Foundation of China(62472146)the Key Technologies Research Development Joint Foundation of Henan Province of China(225101610001)。
文摘Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
基金funded by the Youth Project of Basic Research Plan for Natural Sciences in Shaanxi Province,grant number 2024JC-YBQN-0253.
文摘The technology of locating magnetic anomaly targets via geomagnetic eld measurements has been increasingly widely applied,with multiple magnetic anomaly target localization emerging as a critical research direction.However,when two magnetic anomaly targets are horizontally close but vertically separated,traditional clustering-based localization methods tend to omit the deeper target.To address this issue,we propose an improved clustering-based localization method for multiple magnetic anomaly targets,which integrates two core innovations:the introduction of a reference target to establish a benchmark for normal magnetic moment distribution,and the utilization of spatial distribution characteristics of magnetic moment estimates to judge the presence of omitted targets.Simulation results demonstrate that the proposed method not only achieves accurate localization of conventional targets but also eectively identies the omission of deeper targets,providing a reliable basis for determining whether supplementary localization steps are required.
基金National Natural Science Foundation of China(Grant No.62103434)National Science Fund for Distinguished Young Scholars(Grant No.62176263).
文摘With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.
基金supported by the Key Science and Technology Project of HBIS Materials Institute(No.HG2022328)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.311021013).
文摘Maintaining constant mold level variations during the continuous casting process is essential to guarantee the effectiveness and quality of steel production. An unsupervised deep learning-based mold level anomaly detection (MLAD) model for real-time monitoring of mold level fluctuations under varying operating conditions was proposed. The MLAD framework employs a two-stage encoder-decoder structure with adversarial training to accurately reconstruct time-series mold level data. In the first stage, the model learns long-term trends by reconstructing input windows, while in the second stage, it employs reconstruction errors as focus scores to capture short-term anomaly patterns. A transformer-based architecture, incorporating multi-head attention mechanisms and positional encoding, enables MLAD to capture both local and global temporal dependencies. In addition, a novel multi-threshold strategy, based on extreme value theory, is implemented to enhance the model’s ability and to adapt to varying operating conditions, including startup, steady-state, and shutdown phases. The model was validated with over 240-h real data from a steel factory. The results demonstrate its superior performance in anomaly detection compared to popular methods, with a precision of 0.9937, recall of 0.9932, and a low false alarm rate of 0.0038. MLAD represents a significant advancement in the detection of nonlinear and nonstationary anomalies in industrial processes, offering an efficient solution for smart manufacturing systems. Therefore, the established model could be used for online anomaly detection of mold level with real-time data.
基金funded by the Earmarked Fund for China Agriculture Research System,grant number CARS-01-33.
文摘Spikelet filling characteristics in early-season rice in southern China may be distinctive due to its exposure to high temperatures during the ripening period.However,limited information is currently available on these characteristics.This study aimed to characterize spikelet filling in early-season rice and identify the key factors contributing to its improvement.Field experiments were conducted over two years(2021 and 2022)to mainly investigate the proportions of fully-filled,partially-filled,and empty spikelets,along with the biomass-fertilized spikelet ratio and harvest index,in 11 early-season rice varieties.The results revealed significant varietal variation in spikelet filling,with the proportion of fully-filled spikelets ranging from 60.6%to 81.1%in 2021 and from 66.3%to 79.2%in 2022.Among the 11 varieties,Liangyou 42,Lingliangyou 942,and Liangyou 287 exhibited relatively superior performance in spikelet filling.Linear regression revealed that,although a significant negative relationship existed between the proportion of fully-filled spikelets and both partially-filled and empty spikelets,the relationship with partially-filled spikelets was stronger.Additionally,the proportion of fully-filled spikelets showed a significant positive relationship with the harvest index but not with the biomass-fertilized spikelet ratio.These findings indicate that increasing the harvest index and reducing the occurrence of partially-filled grains are essential strategies for improving spikelet filling in early-season rice.
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
