With the urgently increasing demand for high-speed and large-capacity communication trans-mission,there remains a critical need for tunable terahertz(THz)devices with multi-channel in 5G/6G communication systems.A mag...With the urgently increasing demand for high-speed and large-capacity communication trans-mission,there remains a critical need for tunable terahertz(THz)devices with multi-channel in 5G/6G communication systems.A magnetic phase-coding meta-atom(MPM)is formed by the heterogeneous integration of La:YIG magneto-optical(MO)materials and Si microstructures.The MPM couples the magnetic induction phase of spin states with the propagation phase and can simultaneously satisfy the required output phase for dual frequencies under various external magnetic fields to realize the dynamic beam steering among multiple channels at 0.25 and 0.5 THz.The energy ratio of the target direction can reach 96.5%,and the nonreciprocal one-way transmission with a max isolation of 29.8 dB is realized due to the nonreciprocal phase shift of the MO layer.This nonreciprocal mechanism of magnetic induction reshaping of wavefront significantly holds promise for advancing integrated multi-functional THz devices with the characteristics of low-crosstalk,multi-channel,and multi-frequency,and has great potential to promote the development of THz large-capacity and high-speed communication.展开更多
This paper presents a compact broadband antenna that overcomes bandwidth limitations in a diamond nitrogenvacancy(NV)center-based quantum magnetic sensor.Conventional antennas struggle to achieve both broadband operat...This paper presents a compact broadband antenna that overcomes bandwidth limitations in a diamond nitrogenvacancy(NV)center-based quantum magnetic sensor.Conventional antennas struggle to achieve both broadband operation and compact integration,restricting the sensitivity and dynamic range of the sensor.The broadband antenna based on a dualfrequency monopole structure achieves a bandwidth extension of 777 MHz at the Zeeman splitting frequency of 2.87 GHz,with the dual resonant points positioned near 2.87 GHz.Additionally,high-resolution imaging of the microwave magnetic field on the antenna surface was performed using a diamond optical fiber probe,which verified the dual-frequency design principle.Experimental results using the proposed antenna demonstrate the outstanding performance of the NV centerbased magnetic sensor:a sensitivity of 55 nT/Hz^(1/2)and a dynamic range of up to 54.0 dB.Compared to sensors using conventional antennas,the performance has been significantly improved.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi...Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.展开更多
The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack...The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
4-Chlorophenol (4-CP) solution was treated by dual-frequency ultrasound inconjunction with Fenton reagent, and obvious improvement in the 4-CP degradation rate was observedin this advanced oxidation process. Experimen...4-Chlorophenol (4-CP) solution was treated by dual-frequency ultrasound inconjunction with Fenton reagent, and obvious improvement in the 4-CP degradation rate was observedin this advanced oxidation process. Experimental results showed that ultrasonic intensity,saturating gas and pH value affected greatly the 4-CP removal rate. Among four different saturatinggases (Ar, O_2, air and N_2), 4-CP degradation with Ar-saturated solution was the best. However, inthe view of practical wastewater treatment, using oxygen as the saturating gas would be moreeconomical. The addition of Fenton reagent followed the first-order kinetics and increased the 4-CPdegradation rate. The 4-CP removal rate increased by around 126% within 15 min treatment. Thesynergetic effect of dual-frequency ultrasound with Fenton reagent on 4-CP degradation was obviouslyobserved.展开更多
A dual-frequency(105/140 GHz)MW-level continuous-wave gyrotron was developed for fusion application at Institute of Applied Electronics,China Academy of Engineering Physics.This gyrotron employs a cylindrical cavity w...A dual-frequency(105/140 GHz)MW-level continuous-wave gyrotron was developed for fusion application at Institute of Applied Electronics,China Academy of Engineering Physics.This gyrotron employs a cylindrical cavity working in the TE18,7 mode at 105 GHz and the TE24,9 mode at 140 GHz.A triode magnetron injection gun and a built-in quasi-optical mode converter were designed to operate at these two frequencies.For the proof-test phase,the gyrotron was equipped with a single-disk boron nitride window to achieve radio frequency output with a power of~500 k W for a short-pulse duration.In the preliminary short-pulse proof-test in the first quarter of2021,the dual-frequency gyrotron achieved output powers of 300 k W at 105 GHz and 540 k W at140 GHz,respectively,under 5 Hz 1 ms continuous pulse-burst operations.Power upgrade and pulse-width extension were hampered by the limitation of the high-voltage power supply and output window.This gyrotron design was preliminarily validated.展开更多
The wind retrieval performance of HY-2 A scanning scatterometer operating at Ku-band in HH and VV polarizations has been well evaluated in the wind speed range of 0–25 m s^-1.In order to obtain more accurate ocean wi...The wind retrieval performance of HY-2 A scanning scatterometer operating at Ku-band in HH and VV polarizations has been well evaluated in the wind speed range of 0–25 m s^-1.In order to obtain more accurate ocean wind field,a potential extension of dual-frequency(C-band and Ku-band)polarimetric measurements is investigated for both low and very high wind speeds,from 5 to 45 m s^-1.Based on the geophysical model functions of C-band and Ku-band,the simulation results show that the polarimetric measurements of Ku-band can improve the wind vector retrieval over the entire scatterometer swath,especially in nadir area,with the wind direction root-mean-square error(RMSE)less than 12?in the wind speed range of 5–25 m s^-1.Furthermore,the results also show that C-band cross-polarization plays a very important role in improving the wind speed retrieval,with the wind speed retrieval accuracy better than 2 m s^-1 for all wind conditions(0–45 m s^-1).For extreme winds,the C-band HH backscatter coefficients modeled by CMOD5.N(H)and the ocean co-polarization ratio model at large incidence are used to retrieve sea surface wind vector.This result reveals that there is a big decrease of wind direction retrieval RMSE for extreme wind fields,and the retrieved result of C-band HH polarization is nearly the same as that of C-band VV polarization for low-to-high wind speed(5–25 m s^-1).Thus,to improve the wind retrieval for all wind conditions,the dual-frequency polarimetric scatterometer with C-band and Ku-band horizontal polarization in inner beam,and C-band horizontal and Ku-band vertical polarization in outer beam,can be used to measure ocean winds.This study will contribute to the wind retrieval with merged satellites data and the future spaceborne scatterometer.展开更多
Based on the fundamental ideas concerning microwave attenuation in plasma, we obtain a new expression of transmission attenuation of microwaves as a function of the incident wave frequency. And with reasonable hypothe...Based on the fundamental ideas concerning microwave attenuation in plasma, we obtain a new expression of transmission attenuation of microwaves as a function of the incident wave frequency. And with reasonable hypothesis, analytical forms of the electron density and the electron-neutral collision frequency are derived from the equations of the transmission attenuation of microwaves at two near frequencies. This method gives an effective and easy approach to diagnose the unmagnetized plasma.展开更多
We study on reduced dynamic orbit determination using differenced phase in adjacent epochs for spacebome dual-frequency GPS. This method not only overcomes the shortcomings that the epoch-difference kinematic method c...We study on reduced dynamic orbit determination using differenced phase in adjacent epochs for spacebome dual-frequency GPS. This method not only overcomes the shortcomings that the epoch-difference kinematic method cannot be used when observation geometry is poor or observations are insufficient, but also avoids solving the ambiguity in the zero-difference reduced dynamic method. As the epoch-difference method is not sensitive to the impact of phase cycle slips, it can lower the difficulty of slip detection in phase observation preprocessing. In the solution strategies, we solve the high-dimensional matrix computation problems by decomposing the long observation arc into a number of short arcs. By gravity recovery and climate experiment （GRACE） satellite orbit determination and compared with GeoForschungsZentrum （GFZ） post science orbit, for epoch-difference reduced dynamic method, the root mean squares （RMSs） of radial, transverse and normal components are 1.92 cm, 3.83 cm and 3.80 cm, and the RMS in three dimensions is 5.76 cm. The solution＇s accuracy is comparable to the zero-difference reduced dynamic method.展开更多
The optical feedback characteristics in a Zeeman-birefringence dual-frequency laser are studied during the laser cavity tuning in three different kinds of optical feedback conditions： （i） only //-light is fed back;...The optical feedback characteristics in a Zeeman-birefringence dual-frequency laser are studied during the laser cavity tuning in three different kinds of optical feedback conditions： （i） only //-light is fed back; （ii） only ⊥-light is fed back; （iii） both lights are fed back. A compact displacement sensor is designed using the experimental result that there is a nearly 90 degrees phase delay between the two lights＇ cosine optical feedback signals when both lights are fed back into the laser cavity. The priority order that the two lights＇ intensity curves appear can be used for direction discrimination. The resolution of the displacement sensor is at least 79 rim, and the sensor can discriminate the target＇s moving direction easily.展开更多
基金supported by the National Natural Science Foun-dation of China(Grant Nos.62371258,62335012,62205160,and 62435010)the Tianjin Youth Science and Technology Talent Project(Grant No.QN20230227)+1 种基金the Natural Science Foundation of Tianjin(Grant No.24JCYBJC01860)the Fundamental Research Funds for the Central Universities,Nan-kai University(Grant No.075-63253215).
文摘With the urgently increasing demand for high-speed and large-capacity communication trans-mission,there remains a critical need for tunable terahertz(THz)devices with multi-channel in 5G/6G communication systems.A magnetic phase-coding meta-atom(MPM)is formed by the heterogeneous integration of La:YIG magneto-optical(MO)materials and Si microstructures.The MPM couples the magnetic induction phase of spin states with the propagation phase and can simultaneously satisfy the required output phase for dual frequencies under various external magnetic fields to realize the dynamic beam steering among multiple channels at 0.25 and 0.5 THz.The energy ratio of the target direction can reach 96.5%,and the nonreciprocal one-way transmission with a max isolation of 29.8 dB is realized due to the nonreciprocal phase shift of the MO layer.This nonreciprocal mechanism of magnetic induction reshaping of wavefront significantly holds promise for advancing integrated multi-functional THz devices with the characteristics of low-crosstalk,multi-channel,and multi-frequency,and has great potential to promote the development of THz large-capacity and high-speed communication.
基金Project supported by the National Key Research and Development Program of China(Grant No.2021YFB2012600)the Science and Technology Plan Project of the State Administration of Market Regulation,China(Grant No.2021MK039)the Suqian Talent Elite Program(Grant No.SQQN202414)。
文摘This paper presents a compact broadband antenna that overcomes bandwidth limitations in a diamond nitrogenvacancy(NV)center-based quantum magnetic sensor.Conventional antennas struggle to achieve both broadband operation and compact integration,restricting the sensitivity and dynamic range of the sensor.The broadband antenna based on a dualfrequency monopole structure achieves a bandwidth extension of 777 MHz at the Zeeman splitting frequency of 2.87 GHz,with the dual resonant points positioned near 2.87 GHz.Additionally,high-resolution imaging of the microwave magnetic field on the antenna surface was performed using a diamond optical fiber probe,which verified the dual-frequency design principle.Experimental results using the proposed antenna demonstrate the outstanding performance of the NV centerbased magnetic sensor:a sensitivity of 55 nT/Hz^(1/2)and a dynamic range of up to 54.0 dB.Compared to sensors using conventional antennas,the performance has been significantly improved.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金funded by University of Transport and Communications(UTC)under grant number T2025-CN-004.
文摘Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.
基金funded by the Science and Technology Project of State Grid Corporation of China(5108-202355437A-3-2-ZN).
文摘The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金Partly supported by the National Natural Science Foundation of China (No. 20176053)Academic Foundation of Zhejiang University of Technology (No. 20040004).
文摘4-Chlorophenol (4-CP) solution was treated by dual-frequency ultrasound inconjunction with Fenton reagent, and obvious improvement in the 4-CP degradation rate was observedin this advanced oxidation process. Experimental results showed that ultrasonic intensity,saturating gas and pH value affected greatly the 4-CP removal rate. Among four different saturatinggases (Ar, O_2, air and N_2), 4-CP degradation with Ar-saturated solution was the best. However, inthe view of practical wastewater treatment, using oxygen as the saturating gas would be moreeconomical. The addition of Fenton reagent followed the first-order kinetics and increased the 4-CPdegradation rate. The 4-CP removal rate increased by around 126% within 15 min treatment. Thesynergetic effect of dual-frequency ultrasound with Fenton reagent on 4-CP degradation was obviouslyobserved.
基金supported in part by NSAF(No.U1830201)in part by the State Administration of Science,Technology and Industry for Nation Defense of China,Technology Foundation Project(No.JSJL2019212B006)+1 种基金in part by the Academy Innovation Funder(No.CX2020038)in part by the National Defense Basic Scientific Research Program(No.2018212C015)。
文摘A dual-frequency(105/140 GHz)MW-level continuous-wave gyrotron was developed for fusion application at Institute of Applied Electronics,China Academy of Engineering Physics.This gyrotron employs a cylindrical cavity working in the TE18,7 mode at 105 GHz and the TE24,9 mode at 140 GHz.A triode magnetron injection gun and a built-in quasi-optical mode converter were designed to operate at these two frequencies.For the proof-test phase,the gyrotron was equipped with a single-disk boron nitride window to achieve radio frequency output with a power of~500 k W for a short-pulse duration.In the preliminary short-pulse proof-test in the first quarter of2021,the dual-frequency gyrotron achieved output powers of 300 k W at 105 GHz and 540 k W at140 GHz,respectively,under 5 Hz 1 ms continuous pulse-burst operations.Power upgrade and pulse-width extension were hampered by the limitation of the high-voltage power supply and output window.This gyrotron design was preliminarily validated.
基金supported by the National Key R&D Program of China (No. 2016YFC1401006)the National Natural Science Foundation of China (Nos. 51279186, 51479183 and 41676169)+2 种基金the National Program on Key Basic Research Project (No. 2011CB013704)the 111 Project (No. B14028)the Marine and Fishery Information Center Project of Jiangsu Province (No. SJC2014 110338)
文摘The wind retrieval performance of HY-2 A scanning scatterometer operating at Ku-band in HH and VV polarizations has been well evaluated in the wind speed range of 0–25 m s^-1.In order to obtain more accurate ocean wind field,a potential extension of dual-frequency(C-band and Ku-band)polarimetric measurements is investigated for both low and very high wind speeds,from 5 to 45 m s^-1.Based on the geophysical model functions of C-band and Ku-band,the simulation results show that the polarimetric measurements of Ku-band can improve the wind vector retrieval over the entire scatterometer swath,especially in nadir area,with the wind direction root-mean-square error(RMSE)less than 12?in the wind speed range of 5–25 m s^-1.Furthermore,the results also show that C-band cross-polarization plays a very important role in improving the wind speed retrieval,with the wind speed retrieval accuracy better than 2 m s^-1 for all wind conditions(0–45 m s^-1).For extreme winds,the C-band HH backscatter coefficients modeled by CMOD5.N(H)and the ocean co-polarization ratio model at large incidence are used to retrieve sea surface wind vector.This result reveals that there is a big decrease of wind direction retrieval RMSE for extreme wind fields,and the retrieved result of C-band HH polarization is nearly the same as that of C-band VV polarization for low-to-high wind speed(5–25 m s^-1).Thus,to improve the wind retrieval for all wind conditions,the dual-frequency polarimetric scatterometer with C-band and Ku-band horizontal polarization in inner beam,and C-band horizontal and Ku-band vertical polarization in outer beam,can be used to measure ocean winds.This study will contribute to the wind retrieval with merged satellites data and the future spaceborne scatterometer.
文摘Based on the fundamental ideas concerning microwave attenuation in plasma, we obtain a new expression of transmission attenuation of microwaves as a function of the incident wave frequency. And with reasonable hypothesis, analytical forms of the electron density and the electron-neutral collision frequency are derived from the equations of the transmission attenuation of microwaves at two near frequencies. This method gives an effective and easy approach to diagnose the unmagnetized plasma.
基金National Natural Science Foundation of China （61002033, 60902089） Open Research Fund of State Key Laboratory of Astronautic Dynamics （2011ADL-DW0103）
文摘We study on reduced dynamic orbit determination using differenced phase in adjacent epochs for spacebome dual-frequency GPS. This method not only overcomes the shortcomings that the epoch-difference kinematic method cannot be used when observation geometry is poor or observations are insufficient, but also avoids solving the ambiguity in the zero-difference reduced dynamic method. As the epoch-difference method is not sensitive to the impact of phase cycle slips, it can lower the difficulty of slip detection in phase observation preprocessing. In the solution strategies, we solve the high-dimensional matrix computation problems by decomposing the long observation arc into a number of short arcs. By gravity recovery and climate experiment （GRACE） satellite orbit determination and compared with GeoForschungsZentrum （GFZ） post science orbit, for epoch-difference reduced dynamic method, the root mean squares （RMSs） of radial, transverse and normal components are 1.92 cm, 3.83 cm and 3.80 cm, and the RMS in three dimensions is 5.76 cm. The solution＇s accuracy is comparable to the zero-difference reduced dynamic method.
基金Project supported by the National Natural Science Foundation of China （Grant No 60437030）.
文摘The optical feedback characteristics in a Zeeman-birefringence dual-frequency laser are studied during the laser cavity tuning in three different kinds of optical feedback conditions： （i） only //-light is fed back; （ii） only ⊥-light is fed back; （iii） both lights are fed back. A compact displacement sensor is designed using the experimental result that there is a nearly 90 degrees phase delay between the two lights＇ cosine optical feedback signals when both lights are fed back into the laser cavity. The priority order that the two lights＇ intensity curves appear can be used for direction discrimination. The resolution of the displacement sensor is at least 79 rim, and the sensor can discriminate the target＇s moving direction easily.
