The planning of teaching for a course that belongs to an undergraduate program usually begins with the definition of its contents,which are derived from syllabus of a political-pedagogical project.The contents listed ...The planning of teaching for a course that belongs to an undergraduate program usually begins with the definition of its contents,which are derived from syllabus of a political-pedagogical project.The contents listed are organized in a sequence considered logical.A set of actions is planned,such as lectures,laboratories,among others,through which content will be developed.The previous training of the student is considered,the concurrent and subsequent courses,the context of the course inside the program,the specific and general objectives of the program.A set of assessments is also defined as part of this planning,the associated methodologies,techniques and teaching objectives.In this context,this paper focuses on the aspect of the sequencing of content,methodologies and teaching techniques in a course.For this purpose,the Bloom's Taxonomy of Educational Objectives is applied,which provides a hierarchical structure for the cognitive process.The importance of this hierarchy of knowledge is greater awareness of the teacher about the ways to be adopted in the teaching process.展开更多
The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to res...The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to reshape talent cultivation but also presents significant challenges,including skill hollowing,ethical risks,and a growing disconnect between education and industry needs.Currently,graduate-level software engineering education struggles with outdated curricula and insufficient alignment with practical demands.In this paper,we propose a dual-core collaborative framework driven by“GenAI technology”and“industry demand”.Under this framework,we design a four-dimensional capability development path to enhance graduate students’innovation in software engineering practice.This path focuses on①scientific research innovation,②engineering problem-solving,③cross-domain collaborative evolution,and④ethical risk governance.The proposed approach promotes a shift from traditional knowledge transfer to human-machine collaborative innovation,aligning talent cultivation with the demands of the NQPF.展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Despite the intrinsic durability of polymeric hole transport materials,poly-triarylamines(PTAA)-based inverted perovskite solar cells(PSCs)have lagged behind their counterparts in efficiency,primarily due to poor surf...Despite the intrinsic durability of polymeric hole transport materials,poly-triarylamines(PTAA)-based inverted perovskite solar cells(PSCs)have lagged behind their counterparts in efficiency,primarily due to poor surface wettability,insufficient interfacial contact,and unfavorable energy level alignment at the PTAA/perovskite interface.Here,we report a highly effective interfacial engineering strategy employing the ionic liquid 1,3-dimethylimidazolium dimethyl phosphate(DMIMPH)as a multifunctional interfacial modifier.The incorporation of DMIMPH improves PTAA wettability,promoting the growth of high-quality perovskite films with enhanced interfacial contact.Concurrently,DMIMPH effectively tunes the energy levels of PTAA,enhances its electrical conductivity,and passivates interfacial defects with more efficient hole extraction and charge transport.Moreover,its interaction with residual PbI_(2) modulates perovskite crystallization kinetics,yielding highly crystalline perovskite films with enlarged grain sizes,reduced PbI_(2) residue,and suppressed trap densities.As a result,PTAA-based p-i-n PSCs employing this approach achieve a record certified power conversion efficiency(PCE)of 24.52%,with a champion efficiency of 25.12%—the highest certified value for PTAA-based perovskite devices to date.Impressively,the DMIMPH-modified PSCs without encapsulation maintained 87.48%of their initial efficiency after 1600 h in air.This strategy offers an effective pathway for advancing the performance and stability of polymer-based inverted PSCs.展开更多
Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with impro...Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with improved efficiency.This study presents the design and implementation of an autonomous radio-controlled(RC)vehicle prototype capable of lane line detection,obstacle avoidance,and navigation through dynamic path planning.The system integrates image processing and ultrasonic sensing,utilizing Raspberry Pi for vision-based tasks and ArduinoNano for real-time control.Lane line detection is achieved through conventional image processing techniques,providing the basis for local path generation,while traffic sign classification employs a You Only Look Once(YOLO)model optimized with TensorFlow Lite to support navigation decisions.Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles,enabling the vehicle to follow the planned path.In addition,ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation.Experimental results demonstrate stable performance under controlled conditions,highlighting the system’s potential for scalable autonomous driving applications.This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems,offering a practical framework for navigation,path planning,and intelligent transportation research.展开更多
Precise experimental control and characterization of electron wave packet dynamics driven by external optical fields remain a fundamental challenge,particularly at ultrafast temporal and sub-microscopic spatial scales...Precise experimental control and characterization of electron wave packet dynamics driven by external optical fields remain a fundamental challenge,particularly at ultrafast temporal and sub-microscopic spatial scales.To overcome these challenges,we introduce a photon-based simulation platform employing a traveling-wave electrooptic phase-modulated waveguide.In our setup,the incident electromagnetic pulse serves as an analog to the electron wave packet,while the traveling-wave modulation simulates the external optical driving field.Our experimental study systematically explores pulse evolution under three distinct regimes defined by the relation between the pulse duration(Δt)and the modulation period(T).When the pulse duration is significantly shorter than the modulation period,we observe a uniform spectral shift analogous to electron acceleration in dielectric laser accelerators,where spectral phase gradients represent electron momentum accumulation.Conversely,when the pulse duration greatly exceeds the modulation period,discrete diffraction patterns emerge,closely resembling the discrete sideband features of electron-photon coupling observed in photon-induced near-field electron microscopy.Notably,in the intermediate regime(T/4<Δt<T/2),the pulse spectrum exhibits Airy-function-type characteristics with self-healing effects.These experimental results provide critical insights into electron-wave interactions under external optical fields and establish a robust,programmable framework for further investigation.展开更多
Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing n...Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing new possibilities for the treatment and rehabilitation of mental illnesses.This paper reviews the advantages,existing risks,and challenges of BCI technology in mental health treatment,and prospects the future development of research on BCI and mental health.展开更多
Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing de...Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.展开更多
Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Op...Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.展开更多
With the growing importance of wearable and portable electronics in modern society and industry,researchers from all over the world have reported on advances in energy harvesting and self-powered sensing technologies....With the growing importance of wearable and portable electronics in modern society and industry,researchers from all over the world have reported on advances in energy harvesting and self-powered sensing technologies.The current review discusses recent developments in triboelectric platforms from a manufacturing perspective,including material,design,application,and industrialization.Manufacturing is an essential component of both industry and technology.The use of a proper manufacturing process enables cutting-edge technology in a lab-scale stage to progress to commercialization and popularization with scalability,availability,commercial advantage,and consistent quality.Furthermore,much literature has emphasized that the most powerful advantage of the triboelectric platform is its wide range of available materials and simple working mechanism,both of which are important characteristics in manufacturing engineering.As a result,different manufacturing processes can be implemented as needed.Because the practical process can have a synergetic effect on the fundamental development,resulting in the growth of both,the development of the triboelectric platform from the standpoint of manufacturing engineering can be further advanced.However,research into the development of a productive manufacturing process is still in its early stages in the field of triboelectric platforms.This review looks at the various manufacturing technologies used in previous studies and discusses the potential benefits of the appropriate process for triboelectric platforms.Given its unique strength,which includes a diverse material selection and a simple working mechanism,the triboelectric platform can use a variety of manufacturing technologies and the process can be optimized as needed.Numerous research groups have clearly demonstrated the triboelectric platform's advantages.As a result,using appropriate manufacturing processes can accelerate the technological advancement of triboelectric platforms in a variety of research and industrial fields by allowing them to move beyond the lab-scale fabrication stage.展开更多
Scalable fabrication of homogeneous perovskite films remains crucial for bridging the efficiency gap between lab-scale solar cells and commercial solar modules.To tackle this issue,we introduce NCyano-4-methyl-N-pheny...Scalable fabrication of homogeneous perovskite films remains crucial for bridging the efficiency gap between lab-scale solar cells and commercial solar modules.To tackle this issue,we introduce NCyano-4-methyl-N-phenylbenzenesulfonamide(CMPS) additives into perovskite precursors,enabling slot-die coating of large-area modules under ambient conditions.CMPS suppresses colloidal aggregation and delays crystallization,yielding high-quality uniform films.Small-area devices(0.07 cm^(2) aperture area) incorporating CMPS exhibited a significant efficiency increase from 22.07 % to 24.58 %.Corresponding encapsulated devices maintained 85 % of their initial power conversion efficiency(PCE,average 23.56 %) after 1500 h of continuous maximum power point(MPP) tracking under one-sun illumination at 50-55℃.Furthermore,we demonstrate impressive efficiency of perovskite solar modules,achieving 20.57 %(52 cm^(2) aperture area) for 10 cm × 10 cm mini-modules and 17.02 %(260 cm^(2) aperture area) for 21 cm × 21 cm sub-modules,representing the state-of-the-art performance for solutionprocessed devices at these scales.展开更多
We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to...We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene...This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal depend...Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal dependencies,and weak resilience to adversarial updates.To address these limitations,EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics.The architecture integrates cross-modal embedding networks for modality alignment,graph transformer encoders for spatial dependency modeling,temporal self-attention for dynamic pattern learning,and adaptive anomaly detection to ensure data quality and security during aggregation.A privacy-preserving federated learning protocol with differential privacy guarantees enables collaborative model training without centralizing sensitive data.The framework employs data-quality-aware weighted aggregation to enhance robustness against noisy and malicious client updates.Experimental evaluation on the GeoLife,PeMS-Bay,and SmartHome+datasets demonstrates that EdgeST-Fusion achieves 21.8%improvement in prediction accuracy,35.7%reduction in communication overhead,and 29.4%enhancement in security resilience compared to recent baselines.Real-world deployment across three smart city testbeds validates practical viability with 90.0%average accuracy and sub-250 ms inference latency.The proposed framework remains feasible for deployment on heterogeneous and resource-constrained consumer electronics devices whilemaintaining strong privacy guarantees and scalability for large-scale urban environments.展开更多
The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the S...The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.展开更多
WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstr...WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response.展开更多
Vehicular Internet ofThings(V-IoT)networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication(URLLC)in highly dynamic environments.Traditional reinforcement...Vehicular Internet ofThings(V-IoT)networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication(URLLC)in highly dynamic environments.Traditional reinforcement learning(RL)-based algorithms,such as Q-Learning and Double Q-Learning,are often characterized by unstable convergence and inefficient exploration in the presence of stochastic vehicular traffic and interference.This paper proposes Adaptive Reinforcement Q-learning with Upper Confidence Bound(ARQ-UCB),a lightweight and reliability-aware RL framework,which explicitly reduces interruption and blocking probabilities while improving throughput and delay across diverse vehicular traffic conditions.This proposed ARQ-UCB algorithm extends the basic Q-updates with an exploration confidence term able to dynamically balance exploration and exploitation based on uncertainty estimates,hence allowing faster convergence in case of bursty vehicular traffic.A comprehensive simulation framework evaluates throughput,delay,fairness,energy efficiency,and computational complexity in several V-IoT scenarios.Obtained results indicate that ARQ–UCB attains substantial gains in terms of throughput,fairness,and blocking/delay probabilities while retaining sub-20μs decision latency and O(1)complexity per decision,thus validating real-time feasibility for reliable spectrum access in 5G and beyond V-IoT networks.展开更多
The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant e...The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.展开更多
文摘The planning of teaching for a course that belongs to an undergraduate program usually begins with the definition of its contents,which are derived from syllabus of a political-pedagogical project.The contents listed are organized in a sequence considered logical.A set of actions is planned,such as lectures,laboratories,among others,through which content will be developed.The previous training of the student is considered,the concurrent and subsequent courses,the context of the course inside the program,the specific and general objectives of the program.A set of assessments is also defined as part of this planning,the associated methodologies,techniques and teaching objectives.In this context,this paper focuses on the aspect of the sequencing of content,methodologies and teaching techniques in a course.For this purpose,the Bloom's Taxonomy of Educational Objectives is applied,which provides a hierarchical structure for the cognitive process.The importance of this hierarchy of knowledge is greater awareness of the teacher about the ways to be adopted in the teaching process.
基金supported in part by the Graduate Education Reform Research Project of Hubei University of Technology under Grant 2024YB003the Hubei University of Arts and Science,Teaching Research Project,under Grant JY2025018.
文摘The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to reshape talent cultivation but also presents significant challenges,including skill hollowing,ethical risks,and a growing disconnect between education and industry needs.Currently,graduate-level software engineering education struggles with outdated curricula and insufficient alignment with practical demands.In this paper,we propose a dual-core collaborative framework driven by“GenAI technology”and“industry demand”.Under this framework,we design a four-dimensional capability development path to enhance graduate students’innovation in software engineering practice.This path focuses on①scientific research innovation,②engineering problem-solving,③cross-domain collaborative evolution,and④ethical risk governance.The proposed approach promotes a shift from traditional knowledge transfer to human-machine collaborative innovation,aligning talent cultivation with the demands of the NQPF.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
基金supported by the Research Projects of the Department of Education of Guangdong Province 2024ZDZX3079The financial support from the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011677)+4 种基金the Scientific and Technical Innovation Council of Shenzhen(20220812165832002)the Research Projects of Department of Education of Guangdong Province-2023GCZX015the Innovation Team Project of Guangdong(2022KCXTD055)the China Postdoctoral Science Foundation(Certificate Number:2024M763441)is gratefully acknowledgedsupported by the Postdoctoral Fellowship Program of CPSF under Grant Number GZB20250031 and Research Projects of the Department of Education of Guangdong Province 2023GCZX015。
文摘Despite the intrinsic durability of polymeric hole transport materials,poly-triarylamines(PTAA)-based inverted perovskite solar cells(PSCs)have lagged behind their counterparts in efficiency,primarily due to poor surface wettability,insufficient interfacial contact,and unfavorable energy level alignment at the PTAA/perovskite interface.Here,we report a highly effective interfacial engineering strategy employing the ionic liquid 1,3-dimethylimidazolium dimethyl phosphate(DMIMPH)as a multifunctional interfacial modifier.The incorporation of DMIMPH improves PTAA wettability,promoting the growth of high-quality perovskite films with enhanced interfacial contact.Concurrently,DMIMPH effectively tunes the energy levels of PTAA,enhances its electrical conductivity,and passivates interfacial defects with more efficient hole extraction and charge transport.Moreover,its interaction with residual PbI_(2) modulates perovskite crystallization kinetics,yielding highly crystalline perovskite films with enlarged grain sizes,reduced PbI_(2) residue,and suppressed trap densities.As a result,PTAA-based p-i-n PSCs employing this approach achieve a record certified power conversion efficiency(PCE)of 24.52%,with a champion efficiency of 25.12%—the highest certified value for PTAA-based perovskite devices to date.Impressively,the DMIMPH-modified PSCs without encapsulation maintained 87.48%of their initial efficiency after 1600 h in air.This strategy offers an effective pathway for advancing the performance and stability of polymer-based inverted PSCs.
文摘Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with improved efficiency.This study presents the design and implementation of an autonomous radio-controlled(RC)vehicle prototype capable of lane line detection,obstacle avoidance,and navigation through dynamic path planning.The system integrates image processing and ultrasonic sensing,utilizing Raspberry Pi for vision-based tasks and ArduinoNano for real-time control.Lane line detection is achieved through conventional image processing techniques,providing the basis for local path generation,while traffic sign classification employs a You Only Look Once(YOLO)model optimized with TensorFlow Lite to support navigation decisions.Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles,enabling the vehicle to follow the planned path.In addition,ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation.Experimental results demonstrate stable performance under controlled conditions,highlighting the system’s potential for scalable autonomous driving applications.This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems,offering a practical framework for navigation,path planning,and intelligent transportation research.
基金supported by the National Natural Science Foundation of China(Grant No.12174260)the Shanghai Rising-Star Program(Grant No.21QA1406400)+1 种基金the Shanghai Science and Technology Development Fund(Grant Nos.21ZR1443500 and 21ZR1443600)supported by Research Grants Council,University Grants Committee(Grant Nos.STG3/E-704/23-N,CityU 11212721,and CityU 11204523).
文摘Precise experimental control and characterization of electron wave packet dynamics driven by external optical fields remain a fundamental challenge,particularly at ultrafast temporal and sub-microscopic spatial scales.To overcome these challenges,we introduce a photon-based simulation platform employing a traveling-wave electrooptic phase-modulated waveguide.In our setup,the incident electromagnetic pulse serves as an analog to the electron wave packet,while the traveling-wave modulation simulates the external optical driving field.Our experimental study systematically explores pulse evolution under three distinct regimes defined by the relation between the pulse duration(Δt)and the modulation period(T).When the pulse duration is significantly shorter than the modulation period,we observe a uniform spectral shift analogous to electron acceleration in dielectric laser accelerators,where spectral phase gradients represent electron momentum accumulation.Conversely,when the pulse duration greatly exceeds the modulation period,discrete diffraction patterns emerge,closely resembling the discrete sideband features of electron-photon coupling observed in photon-induced near-field electron microscopy.Notably,in the intermediate regime(T/4<Δt<T/2),the pulse spectrum exhibits Airy-function-type characteristics with self-healing effects.These experimental results provide critical insights into electron-wave interactions under external optical fields and establish a robust,programmable framework for further investigation.
文摘Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing new possibilities for the treatment and rehabilitation of mental illnesses.This paper reviews the advantages,existing risks,and challenges of BCI technology in mental health treatment,and prospects the future development of research on BCI and mental health.
基金The author Dr.Arshiya S.Ansari extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1538).
文摘Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.
基金funded by Researchers Supporting Programnumber(RSPD2024R809),King Saud University,Riyadh,Saudi Arabia.
文摘Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.
基金supported by the National Research Foundation of Korea(NRF)(No.2021R1C1C2009703)supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(RS-2024-00344920)supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)Grant funded by the Ministry of Trade,Industry and Energy of Korea(No.RS2023-00244330)。
文摘With the growing importance of wearable and portable electronics in modern society and industry,researchers from all over the world have reported on advances in energy harvesting and self-powered sensing technologies.The current review discusses recent developments in triboelectric platforms from a manufacturing perspective,including material,design,application,and industrialization.Manufacturing is an essential component of both industry and technology.The use of a proper manufacturing process enables cutting-edge technology in a lab-scale stage to progress to commercialization and popularization with scalability,availability,commercial advantage,and consistent quality.Furthermore,much literature has emphasized that the most powerful advantage of the triboelectric platform is its wide range of available materials and simple working mechanism,both of which are important characteristics in manufacturing engineering.As a result,different manufacturing processes can be implemented as needed.Because the practical process can have a synergetic effect on the fundamental development,resulting in the growth of both,the development of the triboelectric platform from the standpoint of manufacturing engineering can be further advanced.However,research into the development of a productive manufacturing process is still in its early stages in the field of triboelectric platforms.This review looks at the various manufacturing technologies used in previous studies and discusses the potential benefits of the appropriate process for triboelectric platforms.Given its unique strength,which includes a diverse material selection and a simple working mechanism,the triboelectric platform can use a variety of manufacturing technologies and the process can be optimized as needed.Numerous research groups have clearly demonstrated the triboelectric platform's advantages.As a result,using appropriate manufacturing processes can accelerate the technological advancement of triboelectric platforms in a variety of research and industrial fields by allowing them to move beyond the lab-scale fabrication stage.
基金supported by the Fundamental Research Funds for the Central Universities (YJSJ25013,ZYTS25235,ZYTS25229)the National Natural Science Foundation of China (62274132, 62204189,62004151,62274126)+3 种基金the National Key R&D Program of China (2021YFF0500504,2021YFF0500501)the Natural Science Basic Research Plan in Shaanxi Province of China (2024GX-YBXM514)the Young Talent Fund of Association for Science and Technology in Shaanxi,China (20220115)the Interdisciplinary Cultivation Program of Xidian University (21103240003)。
文摘Scalable fabrication of homogeneous perovskite films remains crucial for bridging the efficiency gap between lab-scale solar cells and commercial solar modules.To tackle this issue,we introduce NCyano-4-methyl-N-phenylbenzenesulfonamide(CMPS) additives into perovskite precursors,enabling slot-die coating of large-area modules under ambient conditions.CMPS suppresses colloidal aggregation and delays crystallization,yielding high-quality uniform films.Small-area devices(0.07 cm^(2) aperture area) incorporating CMPS exhibited a significant efficiency increase from 22.07 % to 24.58 %.Corresponding encapsulated devices maintained 85 % of their initial power conversion efficiency(PCE,average 23.56 %) after 1500 h of continuous maximum power point(MPP) tracking under one-sun illumination at 50-55℃.Furthermore,we demonstrate impressive efficiency of perovskite solar modules,achieving 20.57 %(52 cm^(2) aperture area) for 10 cm × 10 cm mini-modules and 17.02 %(260 cm^(2) aperture area) for 21 cm × 21 cm sub-modules,representing the state-of-the-art performance for solutionprocessed devices at these scales.
基金financial supports from the National Natural Science Foundation of China(Grant No.6227511362405124).
文摘We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
基金the Engineering and Physical Sciences Research Council(EPSRC)for funding the researchUK India Education Research Initiative(UKIERI)for funding support.
文摘This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金supported by the University of Tabuk,Saudi Arabia。
文摘Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal dependencies,and weak resilience to adversarial updates.To address these limitations,EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics.The architecture integrates cross-modal embedding networks for modality alignment,graph transformer encoders for spatial dependency modeling,temporal self-attention for dynamic pattern learning,and adaptive anomaly detection to ensure data quality and security during aggregation.A privacy-preserving federated learning protocol with differential privacy guarantees enables collaborative model training without centralizing sensitive data.The framework employs data-quality-aware weighted aggregation to enhance robustness against noisy and malicious client updates.Experimental evaluation on the GeoLife,PeMS-Bay,and SmartHome+datasets demonstrates that EdgeST-Fusion achieves 21.8%improvement in prediction accuracy,35.7%reduction in communication overhead,and 29.4%enhancement in security resilience compared to recent baselines.Real-world deployment across three smart city testbeds validates practical viability with 90.0%average accuracy and sub-250 ms inference latency.The proposed framework remains feasible for deployment on heterogeneous and resource-constrained consumer electronics devices whilemaintaining strong privacy guarantees and scalability for large-scale urban environments.
文摘The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.
文摘WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response.
基金support the findings of this study are available from the corresponding authors upon reasonable request.
文摘Vehicular Internet ofThings(V-IoT)networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication(URLLC)in highly dynamic environments.Traditional reinforcement learning(RL)-based algorithms,such as Q-Learning and Double Q-Learning,are often characterized by unstable convergence and inefficient exploration in the presence of stochastic vehicular traffic and interference.This paper proposes Adaptive Reinforcement Q-learning with Upper Confidence Bound(ARQ-UCB),a lightweight and reliability-aware RL framework,which explicitly reduces interruption and blocking probabilities while improving throughput and delay across diverse vehicular traffic conditions.This proposed ARQ-UCB algorithm extends the basic Q-updates with an exploration confidence term able to dynamically balance exploration and exploitation based on uncertainty estimates,hence allowing faster convergence in case of bursty vehicular traffic.A comprehensive simulation framework evaluates throughput,delay,fairness,energy efficiency,and computational complexity in several V-IoT scenarios.Obtained results indicate that ARQ–UCB attains substantial gains in terms of throughput,fairness,and blocking/delay probabilities while retaining sub-20μs decision latency and O(1)complexity per decision,thus validating real-time feasibility for reliable spectrum access in 5G and beyond V-IoT networks.
文摘The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.
