Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,co...Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.展开更多
Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic computing.However,most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor(CMO...Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic computing.However,most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor(CMOS)technology still suffer from substantially larger energy consumption than biological synapses,due to the slow kinetics of forming conductive paths inside the memristive units.Here we report wafer-scale Ag_(2)S-based MCAs realized using CMOS-compatible processes at temperatures below 160℃.Ag_(2)S electrolytes supply highly mobile Ag+ions,and provide the Ag/Ag_(2)S interface with low silver nucleation barrier to form silver filaments at low energy costs.By further enhancing Ag+migration in Ag_(2)S electrolytes via microstructure modulation,the integrated memristors exhibit a record low threshold of approximately−0.1 V,and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses.The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics.Moreover,the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm.An impressive accuracy of 92.6%in image recognition simulations is demonstrated with the MCAs after the compensation.The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.展开更多
Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-spe...Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.展开更多
This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r...This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with v...Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.展开更多
The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists fac...The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.展开更多
The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the c...The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.展开更多
Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety advantages.These benefits originate from the abundan...Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety advantages.These benefits originate from the abundance of zinc and its compatibility with non-flammable aqueous electrolytes.However,the inherent instability of zinc in aqueous environments,manifested through hydrogen evolution reactions(HER)and dendritic growth,has hindered commercialization due to poor cycling stability.Enter potassium polyacrylate(PAAK)-based water-in-polymer salt electrolyte(WiPSE),a novel variant of water-in-salt electrolytes(WiSE),designed to mitigate side reactions associated with water redox processes,thereby enhancing the cyclic stability of ZnBs.In this study,WiPSE was employed in ZnBs featuring lignin and carbon composites as cathode materials.Our research highlights the crucial function of acrylate groups from WiPSE in stabilizing the ionic flux on the surface of the Zn electrode.This stabilization promotes the parallel deposition of Zn along the(002)plane,resulting in a significant reduction in dendritic growth.Notably,our sustainable Zn-lignin battery showcases remarkable cyclic stability,retaining 80%of its initial capacity after 8000 cycles at a high current rate(1 A g^(-1))and maintaining over 75%capacity retention up to 2000 cycles at a low current rate(0.2 A g^(-1)).This study showcases the practical application of WiPSE for the development of low-cost,dendrite-free,and scalable ZnBs.展开更多
VME system of the Resistive Plate Chamber (RPC) electronics for the Daya Bay Reactor Neutrino Experiment is described in this paper. A 9U VME RPC trigger module (RTM) is designed to process coincidence signals coming ...VME system of the Resistive Plate Chamber (RPC) electronics for the Daya Bay Reactor Neutrino Experiment is described in this paper. A 9U VME RPC trigger module (RTM) is designed to process coincidence signals coming from front end cards (FECs), to generate local triggers and send them to FECs to select the hit data from RPC detector, to report trigger information to a master trigger system and receive cross triggers from the master trigger system. Another 9U VME readout module is designed to collect data from all FECs, to send out configurations to FECs, and to transmit collected hit data to the data acquisition system via VME bus. Test results prove that the VME system is capable of treating a maximum data rate (2.2 MB·s-1 ), without data loss.展开更多
Aiming at the problem of low surface defect detection accuracy of industrial products, an object detection method based on simplified spatial pyramid pooling fast(Sim SPPF) hybrid pooling improved you only look once v...Aiming at the problem of low surface defect detection accuracy of industrial products, an object detection method based on simplified spatial pyramid pooling fast(Sim SPPF) hybrid pooling improved you only look once version 5s(YOLOV5s) model is proposed. The algorithm introduces channel attention(CA) module, simplified SPPF feature vector pyramid and efficient intersection over union(EIOU) loss function. Feature vector pyramids fuse high-dimensional and low-dimensional features, which makes semantic information richer. The CA mechanism performs maximum pooling and average pooling operations on the feature map. Hybrid pooling comprehensively improves detection computing efficiency and accurate deployment ability. The results show that the improved YOLOV5s model is better than the original YOLOV5s model. The average test accuracy(mAP) can reach 91.8%, which can be increased by 17.4%, and the detection speed can reach 108 FPS, which can be increased by 18 FPS. The improved model is practicable, and the overall performance is better than other conventional models.展开更多
The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in re...The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in remote areas.So far,Solar Home Systems(SHS)have mostly been applied to increase electricity access in rural areas.SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities.The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study.The renewable energy resources available in Remera are the key sources of electricity in that village.The generation capacity is estimated based on the load profile.The microgrid configurations are simulated with HOMER,and the genetic algorithm is used to analyze the optimum cost.By analyzing the impact of operation and maintenance costs,the results show that the absence of subsidies increases the levelized cost of electricity(COE)five times greater than the electricity price from the public utility.The microgrid made up of PV,diesel generator,and batteries proved to be the most viable solution and ensured continuous power supply to customers.By considering the subsidies,COE reaches 0.186$/kWh,a competitive price with electricity from public utilities in Rwanda.展开更多
A low-profile,vertically polarized,ultra-wideband array antenna with end-fire beams operating in an ultra-high frequency(UHF)band is developed in this paper.The array antenna consists of 1×16 log-periodic top-hat...A low-profile,vertically polarized,ultra-wideband array antenna with end-fire beams operating in an ultra-high frequency(UHF)band is developed in this paper.The array antenna consists of 1×16 log-periodic top-hat loaded monopole antenna arrays and is feasible to embed into a shallow cavity to further reduce the array height.Capacitance is introduced in the proposed antenna element to reduce profile height and the rectangular top hats are carefully designed to minimize the transverse dimension.Simulated results show that when the antenna array operates in a frequency range of 300 MHz-900 MHz,the end-fire radiation pattern achieves±45°scanning range in the horizontal plane.Then prototypes of the proposed end-fire antenna element and a uniformly spaced linear array(1×2)are fabricated and validated.The end-fire antenna array should be suitable for airborne applications where low-profile and conformal scanning phased antenna arrays with end-fire radiations are required.This design is attractive for airborne platform applications that are used to search,discover,identify,and scout the aerial target with vertically polarized beams.展开更多
Surface modifications can introduce natural gradients or structural hierarchy into human-made microlattices,making them simultaneously strong and tough.Herein,we describe our investigations of the mechanical propertie...Surface modifications can introduce natural gradients or structural hierarchy into human-made microlattices,making them simultaneously strong and tough.Herein,we describe our investigations of the mechanical properties and the underlying mechanisms of additively manufactured nickel–chromium superalloy(IN625)microlattices after surface mechanical attrition treatment(SMAT).Our results demonstrated that SMAT increased the yielding strength of these microlattices by more than 64.71%and also triggered a transition in their mechanical behaviour.Two primary failure modes were distinguished:weak global deformation,and layer-by-layer collapse,with the latter enhanced by SMAT.The significantly improved mechanical performance was attributable to the ultrafine and hard graded-nanograin layer induced by SMAT,which effectively leveraged the material and structural effects.These results were further validated by finite element analysis.This work provides insight into collapse behaviour and should facilitate the design of ultralight yet buckling-resistant cellular materials.展开更多
Satellite-to-ground terahertz communication is limited by the power of signal source and antenna gain level,and has large path loss,which is difficult to implement.In this paper,a feasible scheme of satellite-to-groun...Satellite-to-ground terahertz communication is limited by the power of signal source and antenna gain level,and has large path loss,which is difficult to implement.In this paper,a feasible scheme of satellite-to-ground terahertz communication using High Altitude Platforms(HAPs)as relay is presented,and the path loss on terahertz communication links is modeled and analyzed.Combined with the path loss model,the transmission loss along HAP-to-ground paths under different seasons and complex weather environment in Ali,Xizang,China is calculated.The results show that the transmission characteristics of terahertz waves in winter and summer are significantly different,mainly reflected in the number and bandwidth of usable atmospheric windows.Furthermore,the additional attenuation caused by the typical sand dust and ice cloud environment on terahertz band can reach 6.1 dB and 1.9 dB at the maximum respectively.With the aid of high gain antenna,the usable communication frequencies of the HAP-to-ground links in winter are significantly more than those in summer.When the transmitting and receiving antenna gain is 40 dBi respectively,the usable communication frequency can reach 1.35 THz in winter,while it is limited to less than 1 THz in summer,up to 0.493 THz.展开更多
The BETA application-specific integrated circuit(ASIC)is a fully programmable chip designed to amplify,shape and digitize the signal of up to 64 Silicon photomultiplier(SiPM)channels,with a power consumption of approx...The BETA application-specific integrated circuit(ASIC)is a fully programmable chip designed to amplify,shape and digitize the signal of up to 64 Silicon photomultiplier(SiPM)channels,with a power consumption of approximately~1 mW/channel.Owing to its dual-path gain,the BETA chip is capable of resolving single photoelectrons(phes)with a signal-to-noise ratio(SNR)>5 while simultaneously achieving a dynamic range of~4000 phes.Thus,BETA can provide a cost-effective solution for the readout of SiPMs in space missions and other applications with a maximum rate below 10 kHz.In this study,we describe the key characteristics of the BETA ASIC and present an evaluation of the performance of its 16-channel version,which is implemented using 130 nm technology.The ASIC also contains two discriminators that can provide trigger signals with a time jitter down to 400 ps FWHM for 10 phes.The linearity error of the charge gain measurement was less than 2%for a dynamic range as large as 15 bits.展开更多
Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR...Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is presented.The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this work.The new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency regularisation.The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the baseline.The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.展开更多
Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional ...Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional performance across a spectrum of tasks.The success of deep learning methods can be attributed to their capability to derive potent representations from data,integral for a myriad of downstream applications.These representations encapsulate the intrinsic structure,fea-tures,or latent variables characterising the underlying statistics of visual data.Despite these achievements,the challenge per-sists in effectively conducting representation learning of visual data with deep models,particularly when confronted with vast and noisy datasets.This special issue is a dedicated platform for researchers worldwide to disseminate their latest,high-quality articles,aiming to enhance readers'comprehension of the principles,limitations,and diverse applications of repre-sentation learning in computer vision.展开更多
This comprehensive review examines the structural,mechanical,electronic,and thermodynamic properties of Mg-Li-Al alloys,focusing on their corrosion resistance and mechanical performance enhancement.Utilizing first-pri...This comprehensive review examines the structural,mechanical,electronic,and thermodynamic properties of Mg-Li-Al alloys,focusing on their corrosion resistance and mechanical performance enhancement.Utilizing first-principles calculations based on Density Functional Theory(DFT)and the quasi-harmonic approximation(QHA),the combined properties of the Mg-Li-Al phase are explored,revealing superior incompressibility,shear resistance,and stiffness compared to individual elements.The review highlights the brittleness of the alloy,supported by B/G ratios,Cauchy pressures,and Poisson’s ratios.Electronic structure analysis shows metallic behavior with varied covalent bonding characteristics,while Mulliken population analysis emphasizes significant electron transfer within the alloy.This paper also studied thermodynamic properties,including Debye temperature,heat capacity,enthalpy,free energy,and entropy,which are precisely examined,highlighting the Mg-Li-Al phase sensitive to thermal conductivity and thermal performance potential.Phonon density of states(PHDOS)confirms dynamic stability,while anisotropic sound velocities reveal elastic anisotropies.This comprehensive review not only consolidates the current understanding of the Mg-Li-Al alloy’s properties but also proposes innovative strategies for enhancing corrosion resistance.Among these strategies is the introduction of a corrosion barrier akin to the Mg-Li-Al network,which holds promise for advancing both the applications and performance of these alloys.This review serves as a crucial foundation for future research aimed at optimizing alloy design and processing methods.展开更多
文摘Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
基金supported by the Swedish Strategic Research Foundation(SSF FFL15-0174 to Zhen Zhang)the Swedish Research Council(VR 2018-06030 and 2019-04690 to Zhen Zhang)+1 种基金the Wallenberg Academy Fellow Extension Program(KAW 2020-0190 to Zhen Zhang)the Olle Engkvist Foundation(Postdoc grant 214-0322 to Zhen Zhang).
文摘Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic computing.However,most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor(CMOS)technology still suffer from substantially larger energy consumption than biological synapses,due to the slow kinetics of forming conductive paths inside the memristive units.Here we report wafer-scale Ag_(2)S-based MCAs realized using CMOS-compatible processes at temperatures below 160℃.Ag_(2)S electrolytes supply highly mobile Ag+ions,and provide the Ag/Ag_(2)S interface with low silver nucleation barrier to form silver filaments at low energy costs.By further enhancing Ag+migration in Ag_(2)S electrolytes via microstructure modulation,the integrated memristors exhibit a record low threshold of approximately−0.1 V,and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses.The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics.Moreover,the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm.An impressive accuracy of 92.6%in image recognition simulations is demonstrated with the MCAs after the compensation.The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.
基金Funded by the Spanish Government and FEDER funds(AEI/FEDER,UE)under grant PID2021-124502OB-C42(PRESECREL)the predoctoral program“Concepción Arenal del Programa de Personal Investigador en formación Predoctoral”funded by Universidad de Cantabria and Cantabria’s Government(BOC 18-10-2021).
文摘Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.
基金supported by the Scientific and Technological Research Council of Turkey(TUBITAK)under Grant No.124E002(1001-Project).
文摘This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
文摘Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.
基金supported by NSTC 113-2221-E-155-055NSTC 113-2222-E-155-007,Taiwan.
文摘The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.
文摘The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.
基金primarily supported by the Proof-of-Concept project “high-voltage aqueous electrolytes (KAW 2020.0174)”the “Wood Wal enberg Science Center” funded by Knut and Alice Wal enberg (KAW) foundation+6 种基金supported by the Swedish Research Council (2016-05990)the Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-Li U No. 2009-00971)the competence center Fun Mat-II funded by the Swedish Agency for Innovation Systems (Vinnova, grant no 2016-05156)Aforsk foundation for the project “anode free Zn-ion battery (21-130)” and “Zn-lignin battery (22-134)”Swedish Electricity Storage and Balancing Centre (SESBC) funded by Energyimyndigghetenthe Swedish Research Council VR International Postdoc Grant 2022-00213“STand UP for energy col aboration and Swedish Research Council (2020-05223)”
文摘Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety advantages.These benefits originate from the abundance of zinc and its compatibility with non-flammable aqueous electrolytes.However,the inherent instability of zinc in aqueous environments,manifested through hydrogen evolution reactions(HER)and dendritic growth,has hindered commercialization due to poor cycling stability.Enter potassium polyacrylate(PAAK)-based water-in-polymer salt electrolyte(WiPSE),a novel variant of water-in-salt electrolytes(WiSE),designed to mitigate side reactions associated with water redox processes,thereby enhancing the cyclic stability of ZnBs.In this study,WiPSE was employed in ZnBs featuring lignin and carbon composites as cathode materials.Our research highlights the crucial function of acrylate groups from WiPSE in stabilizing the ionic flux on the surface of the Zn electrode.This stabilization promotes the parallel deposition of Zn along the(002)plane,resulting in a significant reduction in dendritic growth.Notably,our sustainable Zn-lignin battery showcases remarkable cyclic stability,retaining 80%of its initial capacity after 8000 cycles at a high current rate(1 A g^(-1))and maintaining over 75%capacity retention up to 2000 cycles at a low current rate(0.2 A g^(-1)).This study showcases the practical application of WiPSE for the development of low-cost,dendrite-free,and scalable ZnBs.
基金Supported by National Natural Science Foundation of China (Grant No.10890091)Guangdong Province and Chinese Academy of Sciences’Comprehensive Strategic Cooperation Projects (No.2011A090100015)
文摘VME system of the Resistive Plate Chamber (RPC) electronics for the Daya Bay Reactor Neutrino Experiment is described in this paper. A 9U VME RPC trigger module (RTM) is designed to process coincidence signals coming from front end cards (FECs), to generate local triggers and send them to FECs to select the hit data from RPC detector, to report trigger information to a master trigger system and receive cross triggers from the master trigger system. Another 9U VME readout module is designed to collect data from all FECs, to send out configurations to FECs, and to transmit collected hit data to the data acquisition system via VME bus. Test results prove that the VME system is capable of treating a maximum data rate (2.2 MB·s-1 ), without data loss.
基金supported by the Tianjin Postgraduate Research Innovation Project (No.2022SKY286)the National Science and the National Key Research and Development Program (No.2022YFF0706000)。
文摘Aiming at the problem of low surface defect detection accuracy of industrial products, an object detection method based on simplified spatial pyramid pooling fast(Sim SPPF) hybrid pooling improved you only look once version 5s(YOLOV5s) model is proposed. The algorithm introduces channel attention(CA) module, simplified SPPF feature vector pyramid and efficient intersection over union(EIOU) loss function. Feature vector pyramids fuse high-dimensional and low-dimensional features, which makes semantic information richer. The CA mechanism performs maximum pooling and average pooling operations on the feature map. Hybrid pooling comprehensively improves detection computing efficiency and accurate deployment ability. The results show that the improved YOLOV5s model is better than the original YOLOV5s model. The average test accuracy(mAP) can reach 91.8%, which can be increased by 17.4%, and the detection speed can reach 108 FPS, which can be increased by 18 FPS. The improved model is practicable, and the overall performance is better than other conventional models.
文摘The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in remote areas.So far,Solar Home Systems(SHS)have mostly been applied to increase electricity access in rural areas.SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities.The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study.The renewable energy resources available in Remera are the key sources of electricity in that village.The generation capacity is estimated based on the load profile.The microgrid configurations are simulated with HOMER,and the genetic algorithm is used to analyze the optimum cost.By analyzing the impact of operation and maintenance costs,the results show that the absence of subsidies increases the levelized cost of electricity(COE)five times greater than the electricity price from the public utility.The microgrid made up of PV,diesel generator,and batteries proved to be the most viable solution and ensured continuous power supply to customers.By considering the subsidies,COE reaches 0.186$/kWh,a competitive price with electricity from public utilities in Rwanda.
文摘A low-profile,vertically polarized,ultra-wideband array antenna with end-fire beams operating in an ultra-high frequency(UHF)band is developed in this paper.The array antenna consists of 1×16 log-periodic top-hat loaded monopole antenna arrays and is feasible to embed into a shallow cavity to further reduce the array height.Capacitance is introduced in the proposed antenna element to reduce profile height and the rectangular top hats are carefully designed to minimize the transverse dimension.Simulated results show that when the antenna array operates in a frequency range of 300 MHz-900 MHz,the end-fire radiation pattern achieves±45°scanning range in the horizontal plane.Then prototypes of the proposed end-fire antenna element and a uniformly spaced linear array(1×2)are fabricated and validated.The end-fire antenna array should be suitable for airborne applications where low-profile and conformal scanning phased antenna arrays with end-fire radiations are required.This design is attractive for airborne platform applications that are used to search,discover,identify,and scout the aerial target with vertically polarized beams.
基金support provided by Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone Shenzhen Park Project:HZQB-KCZYB-2020030the Hong Kong General Research Fund(GRF)Scheme(Ref:CityU 11216219)+2 种基金the Research Grants Council of Hong Kong(Project No:AoE/M-402/20)Shenzhen Science and Technology Program:JCYJ20220818101204010the Hong Kong Innovation and Technology Commission via the Hong Kong Branch of National Precious Metals Material Engineering Research Center.
文摘Surface modifications can introduce natural gradients or structural hierarchy into human-made microlattices,making them simultaneously strong and tough.Herein,we describe our investigations of the mechanical properties and the underlying mechanisms of additively manufactured nickel–chromium superalloy(IN625)microlattices after surface mechanical attrition treatment(SMAT).Our results demonstrated that SMAT increased the yielding strength of these microlattices by more than 64.71%and also triggered a transition in their mechanical behaviour.Two primary failure modes were distinguished:weak global deformation,and layer-by-layer collapse,with the latter enhanced by SMAT.The significantly improved mechanical performance was attributable to the ultrafine and hard graded-nanograin layer induced by SMAT,which effectively leveraged the material and structural effects.These results were further validated by finite element analysis.This work provides insight into collapse behaviour and should facilitate the design of ultralight yet buckling-resistant cellular materials.
基金This work was supported by the Joint Funds of the National Natural Science Foundation of China(No.U1730247)the National Defense Basic Scientific Research program of China(No.6142605200301).
文摘Satellite-to-ground terahertz communication is limited by the power of signal source and antenna gain level,and has large path loss,which is difficult to implement.In this paper,a feasible scheme of satellite-to-ground terahertz communication using High Altitude Platforms(HAPs)as relay is presented,and the path loss on terahertz communication links is modeled and analyzed.Combined with the path loss model,the transmission loss along HAP-to-ground paths under different seasons and complex weather environment in Ali,Xizang,China is calculated.The results show that the transmission characteristics of terahertz waves in winter and summer are significantly different,mainly reflected in the number and bandwidth of usable atmospheric windows.Furthermore,the additional attenuation caused by the typical sand dust and ice cloud environment on terahertz band can reach 6.1 dB and 1.9 dB at the maximum respectively.With the aid of high gain antenna,the usable communication frequencies of the HAP-to-ground links in winter are significantly more than those in summer.When the transmitting and receiving antenna gain is 40 dBi respectively,the usable communication frequency can reach 1.35 THz in winter,while it is limited to less than 1 THz in summer,up to 0.493 THz.
基金support from Grant PID2020-116075GB-C21funded by MCIN/AEI/10.13039/501100011033+1 种基金by“ERDF A way of making Europe”under Grant PID2020-116075GB-C21They also acknowledge financial support from the State Agency for Research of the Spanish Ministry of Science and Innovation through the“Unit of Excellence Maria de Maeztu 2020-2023”award to the Institute of Cosmos Sciences(CEX2019-000918-M)。
文摘The BETA application-specific integrated circuit(ASIC)is a fully programmable chip designed to amplify,shape and digitize the signal of up to 64 Silicon photomultiplier(SiPM)channels,with a power consumption of approximately~1 mW/channel.Owing to its dual-path gain,the BETA chip is capable of resolving single photoelectrons(phes)with a signal-to-noise ratio(SNR)>5 while simultaneously achieving a dynamic range of~4000 phes.Thus,BETA can provide a cost-effective solution for the readout of SiPMs in space missions and other applications with a maximum rate below 10 kHz.In this study,we describe the key characteristics of the BETA ASIC and present an evaluation of the performance of its 16-channel version,which is implemented using 130 nm technology.The ASIC also contains two discriminators that can provide trigger signals with a time jitter down to 400 ps FWHM for 10 phes.The linearity error of the charge gain measurement was less than 2%for a dynamic range as large as 15 bits.
基金the PID2022‐137451OB‐I00 and PID2022‐137629OA‐I00 projects funded by the MICIU/AEIAEI/10.13039/501100011033 and by ERDF/EU.
文摘Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is presented.The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this work.The new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency regularisation.The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the baseline.The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.
文摘Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional performance across a spectrum of tasks.The success of deep learning methods can be attributed to their capability to derive potent representations from data,integral for a myriad of downstream applications.These representations encapsulate the intrinsic structure,fea-tures,or latent variables characterising the underlying statistics of visual data.Despite these achievements,the challenge per-sists in effectively conducting representation learning of visual data with deep models,particularly when confronted with vast and noisy datasets.This special issue is a dedicated platform for researchers worldwide to disseminate their latest,high-quality articles,aiming to enhance readers'comprehension of the principles,limitations,and diverse applications of repre-sentation learning in computer vision.
文摘This comprehensive review examines the structural,mechanical,electronic,and thermodynamic properties of Mg-Li-Al alloys,focusing on their corrosion resistance and mechanical performance enhancement.Utilizing first-principles calculations based on Density Functional Theory(DFT)and the quasi-harmonic approximation(QHA),the combined properties of the Mg-Li-Al phase are explored,revealing superior incompressibility,shear resistance,and stiffness compared to individual elements.The review highlights the brittleness of the alloy,supported by B/G ratios,Cauchy pressures,and Poisson’s ratios.Electronic structure analysis shows metallic behavior with varied covalent bonding characteristics,while Mulliken population analysis emphasizes significant electron transfer within the alloy.This paper also studied thermodynamic properties,including Debye temperature,heat capacity,enthalpy,free energy,and entropy,which are precisely examined,highlighting the Mg-Li-Al phase sensitive to thermal conductivity and thermal performance potential.Phonon density of states(PHDOS)confirms dynamic stability,while anisotropic sound velocities reveal elastic anisotropies.This comprehensive review not only consolidates the current understanding of the Mg-Li-Al alloy’s properties but also proposes innovative strategies for enhancing corrosion resistance.Among these strategies is the introduction of a corrosion barrier akin to the Mg-Li-Al network,which holds promise for advancing both the applications and performance of these alloys.This review serves as a crucial foundation for future research aimed at optimizing alloy design and processing methods.
