Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
When humans jump down from a high position,there is a risk of serious injury to the lower limbs.However,cats can jump down from the same heights without any injury because of their excellent ability to attenuate impac...When humans jump down from a high position,there is a risk of serious injury to the lower limbs.However,cats can jump down from the same heights without any injury because of their excellent ability to attenuate impact forces.The present study aims to investigate the macro/micro biomechanical features of paw pads and limb bones of cats,and the coordination control of joints during landing,providing insights into how cats protect themselves from landing injury.Accordingly,histological analysis,radiological analysis,finite element method,and mechanical testing were performed to investigate the mechanical properties,microstructure,and biomechanical response of the pads and limb bones.In addition,using a motion capture system,the kinematic/kinetic data during landing were analysed based on inverse dynamics.The results show that the pads and limb bones are major contributors to non-impact-injuries,and cats actively couple their joints to adjust the parameters of movement to dissipate the higher impact.Therefore,the paw pads,limb bones,and coordinated joints complement each other and constitute a multi-level buffering mechanism,providing the cat with the sophisticated shock absorption system.This biomechanical analysis can accordingly provide biological inspiration for new approaches to prevent human lower limb injuries.展开更多
The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmenta...The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.展开更多
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ...Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.展开更多
Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/...Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/TaO_(x) structure,which is facilitated by a wedge-shaped HfO_(2)buffer layer.The field-free switching ratio varies with HfO_(2)thickness,reaching optimal performance at 25 nm.This phenomenon is attributed to the lateral anisotropy gradient of the Co layer,which is induced by the wedge-shaped HfO_(2)buffer layer.The thickness gradient of HfO_(2)along the wedge creates a corresponding lateral anisotropy gradient in the Co layer,correlating with the switching ratio.These findings indicate that field-free SOT switching can be achieved through designing buffer layer,offering a novel approach to innovating spin-orbit device.展开更多
Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-see...Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.展开更多
Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior know...Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.展开更多
Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remain...Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.展开更多
An innovative metallic buffer consisting of series-connected hat-shaped entangled wire mesh damper(EWMD)and parallel springs are proposed in this work to enhance the reliability of engineering equipment.The impact res...An innovative metallic buffer consisting of series-connected hat-shaped entangled wire mesh damper(EWMD)and parallel springs are proposed in this work to enhance the reliability of engineering equipment.The impact response and the energy dissipation mechanism of hat-shaped EWMD under different quasi-static compression deformations(2-7 mm)and impact heights(100-200 mm)are investigated using experimental and numerical methods.The results demonstrate distinct stages in the quasi-static mechanical characteristics of hat-shaped EWMD,including stiffness softening,negative stiffness,and stiffness hardening.The loss factor gradually increases with increasing compression deformation before entering the stiffness hardening stage.Under impact loads,the hat-shaped EWMD exhibits optimal impact energy absorption when it enters the negative stiffness stage(150 mm),resulting in the best impact isolation effect of metallic buffer.However,the impact energy absorption significantly decreases when hat-shaped EWMD enters the stiffness hardening stage.Interestingly,quasi-static compression analysis after experiencing different impact loads reveals the disappearance of the negative stiffness phenomenon.Moreover,with increasing impact loads,the stiffness hardening point progressively shifts to an earlier stage.展开更多
Two methods of irrigation,drip,and sprinkler were studied to determine the response of the Javits green roof to irrigation.The control study was dry unirrigated plots.Drip irrigation consisted of irrigation tubes runn...Two methods of irrigation,drip,and sprinkler were studied to determine the response of the Javits green roof to irrigation.The control study was dry unirrigated plots.Drip irrigation consisted of irrigation tubes running through the green roof that would water the soil throughout and sprinkler irrigation used a sprinkler system to irrigate the green roof from above.In all cases,the irrigated roofs had increased the soil moisture,reduced temperatures of both the upper and lower surfaces,reduced growing medium temperatures and reduced air temperatures above the green roof relative to the unirrigated roof.The buffered temperature fluctuations were also studied via air conditioner energy consumption.There was a 28%reduction in air conditioner energy consumption and a 33%reduction in overall energy consumption between dry and irrigated plots.Values of thermal resistance or S were determined for accuracy and for this study,there was little change which is ideal.A series of infra-red and thermal probe measurements were used to determine temperatures in the air and sedum.It was determined that the sprinkler irrigation did a better job than the drip irrigation in keeping cooler temperatures within the green roof.A Mann-Whitney U test was performed to verify the variation in moisture temperatures buffering energy consumption.By getting a p-value<0.05,it indicates that the model is accurate for prediction and medium temperatures were statistically different.展开更多
Near-infrared(NIR)transparent inverted all-inorganic perovskite solar cells(PSCs)are excellent top cell candidates in tandem applications.An essential challenge is the replacement of metal contacts with transparent co...Near-infrared(NIR)transparent inverted all-inorganic perovskite solar cells(PSCs)are excellent top cell candidates in tandem applications.An essential challenge is the replacement of metal contacts with transparent conductive oxide(TCO)electrodes,which requires the introduction of a buffer layer to prevent sputtering damage.In this study,we show that the conventional buffers(i.e.,small organic molecules and atomic layer deposited metal oxides)used for organic-inorganic hybrid perovskites are not applicable to all-inorganic perovskites,due to non-uniform coverage of the vulnerable layers underneath,deterioration upon ion bombardment and moisture induced perovskite phase transition,A thin film of metal oxide nanoparticles by the spin-coating method serves as a non-destructive buffer layer for inorganic PSCs.All-inorganic inverted near-infrared-transparent PSCs deliver a PCE of 17.46%and an average transmittance of 73.7%between 780 and 1200 nm.In combination with an 18.56%Cu(In,Ga)Se_(2) bottom cell,we further demonstrate the first all-inorganic perovskite/CIGS 4-T tandem solar cell with a PCE of 24.75%,which exhibits excellent illumination stability by maintaining 86.7%of its initial efficiency after 1400 h.The non-destructive buffer lays the foundation for efficient and stable NIR-transparent inverted inorganic perovskite solar cells and perovskite-based tandems.展开更多
The path planning of Unmanned Aerial Vehicle(UAV)is a critical issue in emergency communication and rescue operations,especially in adversarial urban environments.Due to the continuity of the flying space,complex buil...The path planning of Unmanned Aerial Vehicle(UAV)is a critical issue in emergency communication and rescue operations,especially in adversarial urban environments.Due to the continuity of the flying space,complex building obstacles,and the aircraft's high dynamics,traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the destination.Accordingly,in this paper,we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-Step Experience Buffer Deep Deterministic Policy Gradient(TSEB-DDPG)algorithm.We first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric shapes.After transformation,we propose the Hierarchical Learning Particle Swarm Optimization(HL-PSO)to obtain the empirical path.Then,to ensure the accuracy of the obtained paths,the empirical path,the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance information.The sampling ratio of each buffer is dynamically adapted to the training stages.Moreover,we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path planning.The proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally,and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest accuracy.We also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm,DDPG algorithm,and TSEB-DDPG algorithm.The results show that the TSEBDDPG algorithm can archive almost the best in terms of accuracy,the average time of actual path planning,and the success rate.展开更多
The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orient...The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.展开更多
Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also fa...Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices.Precisely, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time slot.The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.展开更多
Aqueous Zn-based batteries(AZBs)are hindered by issues associated with the Zn electrodeposition process(ZEDP)on electrode surfaces,including passivation,dendrite formation,and hydrogen evolution.One of the important r...Aqueous Zn-based batteries(AZBs)are hindered by issues associated with the Zn electrodeposition process(ZEDP)on electrode surfaces,including passivation,dendrite formation,and hydrogen evolution.One of the important reasons is the drastic fluctuation in the concentration of Zn^(2+)ions on the electrode surface during the charging and discharging process.In this work,an electrolyte with Zn^(2+)ion buffer layer(EZIBL)is proposed to regulate the ZEDP.First,numerical simulations and corresponding experiments are conducted to assess the impact of different thicknesses of the Zn^(2+)ion buffer layer(ZIBL)on the variation in Zn^(2+)ion concentration,from which the optimal thickness of the ZIBL is determined.Then,the regulation role of EZIBL in the cycling process is demonstrated by a Zn-Cu half cell.Further,combined with the potential profile of the symmetric cell and the experimental phenomena,the regulation role of EZIBL in ZEDP is systematically explained at the mechanistic level through the analysis of key parameters.Finally,a full battery composed of Zn-LiMn2O4 is assembled to evaluate the practical applicability of the EZIBL in real battery cycles,which shows great enhancement in capacity retention and coulombic efficiency.This work proposes the design of the EZIBL used to regulate the ZEDP and provides a simple,low-cost regulation method for the development of high-performance AZBs.展开更多
Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream ...Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of VR.In this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission.To support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint prediction.The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content.Second,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client buffer.Then,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are presented.Finally,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.展开更多
Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of ...Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.展开更多
The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and u...The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and uncertainties.This study introduces two order dispatching strategies,namely task buffering and dynamic batching,as potential solutions to address these challenges.The task buffering strategy aims to optimize the assignment timing of orders to couriers,thereby mitigating demand uncertainties.On the other hand,the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery dis tances.To model the instant delivery problem and evaluate the performances of order dis patching strategies,Adaptive Agent-Based Order Dispatching(ABOD)approach is developed,which combines agent-based modelling,deep reinforcement learning,and the Kuhn-Munkres algorithm.The ABOD effectively captures the system’s uncertainties and heterogeneity,facilitating stakeholders learning in novel scenarios and enabling adap tive task buffering and dynamic batching decision-makings.The efficacy of the ABOD approach is verified through both synthetic and real-world case studies.Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction,up to 275.42%,while simultaneously reducing the deliv ery distance by 11.38%compared to baseline policies.Additionally,the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios.As a result,this approach offers valuable sup port to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.展开更多
This study explored the performances of CZTS-based thin-film solar cell with three novel buffer layer materials ZnS, CdS, and CdZnS, as well as with variation in thickness of buffer and absorber-layer, doping concentr...This study explored the performances of CZTS-based thin-film solar cell with three novel buffer layer materials ZnS, CdS, and CdZnS, as well as with variation in thickness of buffer and absorber-layer, doping concentrations of absorber-layer material and operating temperature. Our aims focused to identify the most optimal thin-film solar cell structure that offers high efficiency and lower toxicity which are desirable for sustainable and eco-friendly energy sources globally. SCAPS-1D, widely used software for modeling and simulating solar cells, has been used and solar cell fundamental performance parameters such as open-circuited voltage (), short-circuited current density (), fill-factor() and efficiency() have been optimized in this study. Based on our simulation results, it was found that CZTS solar cell with Cd<sub>0.4</sub>Zn<sub>0.6</sub>S as buffer-layer offers the most optimal combination of high efficiency and lower toxicity in comparison to other structure investigated in our study. Although the efficiency of Cd<sub>0.4</sub>Zn<sub>0.6</sub>S, ZnS and CdS are comparable, Cd<sub>0.4</sub>Zn<sub>0.6</sub>S is preferable to use as buffer-layer for its non-toxic property. In addition, evaluation of performance as a function of buffer-layer thickness for Cd<sub>0.4</sub>Zn<sub>0.6</sub>S, ZnS and CdS showed that optimum buffer-layer thickness for Cd<sub>0.4</sub>Zn<sub>0.6</sub>S was in the range from 50 to 150nm while ZnS offered only 50 – 75 nm. Furthermore, the temperature dependence performance parameters evaluation revealed that it is better to operate solar cell at temperature 290K for stable operation with optimum performances. This study would provide valuable insights into design and optimization of nanotechnology-based solar energy technology for minimizing global energy crisis and developing eco-friendly energy sources sustainable and simultaneously.展开更多
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金The work is financially supported by the Defense Industrial Technology Development Program under the Grant JCKY2018601B106 and JCKY2017205B032.
文摘When humans jump down from a high position,there is a risk of serious injury to the lower limbs.However,cats can jump down from the same heights without any injury because of their excellent ability to attenuate impact forces.The present study aims to investigate the macro/micro biomechanical features of paw pads and limb bones of cats,and the coordination control of joints during landing,providing insights into how cats protect themselves from landing injury.Accordingly,histological analysis,radiological analysis,finite element method,and mechanical testing were performed to investigate the mechanical properties,microstructure,and biomechanical response of the pads and limb bones.In addition,using a motion capture system,the kinematic/kinetic data during landing were analysed based on inverse dynamics.The results show that the pads and limb bones are major contributors to non-impact-injuries,and cats actively couple their joints to adjust the parameters of movement to dissipate the higher impact.Therefore,the paw pads,limb bones,and coordinated joints complement each other and constitute a multi-level buffering mechanism,providing the cat with the sophisticated shock absorption system.This biomechanical analysis can accordingly provide biological inspiration for new approaches to prevent human lower limb injuries.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077232 and 42077235)the Key Research and Development Plan of Jiangsu Province(Grant No.BE2022156).
文摘The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.
基金supported in part by the Research on the Application of Multimodal Artificial Intelligence in Diagnosis and Treatment of Type 2 Diabetes under Grant No.2020SK50910in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ60020.
文摘Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.
基金Project supported by the National Natural Science Foundation of China (Grant No.12274108)the Natural Science Foundation of Zhejiang Province,China (Grant Nos.LY23A040008 and LY23A040008)the Basic Scientific Research Project of Wenzhou,China (Grant No.G20220025)。
文摘Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/TaO_(x) structure,which is facilitated by a wedge-shaped HfO_(2)buffer layer.The field-free switching ratio varies with HfO_(2)thickness,reaching optimal performance at 25 nm.This phenomenon is attributed to the lateral anisotropy gradient of the Co layer,which is induced by the wedge-shaped HfO_(2)buffer layer.The thickness gradient of HfO_(2)along the wedge creates a corresponding lateral anisotropy gradient in the Co layer,correlating with the switching ratio.These findings indicate that field-free SOT switching can be achieved through designing buffer layer,offering a novel approach to innovating spin-orbit device.
文摘Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.62005307 and 61975228).
文摘Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.
文摘Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.
基金the financial support by the National Natural Science Foundation of China(No.12272094)the Natural Science Foundation of Fujian Province of China(No.2022J01541)Natural Science Foundation of Hubei Province of China(No.2022CFB441)。
文摘An innovative metallic buffer consisting of series-connected hat-shaped entangled wire mesh damper(EWMD)and parallel springs are proposed in this work to enhance the reliability of engineering equipment.The impact response and the energy dissipation mechanism of hat-shaped EWMD under different quasi-static compression deformations(2-7 mm)and impact heights(100-200 mm)are investigated using experimental and numerical methods.The results demonstrate distinct stages in the quasi-static mechanical characteristics of hat-shaped EWMD,including stiffness softening,negative stiffness,and stiffness hardening.The loss factor gradually increases with increasing compression deformation before entering the stiffness hardening stage.Under impact loads,the hat-shaped EWMD exhibits optimal impact energy absorption when it enters the negative stiffness stage(150 mm),resulting in the best impact isolation effect of metallic buffer.However,the impact energy absorption significantly decreases when hat-shaped EWMD enters the stiffness hardening stage.Interestingly,quasi-static compression analysis after experiencing different impact loads reveals the disappearance of the negative stiffness phenomenon.Moreover,with increasing impact loads,the stiffness hardening point progressively shifts to an earlier stage.
文摘Two methods of irrigation,drip,and sprinkler were studied to determine the response of the Javits green roof to irrigation.The control study was dry unirrigated plots.Drip irrigation consisted of irrigation tubes running through the green roof that would water the soil throughout and sprinkler irrigation used a sprinkler system to irrigate the green roof from above.In all cases,the irrigated roofs had increased the soil moisture,reduced temperatures of both the upper and lower surfaces,reduced growing medium temperatures and reduced air temperatures above the green roof relative to the unirrigated roof.The buffered temperature fluctuations were also studied via air conditioner energy consumption.There was a 28%reduction in air conditioner energy consumption and a 33%reduction in overall energy consumption between dry and irrigated plots.Values of thermal resistance or S were determined for accuracy and for this study,there was little change which is ideal.A series of infra-red and thermal probe measurements were used to determine temperatures in the air and sedum.It was determined that the sprinkler irrigation did a better job than the drip irrigation in keeping cooler temperatures within the green roof.A Mann-Whitney U test was performed to verify the variation in moisture temperatures buffering energy consumption.By getting a p-value<0.05,it indicates that the model is accurate for prediction and medium temperatures were statistically different.
基金financially supported by the National Natural Science Foundation of China (22279083,22109166,52202183)Guangdong Basic and Applied Basic Research Foundation (Grant No.2019A1515011136,2022B1515120006,2023B1515120041,2414050001473)+3 种基金Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded SchemeGuangdong Provincial Key Laboratory Program (2021B1212040001)from the Department of Science and Technology of Guangdong ProvinceBeijing Institute of TechnologySongshan Lake Materials Laboratory。
文摘Near-infrared(NIR)transparent inverted all-inorganic perovskite solar cells(PSCs)are excellent top cell candidates in tandem applications.An essential challenge is the replacement of metal contacts with transparent conductive oxide(TCO)electrodes,which requires the introduction of a buffer layer to prevent sputtering damage.In this study,we show that the conventional buffers(i.e.,small organic molecules and atomic layer deposited metal oxides)used for organic-inorganic hybrid perovskites are not applicable to all-inorganic perovskites,due to non-uniform coverage of the vulnerable layers underneath,deterioration upon ion bombardment and moisture induced perovskite phase transition,A thin film of metal oxide nanoparticles by the spin-coating method serves as a non-destructive buffer layer for inorganic PSCs.All-inorganic inverted near-infrared-transparent PSCs deliver a PCE of 17.46%and an average transmittance of 73.7%between 780 and 1200 nm.In combination with an 18.56%Cu(In,Ga)Se_(2) bottom cell,we further demonstrate the first all-inorganic perovskite/CIGS 4-T tandem solar cell with a PCE of 24.75%,which exhibits excellent illumination stability by maintaining 86.7%of its initial efficiency after 1400 h.The non-destructive buffer lays the foundation for efficient and stable NIR-transparent inverted inorganic perovskite solar cells and perovskite-based tandems.
基金supported in part by the Hubei Provincial Science and Technology Major Project of China(Grant No.2020AEA011)in part by the National Ethnic Affairs Commission of the People’s Republic of China(Training Program for Young and Middle-aged Talents)(No:MZR20007)+4 种基金in part by the National Natural Science Foundation of China(Grant No.61902437)in part by the Hubei Provincial Natural Science Foundation of China(Grant No.2020CFB629)in part by the Application Foundation Frontier Project of Wuhan Science and Technology Program(Grant No.2020020601012267)in part by the Fundamental Research Funds for the Central Universities,South-Central MinZu University(No:CZQ21026)in part by the Special Project on Regional Collaborative Innovation of Xinjiang Uygur Autonomous Region(Plan to Aid Xinjiang with Science and Technology)(2022E02035)。
文摘The path planning of Unmanned Aerial Vehicle(UAV)is a critical issue in emergency communication and rescue operations,especially in adversarial urban environments.Due to the continuity of the flying space,complex building obstacles,and the aircraft's high dynamics,traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the destination.Accordingly,in this paper,we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-Step Experience Buffer Deep Deterministic Policy Gradient(TSEB-DDPG)algorithm.We first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric shapes.After transformation,we propose the Hierarchical Learning Particle Swarm Optimization(HL-PSO)to obtain the empirical path.Then,to ensure the accuracy of the obtained paths,the empirical path,the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance information.The sampling ratio of each buffer is dynamically adapted to the training stages.Moreover,we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path planning.The proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally,and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest accuracy.We also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm,DDPG algorithm,and TSEB-DDPG algorithm.The results show that the TSEBDDPG algorithm can archive almost the best in terms of accuracy,the average time of actual path planning,and the success rate.
文摘The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.
基金National Natural Science Foundation of China(No.61902060)Shanghai Sailing Program,China(No.19YF1402100)+1 种基金Fundamental Research Funds for the Central Universities,China(No.2232019D3-51)Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications,China)(No.SKLNST-2021-1-06)。
文摘Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices.Precisely, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time slot.The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.
基金funding support from National Innovative Talents Program(GG2090007001)USTC Startup Program(KY2090000044).
文摘Aqueous Zn-based batteries(AZBs)are hindered by issues associated with the Zn electrodeposition process(ZEDP)on electrode surfaces,including passivation,dendrite formation,and hydrogen evolution.One of the important reasons is the drastic fluctuation in the concentration of Zn^(2+)ions on the electrode surface during the charging and discharging process.In this work,an electrolyte with Zn^(2+)ion buffer layer(EZIBL)is proposed to regulate the ZEDP.First,numerical simulations and corresponding experiments are conducted to assess the impact of different thicknesses of the Zn^(2+)ion buffer layer(ZIBL)on the variation in Zn^(2+)ion concentration,from which the optimal thickness of the ZIBL is determined.Then,the regulation role of EZIBL in the cycling process is demonstrated by a Zn-Cu half cell.Further,combined with the potential profile of the symmetric cell and the experimental phenomena,the regulation role of EZIBL in ZEDP is systematically explained at the mechanistic level through the analysis of key parameters.Finally,a full battery composed of Zn-LiMn2O4 is assembled to evaluate the practical applicability of the EZIBL in real battery cycles,which shows great enhancement in capacity retention and coulombic efficiency.This work proposes the design of the EZIBL used to regulate the ZEDP and provides a simple,low-cost regulation method for the development of high-performance AZBs.
基金supported in part by the Chongqing Municipal Education Commission projects under Grant No.KJCX2020035,KJQN202200829Chongqing Science and Technology Commission projects under grant No.CSTB2022BSXM-JCX0117 and cstc2020jcyjmsxmX0339+1 种基金supported in part by National Natural Science Foundation of China under Grant No.(62171072,62172064,62003067,61901067)supported in part by Chongqing Technology and Business University projects under Grant no.(2156004,212017).
文摘Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of VR.In this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission.To support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint prediction.The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content.Second,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client buffer.Then,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are presented.Finally,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.
基金Achievements of Sichuan Fine Arts Institute Education and Teaching Reform Research Project“Construction of Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities”(2024jg10)。
文摘Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.
基金This work was supported in part by the National Natural Science Foundation of China[72101188]the Shanghai Municipal Science and Technology Major Project[2021SHZDZX0100]the Fundamental Research Funds for the Central Universities.
文摘The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and uncertainties.This study introduces two order dispatching strategies,namely task buffering and dynamic batching,as potential solutions to address these challenges.The task buffering strategy aims to optimize the assignment timing of orders to couriers,thereby mitigating demand uncertainties.On the other hand,the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery dis tances.To model the instant delivery problem and evaluate the performances of order dis patching strategies,Adaptive Agent-Based Order Dispatching(ABOD)approach is developed,which combines agent-based modelling,deep reinforcement learning,and the Kuhn-Munkres algorithm.The ABOD effectively captures the system’s uncertainties and heterogeneity,facilitating stakeholders learning in novel scenarios and enabling adap tive task buffering and dynamic batching decision-makings.The efficacy of the ABOD approach is verified through both synthetic and real-world case studies.Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction,up to 275.42%,while simultaneously reducing the deliv ery distance by 11.38%compared to baseline policies.Additionally,the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios.As a result,this approach offers valuable sup port to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.
文摘This study explored the performances of CZTS-based thin-film solar cell with three novel buffer layer materials ZnS, CdS, and CdZnS, as well as with variation in thickness of buffer and absorber-layer, doping concentrations of absorber-layer material and operating temperature. Our aims focused to identify the most optimal thin-film solar cell structure that offers high efficiency and lower toxicity which are desirable for sustainable and eco-friendly energy sources globally. SCAPS-1D, widely used software for modeling and simulating solar cells, has been used and solar cell fundamental performance parameters such as open-circuited voltage (), short-circuited current density (), fill-factor() and efficiency() have been optimized in this study. Based on our simulation results, it was found that CZTS solar cell with Cd<sub>0.4</sub>Zn<sub>0.6</sub>S as buffer-layer offers the most optimal combination of high efficiency and lower toxicity in comparison to other structure investigated in our study. Although the efficiency of Cd<sub>0.4</sub>Zn<sub>0.6</sub>S, ZnS and CdS are comparable, Cd<sub>0.4</sub>Zn<sub>0.6</sub>S is preferable to use as buffer-layer for its non-toxic property. In addition, evaluation of performance as a function of buffer-layer thickness for Cd<sub>0.4</sub>Zn<sub>0.6</sub>S, ZnS and CdS showed that optimum buffer-layer thickness for Cd<sub>0.4</sub>Zn<sub>0.6</sub>S was in the range from 50 to 150nm while ZnS offered only 50 – 75 nm. Furthermore, the temperature dependence performance parameters evaluation revealed that it is better to operate solar cell at temperature 290K for stable operation with optimum performances. This study would provide valuable insights into design and optimization of nanotechnology-based solar energy technology for minimizing global energy crisis and developing eco-friendly energy sources sustainable and simultaneously.
