Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges.Concurrently,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human lives.This paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain systems.By integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be achieved.Moreover,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated data.The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain data.This platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy.The results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.

Lightweight innovation ADIs help development of renewable energy and new technology industries in China

The world is undergoing profound changes in energy and technology.Countries are vigorously developing new sustainable energy sources and technologies.Renewable energy sources encompass various technologies such as wind turbines,solar energy,nuclear energy,and bioenergy.Additionally,emerging technology fields include new energy vehicles,robots,and artificial intelligence devices,among others.The renewable energy industries and implementation of new technologies necessitate the development and adoption of new equipment and components.Austempered ductile iron(ADI)is renowned for its unique microstructure and superior properties.By utilizing ADI,lightweight and innovative castings can be designed to not only reduce weight but also save energy and decrease emissions.More importantly,these castings enhance the efficiency and reliability of new energy equipment and emerging technology installations.This paper describes the development,applications,and future prospects of lightweight and innovative ADI castings within sectors such as solar photovoltaic(PV),wind power generation,industry robots,and trucks in China.

A Lightweight, Searchable, and Controllable EMR Sharing Scheme

Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharingscheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computationaloverhead of encryption and decryption reaches a lightweight constant level, and supports keyword search andpolicy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technologyis utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the datato achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions(SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology isutilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflectthe obvious advantages of the scheme in terms of encryption and decryption computation overhead and storageoverhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solutionfor the safe and efficient sharing of EMR.

Lightweight Multi-Resolution Network for Human Pose Estimation

Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,humanpose estimation has achieved great success in multiple fields such as animation and sports.However,to obtainaccurate positioning results,existing methods may suffer from large model sizes,a high number of parameters,and increased complexity,leading to high computing costs.In this paper,we propose a new lightweight featureencoder to construct a high-resolution network that reduces the number of parameters and lowers the computingcost.We also introduced a semantic enhancement module that improves global feature extraction and networkperformance by combining channel and spatial dimensions.Furthermore,we propose a dense connected spatialpyramid pooling module to compensate for the decrease in image resolution and information loss in the network.Finally,ourmethod effectively reduces the number of parameters and complexitywhile ensuring high performance.Extensive experiments show that our method achieves a competitive performance while dramatically reducing thenumber of parameters,and operational complexity.Specifically,our method can obtain 89.9%AP score on MPIIVAL,while the number of parameters and the complexity of operations were reduced by 41%and 36%,respectively.

Vibration Control of the Rail Grinding Vehicle with Abrasive Belt Based on Structural Optimization and Lightweight Design

As a new grinding and maintenance technology,rail belt grinding shows significant advantages in many applications The dynamic characteristics of the rail belt grinding vehicle largely determines its grinding performance and service life.In order to explore the vibration control method of the rail grinding vehicle with abrasive belt,the vibration response changes in structural optimization and lightweight design are respectively analyzed through transient response and random vibration simulations in this paper.Firstly,the transient response simulation analysis of the rail grinding vehicle with abrasive belt is carried out under operating conditions and non-operating conditions.Secondly,the vibration control of the grinding vehicle is implemented by setting vibration isolation elements,optimizing the structure,and increasing damping.Thirdly,in order to further explore the dynamic characteristics of the rail grinding vehicle,the random vibration simulation analysis of the grinding vehicle is carried out under the condition of the horizontal irregularity of the American AAR6 track.Finally,by replacing the Q235 steel frame material with 7075 aluminum alloy and LA43M magnesium alloy,both vibration control and lightweight design can be achieved simultaneously.The results of transient dynamic response analysis show that the acceleration of most positions in the two working conditions exceeds the standard value in GB/T 17426-1998 standard.By optimizing the structure of the grinding vehicle in three ways,the average vibration acceleration of the whole car is reduced by about 55.1%from 15.6 m/s^(2) to 7.0 m/s^(2).The results of random vibration analysis show that the grinding vehicle with Q235 steel frame does not meet the safety conditions of 3σ.By changing frame material,the maximum vibration stress of the vehicle can be reduced from 240.7 MPa to 160.0 MPa and the weight of the grinding vehicle is reduced by about 21.7%from 1500 kg to 1175 kg.The modal analysis results indicate that the vibration control of the grinding vehicle can be realized by optimizing the structure and replacing the materials with lower stiffness under the premise of ensuring the overall strength.The study provides the basis for the development of lightweight,diversified and efficient rail grinding equipment.

GL-YOLOv5: An Improved Lightweight Non-Dimensional Attention Algorithm Based on YOLOv5

Craniocerebral injuries represent the primary cause of fatalities among riders involved in two-wheeler accidents;nevertheless,the prevalence of helmet usage among these riders remains alarmingly low.Consequently,the accurate identification of riders who are wearing safety helmets is of paramount importance.Current detection algorithms exhibit several limitations,including inadequate accuracy,substantial model size,and suboptimal performance in complex environments with small targets.To address these challenges,we propose a novel lightweight detection algorithm,termed GL-YOLOv5,which is an enhancement of the You Only Look Once version 5(YOLOv5)framework.This model incorporates a Global DualPooling NoReduction Blend Attention(GDPB)module,which optimizes the MobileNetV3 architecture by reducing the number of channels by half and implementing a parallelized channel and spatial attention mechanism without dimensionality reduction.Additionally,it replaces the conventional convolutional layer with a channel shuffle approach to overcome the constraints associated with the Squeeze-and-Excitation(SE)attention module,thereby significantly improving both the efficiency and accuracy of feature extraction and decreasing computational complexity.Furthermore,we have optimized the Variable Normalization and Attention Channel Spatial Partitioning(VNACSP)within the C3 module of YOLOv5,which enhances sensitivity to small targets through the application of a lightweight channel attention mechanism,substituting it for the standard convolution in the necking network.The Parameter-Free Spatial Adaptive Feature Fusion(PSAFF)module is designed to adaptively modify the weights of each spatial position through spatial pooling and activation functions,thereby effectively enhancing the model’s ability to perceive contextual information over distances.Ultimately,GL-YOLOv5 performs remarkably in the custom dataset,achieving a model parameter count of 922,895 M,a computational load of 2.9 GFLOPS,and a mean average precision(mAP)of 92.1%.These advancements significantly improve the model’s detection capabilities and underscore its potential for practical applications.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

With the advancement of wireless network technology,vast amounts of traffic have been generated,and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated.While signature-based detection methods,static analysis,and dynamic analysis techniques have been previously explored for malicious traffic detection,they have limitations in identifying diversified malware traffic patterns.Recent research has been focused on the application of machine learning to detect these patterns.However,applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process.In this study,we examined methods for effectively utilizing machine learning-based malicious traffic detection approaches for lightweight devices.We introduced the suboptimal feature selection model(SFSM),a feature selection technique designed to reduce complexity while maintaining the effectiveness of malicious traffic detection.Detection performance was evaluated on various malicious traffic,benign,exploits,and generic,using the UNSW-NB15 dataset and SFSM sub-optimized hyperparameters for feature selection and narrowed the search scope to encompass all features.SFSM improved learning performance while minimizing complexity by considering feature selection and exhaustive search as two steps,a problem not considered in conventional models.Our experimental results showed that the detection accuracy was improved by approximately 20%compared to the random model,and the reduction in accuracy compared to the greedy model,which performs an exhaustive search on all features,was kept within 6%.Additionally,latency and complexity were reduced by approximately 96%and 99.78%,respectively,compared to the greedy model.This study demonstrates that malicious traffic can be effectively detected even in lightweight device environments.SFSM verified the possibility of detecting various attack traffic on lightweight devices.

Unstructured Road Extraction in UAV Images based on Lightweight Model

There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.

YOLOv5ST:A Lightweight and Fast Scene Text Detector

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

Experimental study of 3D printed carbon fibre sandwich structures for lightweight applications

Honeycomb sandwich structures are widely used in lightweight applications.Usually,these structures are subjected to extreme loading conditions,leading to potential failures due to delamination and debonding between the face sheet and the honeycomb core.Therefore,the present study is focused on the mechanical characterisation of honeycomb sandwich structures fabricated using advanced 3D printing technology.The continuous carbon fibres and ONYX-FR matrix materials have been used as raw materials for 3D printing of the specimens needed for various mechanical characterization testing;ONYX-FR is a commercial trade name for flame retardant short carbon fibre filled nylon filaments,used as a reinforcing material in Morkforged 3D printer.Edgewise and flatwise compression tests have been conducted for different configurations of honeycomb sandwich structures,fabricated by varying the face sheet thickness and core cell size,while keeping the core cell thickness and core height constant.Based on these tests,the proposed structure with face sheet thickness of 3.2 mm and a core cell size of 12.7 mm exhibited the highest energy absorption and prevented delamination and debonding failures.Therefore,3D printing technology can also be considered as an alternative method for sandwich structure fabrication.However,detailed parametric studies still need to be conducted to meet various other structural integrity criteria related to the lightweight applications.

Design,preparation,microstructure and mechanical property of the lightweight radiation-shielding Mg-Ta-Al composites basing differential temperature hot rolling

A novel lightweight,radiation-shielding Mg-Ta-Al layered metal-matrix composite(LMC)was successful designed by doping the extremely refractory metal(Ta)into Mg sheets.These Mg-based LMCs sheets shows excellent radiation-dose shield effect,about 145 krad·a^(−1),which is about 17 times of traditional Mg alloy,while its surface density is only about 0.9 g·cm^(−2),reducing by 60%than that of pure Ta.The quantitate relationship between radiation-dose and the materials’thickness was also confirmed to the logistic function when the surface density is in the range of 0.6-1.5 g·cm^(−2).Meantime,the rolling parameters,interface microstructure and mechanical properties in both as-rolled and annealing treated samples were evaluated.The sheets possess a special dissimilar atoms diffusion transitional zone containing an obvious inter-diffusion Mg-Al interface and the unique micro-corrugated Ta-Al interface,as well as a thin Al film with a thickness of about 10μm.The special zone could reduce the stress concentration and enhance the strength of Mg-Ta-Al LMCs.The interface bonding strength reaches up to 54-76 MPa.The ultimate tensile strength(UTS)and yield strength(TYS)of the Mg-Ta-Al sheet were high to 413 MPa and 263 MPa,respectively,along with an elongation of 5.8%.The molecular dynamics(MD)analysis results show that the two interfaces exhibit different formation mechanism,the Mg-Al interface primarily depended on Mg/Al atoms diffusion basing point defects movement,while the Ta-Al interface with a micro-interlock pining shape formed by close-packed planes slipping during high temperature strain-induced deformation process.

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

Emotion Detection Using ECG Signals and a Lightweight CNN Model

Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Dynamic failure process of expanded polystyrene particle lightweight soil under cyclic loading using discrete element method

Expanded polystyrene(EPS)particle-based lightweight soil,which is a type of lightweight filler,is mainly used in road engineering.The stability of subgrades under dynamic loading is attracting increased research attention.The traditional method for studying the dynamic strength characteristics of soils is dynamic triaxial testing,and the discrete element simulation of lightweight soils under cyclic load has rarely been considered.To study the meso-mechanisms of the dynamic failure processes of EPS particle lightweight soils,a discrete element numerical model is established using the particle flow code(PFC)software.The contact force,displacement field,and velocity field of lightweight soil under different cumulative compressive strains are studied.The results show that the hysteresis curves of lightweight soil present characteristics of strain accumulation,which reflect the cyclic effects of the dynamic load.When the confining pressure increases,the contact force of the particles also increases.The confining pressure can restrain the motion of the particle system and increase the dynamic strength of the sample.When the confining pressure is held constant,an increase in compressive strain causes minimal change in the contact force between soil particles.However,the contact force between the EPS particles decreases,and their displacement direction points vertically toward the center of the sample.Under an increase in compressive strain,the velocity direction of the particle system changes from a random distribution and points vertically toward the center of the sample.When the compressive strain is 5%,the number of particles deflected in the particle velocity direction increases significantly,and the cumulative rate of deformation in the lightweight soil accelerates.Therefore,it is feasible to use 5%compressive strain as the dynamic strength standard for lightweight soil.Discrete element methods provide a new approach toward the dynamic performance evaluation of lightweight soil subgrades.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different fields.Physical settings are probed for data,which is then transferred via linked networks.There are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny sensors.When it comes to deploying IoT,everyone agrees that security is the biggest issue.This is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square footage.This research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted circumstances.Using lightweight cryptography,the information sent between these gadgets may be secured.In order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance features.We also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

A Novel YOLOv5s-Based Lightweight Model for Detecting Fish’s Unhealthy States in Aquaculture

Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.

Effects of Using Municipal Solid Waste Incineration Tailings as Lightweight Aggregate on the Mechanical Properties of Specified Density Concrete

Municipal solid waste incineration tailings were used as lightweight aggregate(MSWIT-LA)in the preparation of specified density concrete to study the effects on compressive strength,axial compressive strength,flexural strength,microhardness,total number of pores,pore area,and pore spacing.The results showed that the internal curing and morphological effects induced by an appropriate quantity of MSWIT-LA improved the compressive response of specified density concrete specimens,whereas an excessive quantity of MSWIT-LA significantly reduced their mechanical properties.An analysis of pore structure indicated that the addition of MSWIT-LA increased the total quantity of pores and promoted cement hydration,resulting in a denser microstructure than that of ordinary concrete.The results of a principal component analysis showed that the mechanical response of specified density concrete prepared with 25%MSWIT-LA was superior to that of an equivalent ordinary concrete.It was therefore concluded that MSWIT-LA can be feasibly applied to achieve excellent specified density concrete properties while utilising municipal solid waste incineration tailings to protect the environment and alleviate shortages of sand and gravel resources.