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We present the Manatee variational autoencoder model to predict transcription factor (TF) perturbation-induced transcriptomes. We demonstrate that the Manatee in silico perturbation analysis recapitulates target transcriptomic phenotypes in diverse cellular lineage transitions. We further propose the Manatee in silico screening analysis for prioritizing TF combinations targeting desired transcriptomic phenotypes.

The optical microscope algorithm (OMA) is a metaheuristic algorithm that draws inspiration from the magnifying functionality of optical microscopes. This study introduces an enhanced variant of OMA, termed PMSOMA, designed to mitigate the original version's limitations, notably its slow convergence rates and vulnerability to local optima. PMSOMA integrates a piecewise linear chaotic map to refine population initialization and augment diversity, alongside a sparse adaptive exploration mechanism to bolster search efficacy. The performance of PMSOMA was rigorously tested using a suite of 50 benchmark functions, the CEC2017 test suite, feature selection datasets, and three classical engineering challenges. The empirical findings confirm that PMSOMA surpasses both the original OMA and competing algorithms by delivering superior solutions, accelerating convergence, and demonstrating enhanced robustness in convergence. [ABSTRACT FROM AUTHOR]

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications. [ABSTRACT FROM AUTHOR]

Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models. [ABSTRACT FROM AUTHOR]

With the rapid advancement of drone technology and the growing applications in the field of drone engineering, the demand for precise and efficient path planning in complex and dynamic environments has become increasingly important. Traditional algorithms struggle with complex terrain, obstacles, and weather changes, often falling into local optima. This study introduces an Improved Crown Porcupine Optimizer (ICPO) for drone path planning, which enables drones to better avoid obstacles, optimize flight paths, and reduce energy consumption. Inspired by porcupines' defense mechanisms, a visuo-auditory synergy perspective is adopted, improving early convergence by balancing visual and auditory defenses. The study also employs a good point set population initialization strategy to enhance diversity and eliminates the traditional population reduction mechanism. To avoid local optima in later stages, a novel periodic retreat strategy inspired by porcupines' precise defenses is introduced for better position updates. Analysis on the IEEE CEC2022 test set shows that ICPO almost reaches the optimal value, demonstrating robustness and stability. In complex mountainous terrain, ICPO achieved optimal values of 778.1775 and 954.0118; in urban terrain, 366.2789 and 910.1682 and ranked first among the compared algorithms, proving its effectiveness and reliability in drone delivery path planning. Looking ahead, the ICPO will provide greater efficiency and safety for drone path planning in navigating complex environments. [ABSTRACT FROM AUTHOR]

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Remote Sensing Image Object Detection (RSIOD) faces the challenges of multi-scale objects, dense overlap of objects and uneven data distribution in practical applications. In order to solve these problems, this paper proposes a YOLO-ACPHD RSIOD algorithm. The algorithm adopts Adaptive Condition Awareness Technology (ACAT), which can dynamically adjust the parameters of the convolution kernel, so as to adapt to the objects of different scales and positions. Compared with the traditional fixed convolution kernel, this dynamic adjustment can better adapt to the diversity of scale, direction and shape of the object, thus improving the accuracy and robustness of Object Detection (OD). In addition, a High-Dimensional Decoupling Technology (HDDT) is used to reduce the amount of calculation to 1/N by performing deep convolution on the input data and then performing spatial convolution on each channel. When dealing with large-scale Remote Sensing Image (RSI) data, this reduction in computation can significantly improve the efficiency of the algorithm and accelerate the speed of OD, so as to better adapt to the needs of practical application scenarios. Through the experimental verification of the RSOD RSI data set, the YOLO-ACPHD model in this paper shows very satisfactory performance. The F1 value reaches 0.99, the Precision value reaches 1, the Precision-Recall value reaches 0.994, the Recall value reaches 1, and the mAP value reaches 99.36 % , which indicates that the model shows the highest level in the accuracy and comprehensiveness of OD. [ABSTRACT FROM AUTHOR]

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness. [ABSTRACT FROM AUTHOR]

In response to the issues of low merging success rates and poor safety in the on-ramp merging scenario within autonomous driving, we propose an on-ramp merging model for unmanned vehicles based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. Firstly, we introduce an Action-Mask (AM) to prevent the sampling of invalid actions during merging, thus enhancing safety by ensuring only valid actions are considered. Secondly, we incorporate noise advantage values to encourage unmanned vehicles to thoroughly explore the environment and avoid being trapped in local optimal solutions. Experimental results demonstrate that the AM-MAPPO algorithm model improves both safety and traffic efficiency. [ABSTRACT FROM AUTHOR]

The reliability and safety of locomotives is crucial for efficient train operation. Repeated turbocharger failures in Israel Railways locomotive fleet have raised serious safety concerns. An investigation into the failures revealed that the uncontrolled acceleration and overspeed transients of the turbocharger shaft occurred before the failure. Early detection of potential turbocharger failures by predicting overspeed conditions is critical to the safety and reliability of locomotives. In this study, an enhanced novel algorithm for estimating the Instantaneous Angular Speed (IAS) of the turbocharger and diesel engines is presented to overcome the challenges of transient operating conditions of diesel engines. Using adaptive dephasing, the algorithm effectively isolates critical asynchronous vibration components that are crucial for the early detection of turbocharger failures. This algorithm is suitable for non-stationary speeds and is applicable to any range of rotational speed and rate of change. The algorithm requires the input of the basic parameters of the system, while all other parameters that control the process are determined automatically. The algorithm was developed specifically for the special operating conditions of diesel engines and improves predictive maintenance and operational reliability. The method is robust as it correlates between several characteristic frequencies of the rotating parts of the system. The algorithm was verified and validated with simulated and experimental data. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%. [ABSTRACT FROM AUTHOR]

Based on the polynomial theory, the error propagation characteristics of the widely used N-step discrete Fourier transform (N-DFT) phase-shift algorithm were analyzed via theoretical analysis, under the effect of Gamma distortion and phase detuning. The results showed that the N-DFT algorithm could not simultaneously suppress both types of error. A robust linear phase-shift (RLPS) algorithm was designed, the performance of the RLPS and 8-DFT algorithms in terms of spectral response, detuning robustness, and G S / N was briefly analysis by Manuel Servin method. The Simulation analysis and comparison of the results show that the RLPS algorithm could suppress both types of error simultaneously, which exhibited better stability and accuracy than N-DFT and exponential algorithms, particularly in gradient measurement stability, peak-to-valley (PV) and root-mean-square (RMS) error suppression. Moreover, a physical experiment apparatus was built to test unidirectionally inclined plane mirror and concave mirror using the RLPS, N-DFT, and exponential algorithms. The results showed that the RLPS algorithm could significantly improve the measurement stability and accuracy in the presence of detuning and without screen Gamma calibration. [ABSTRACT FROM AUTHOR]

Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together. [ABSTRACT FROM AUTHOR]

Based on double-compressed sampling, a hyperspectral spectral unmixing algorithm (SU_DCS) is proposed, which could directly complete the endmember extraction and abundance estimation. On the basis of the linear mixed model (LMM), we designed spatial and spectral sampling matrices, obtained spatial and spectral measurement data, and constructed a joint unmixing model containing endmember and abundance information. By using operator separation and Lagrangian multiplier algorithm, the endmember matrix, abundance matrix and remixing image can be quickly obtained by matrix operation. The parameters of the unmixing algorithm, including regularization parameter, convergence threshold and spatial sampling rate, are determined using synthetic simulated hyperspectral data. The proposed algorithm is applied to two kinds of real hyperspectral data, with or without ground truth, in order to verify the effectiveness and reliability of the algorithm. Firstly, we provide the performance of the algorithm on real datasets without ground truth. Compared with algorithm VCA_FCLS and algorithm CPPCA_VCA_FCLS, the endmember spectral curve extracted by the proposed SU_DCS is almost consistent with that obtained by VCA_FCLS, and is more smooth than that of obtained by CPPCA_VCA_FCLS. Additionally, the abundance estimation map estimated by the SU_DCS has consistency with the results obtained by VCA_FCLS. Moreover, the proposed SU_DCS has higher peak signal-to-noise ratio (PSNR) for remixing images with higher computational efficiency. Secondly, we provide the performance of the proposed algorithm on four real datasets with ground truth, including dataset Cuprite, dataset Samson, dataset Jasper and dataset Urban. We provide the results of endmember extraction and abundance estimation from the compressed data under different sampling rate conditions. The extracted endmember maintains good consistency with the true spectral curves, and the estimated abundance map can also maintain good spatial consistency with the ground truth. The comparison results with other four comparative algorithms also indicate that the proposed algorithm can obtain relatively accurate endmembers and abundance information from compressed data, the reliability and validity of the proposed algorithm have been proved. In summary, the main innovation of the proposed algorithm is that it can extract endmembers and estimate abundance with high accuracy from a small amount of measurement data. [ABSTRACT FROM AUTHOR]

Consensus plays a crucial role in blockchain technology, with the deleted proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link–delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism. [ABSTRACT FROM AUTHOR]

The Common-path Coherent-dispersion Spectrometer (CODES), an exoplanet detection instrument, executes high-precision Radial Velocity (RV) inversions by recording the phase shifts of interference fringes. Salt-and-pepper noise caused by factors such as improper operation of the CCD probe/analog-to-digital converter and strong dark currents may interfere with the phase information of the fringe. This lowers the quality of the interfering fringe image and significantly interferes with the RV's inversion. In this study, an adaptive median filtering algorithm (CODESmF) based on submaximum and subminimum values is designed to eliminate the interference fringe image's salt-and-pepper noise as well as to reduce RV error. This allows the interference fringe image's phase information to be retained more completely. The algorithm consists of two major modules. Pixel Sub-extreme-based Filtered Noise Monitoring Module: discriminates signal pixels and noise pixels based on the submaximum and subminimum values of the pixels in the filtering window. Adaptive Median Filter Noise Suppression Module: the signal pixel is kept at the original value output, the noise pixel serves as the filtering window's center pixel, and the adaptive median filtering procedure is repeated numerous times with various filtering window sizes. According to the experimental findings, the CODESmF outperforms comparable algorithms and works better at recovering interference fringes. More than 90% of the phase/RV error caused by salt-and-pepper noise is typically eliminated by the CODESmF algorithm, and in certain circumstances, it can even remove roughly 98% of the phase error. [ABSTRACT FROM AUTHOR]

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG. [ABSTRACT FROM AUTHOR]

In the realm of e-commerce, personalized recommendations are a crucial component in enhancing user experience and optimizing sales efficiency. To address the inherent sparsity challenge prevalent in collaborative filtering algorithms within personalized recommendation systems, we propose a novel hybrid e-commerce recommendation algorithm based on the User-Nearest-Neighbor model. By integrating the user nearest neighbor model with other recommendation algorithms, this approach effectively mitigates data sparsity and facilitates a more nuanced understanding of the user-product relationship, consequently elevating recommendation quality and enhancing user experience. Taking into account considerations such as data scale and recommendation performance, we conducted experiments utilizing the Spark distributed platform. Empirical findings demonstrate the superiority of our hybrid algorithm over standalone collaborative filtering algorithms across various recommendation indicators. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and 5G communication technologies in smart cities deliver promising services for heterogeneous applications. The application reliability banks on uninterrupted and seamless services experienced by the users. However, the increasing smart city application demands influence the experience reliability through augmented wait times. This article therefore introduces a Coherent Reliability Service Broadcasting Technique (CRSBT) for sustaining constructive application services. This technique incorporates linear regressive and digressive learning for application service improvements and restrictions. Based on the demand, the regressive process verifies the wait time and with the reducing demands, the service broadcast ratio is verified. These two factors are verified post the demand and response through 5G resource allocations and IoT computations. Both the service-oriented features are validated for regressive service broadcast and either of the one is used for digressive response. The coherence between the computations (IoT) and resources (5G) is verified on-demand and linearly. Therefore, the proposed technique is reliable in sustaining service broadcast, less wait time, and maximum flexibility. [ABSTRACT FROM AUTHOR]

Due to their simplicity of implementation and compliance with the encryption issue, chaotic models are often utilized in picture encryption applications. Despite having many benefits, this approach still has a crucial space issue that makes encryption algorithms based on it susceptible to brute-force assaults. This research's proposed novel picture encryption technique has a vast key space and great key sensitivity. To achieve this goal, the proposed method combines two-way chaotic maps and reversible cellular automata (RCA). First, this approach uses a two-way chaotic model named spatiotemporal chaos for image confusion. This step includes permuting the image pixels using a chaotic map at the byte level. Then, the RCA model is utilized for image diffusion. In this step, the RCA model iterates over image pixels to modify them at the bit level. The method's performance in encrypting grayscale images was evaluated using various analysis methods. According to the results, the proposed method is a compelling image encryption algorithm with high robustness against brute-force, statistical, and differential attacks. [ABSTRACT FROM AUTHOR]

High-precision 3D point cloud data have various analyses and application use cases. This study aimed to achieve a more precise noise reduction of the raw 3D point cloud data of asphalt pavements obtained using 3D laser scanning. Hence, a noise-reduction algorithm integrating improved Gaussian filtering and coefficient of variation was developed. A portable laser scanner was used to collect raw, high-precision 3D point cloud data of surface textures from pavement slab samples prepared with three different types of asphalt mixtures: AC-13, SMA-13, and OGFC-13, as well as asphalt from the test sections of the Yakang Expressway. An improved Gaussian filtering and Gaussian filtering that extracts noise using the coefficient of variation were used to filter out the obvious outlier noise and small-scale burr noise, respectively. Finally, the filtering effect of the proposed algorithm, Gaussian filtering, median filtering, and mean filtering on raw 3D point cloud data of pavement textures was evaluated through subjective visual quality and objective index evaluations. The results showed that the proposed algorithm filters out noise while preserving the micro-texture structure information, outperforming Gaussian filtering, median filtering, and mean filtering. [ABSTRACT FROM AUTHOR]

Recent rank-based attacks have reduced the security of Rainbow, which is one of the multi-layer UOV signatures, below the NIST security requirements by speeding up iterative kernel-finding operations using classical mathematics techniques. If quantum algorithms are applied to perform these iterative operations, the rank-based attacks may be more threatening to multi-layer UOV, including Rainbow. In this paper, we propose a quantum rectangular MinRank attack called the Q-rMinRank attack, the first quantum approach to key recovery attacks on multi-layer UOV signatures. Our attack is a general model applicable to multi-layer UOV signature schemes, and in this paper, we provide examples of its application to Rainbow and the Korean TTA standard, HiMQ. We design two quantum oracle circuits to find the kernel in consideration of the depth-width trade-off of quantum circuits. One is to reduce the width of the quantum circuits using qubits as a minimum, and the other is to reduce the depth using parallelization instead of using a lot of qubits. By designing quantum circuits to find kernels with fewer quantum resources and complexity by adding mathematical techniques, we achieve quadratic speedup for the MinRank attack to recover the private keys of multi-layer UOV signatures. We also estimate quantum resources for the designed quantum circuits and analyze quantum complexity based on them. The width-optimized circuit recovers the private keys of Rainbow parameter set V with only 1089 logical qubits. The depth-optimized circuit recovers the private keys of Rainbow parameter set V with a quantum complexity of 2 174 , which is lower than the complexity of 2 221 recovering the secret key of AES-192, which provides the same security level as parameter set III. [ABSTRACT FROM AUTHOR]

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks. [ABSTRACT FROM AUTHOR]

In this article, a visual security image encryption algorithm based on compressed sensing is proposed. The algorithm consists of two stages: the compression and encryption stage and the embedding stage. The key streams in the compression and encryption stage are generated by a newly constructed four-dimensional discrete chaotic map, while the Gaussian measurement matrix is generated by a Chebyshev map, and both of their generations are related to the feature code of the carrier image, which enhances the security of the ciphertext. In the compression and encryption stage, a scrambling-cyclic shift-diffusion encryption structure is adopted for the compressed image in which the shift number in the cyclic shift stage and the diffusion key streams are dynamically changed according to each pixel value, so the algorithm can resist chosen plaintext attack. In the embedding stage, the carrier image is first subjected to integer wavelet transform to obtain the high-frequency and low-frequency components of the image, and then the intermediate ciphertext information is embedded into its high-frequency components. Finally, the carrier image is subjected to inverse integer wavelet transform to obtain a visually secure ciphertext image. The experimental results and security analysis indicate that the encryption scheme has a large key space, high decryption key sensitivity, similar histogram distribution between the carrier image and the visual security ciphertext image, and good robustness to noise attacks. [ABSTRACT FROM AUTHOR]

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique's versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further. [ABSTRACT FROM AUTHOR]

Extractive document summary is usually seen as a sequence labeling task, which the summary is formulated by sentences from the original document. However, the selected sentences usually are high redundancy in semantic space, so that the composed summary are high semantic redundancy. To alleviate this problem, we propose a model to reduce the semantic redundancy of summary by introducing the cluster algorithm to select difference sentences in semantic space and we improve the base BERT to score sentences. We evaluate our model and perform significance testing using ROUGE on the CNN/DailyMail datasets compare with six baselines, which include two traditional methods and four state-of-art deep learning model. The results validate the effectiveness of our approach, which leverages K-means algorithm to produce more accurate and less repeat sentences in semantic summaries. [ABSTRACT FROM AUTHOR]

Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low target detection accuracy and high leakage rate of the current traditional weld surface defect detection methods and existing detection models, an improved YOLOv7 pipeline weld surface defect detection model is proposed to improve detection results. In the improved model, a Le-HorBlock module is designed, and it is introduced into the back of fourth CBS module of the backbone network, which preserves the characteristics of high-order information by realizing second-order spatial interaction, thus enhancing the ability of the network to extract features in weld defect images. The coordinate attention (CoordAtt) block is introduced to enhance the representation ability of target features, suppress interference. The CIoU loss function in YOLOv7 network model is replaced by the SIoU, so as to optimize the loss function, reduce the freedom of the loss function, and accelerate convergence. And a new large-scale pipeline weld surface defect dataset containing 2000 images of pipeline welds with weld defects is used in the proposed model. In the experimental comparison, the improved YOLOv7 network model has greatly improved the missed detection rate compared with the original network. The experimental results show that the improved YOLOv7 network model mAP@80.5 can reach 78.6%, which is 15.9% higher than the original model, and the detection effect is better than the original network and other classical target detection networks. [ABSTRACT FROM AUTHOR]

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.

Spectral imaging holds great promise for the non-invasive diagnosis of retinal diseases. However, to acquire a spectral datacube, conventional spectral cameras require extensive scanning, leading to a prolonged acquisition. Therefore, they are inapplicable to retinal imaging because of the rapid eye movement. To address this problem, we built a coded aperture snapshot spectral imaging fundus camera, which captures a large-sized spectral datacube in a single exposure. Moreover, to reconstruct a high-resolution image, we developed a robust deep unfolding algorithm using a state-of-the-art spectral transformer in the denoising network. We demonstrated the performance of the system through various experiments, including imaging standard targets, utilizing an eye phantom, and conducting in vivo imaging of the human retina.

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

Nowadays QKD plays a critical role in unconditionally-secure and quantum-safe key distribution. Commercially available QKD devices are getting more popular for institutional and governmental national and international networks, but are expensive and offer limited key rates. We provide a formalization of QKD-generated key forwarding and redistribution at the KMS level by extending the network graph of physical QKD links to the complete graph with logical links, and we investigate its application on three practical scalable scenarios (all-to-all, one-to-all, one-to-one). We define a maximization goal for each scenario, and provide a linear programming problem statement to compute the optimal redistribution. We perform an extensive analysis of the algorithm in terms of forwarding results and key consumption on simulated QKD networks and discuss the implications of network size and graph topology on the algorithm's performance and complexity. [ABSTRACT FROM AUTHOR]

In quantum computing there are quite a few universal gate sets, each having their own characteristics. In this paper we study the Clifford+CS universal fault-tolerant gate set. The CS gate is used is many applications and this gate set is an important alternative to Clifford+T. We introduce a generating set in order to represent any unitary implementable by this gate set and with this we derive a bound on the CS-count of arbitrary multi-qubit unitaries. Analysing the channel representation of the generating set elements, we infer J n CS ⊂ J n T , where J n CS and J n T are the set of unitaries exactly implementable by the Clifford+CS and Clifford+T gate sets, respectively. We develop CS-count optimal synthesis algorithms for both approximately and exactly implementable multi-qubit unitaries. With the help of these we derive a CS-count-optimal circuit for Toffoli, implying J n Tof = J n CS , where J n Tof is the set of unitaries exactly implementable by the Clifford+Toffoli gate set. Such conclusions can have an important impact on resource estimates of quantum algorithms. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ( Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ( N A B / F ). The average Q A B / F = 0.765508 , S C D = 1.63185 , S S I M = 0.726317 , and N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test. [ABSTRACT FROM AUTHOR]

The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT). The process is carried out by taking images and then organizing it using switching techniques in the presence of square boxes where entire populace is identified on real time experimentations. For processing and controlling the occurrence a separate architecture is designed with analytical equivalences where all data set is stored in cloud platform. Further the incorporation of system model is carried out using Switching Based Algorithm (SBA) which adds more number of columns even for high population cases. In order to verify the effectiveness of proposed model five scenarios are considered with performance evaluation metrics for SBA and all the test results provides best optimal results. Moreover the projected model is improved with an average percentage of 83 as compared to existing models. [ABSTRACT FROM AUTHOR]

Remote sensing image fusion is dedicated to obtain a high-resolution multispectral (HRMS) image without spatial or spectral distortion compared to the single source image. In this paper, a novel fusion algorithm based on Bayesian estimation for remote sensing images is proposed from the new perspective of risk decisions. In this study, an observation model based on Bayesian estimation for remote sensing image fusion is constructed. Three categories of probabilities including prior, conditional and posterior probabilities are calculated after an intensity-hue-saturation (IHS) transformation is applied to the original low-resolution MS image. To obtain the desired HRMS image, with the corrected posterior probability, a fusion rule based on Bayesian decisions is designed to estimate which pixels to select from the panchromatic (PAN) image and the intensity component of the MS image. The selected pixels constitute a new component that will participate in an IHS inverse transformation to yield the fused image. Extensive experiments were performed on the Pleiades, WorldView-3, and IKONOS datasets, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

r, s, t-spherical fuzzy (r, s, t-SPF) sets provide a robust framework for managing uncertainties in decision-making, surpassing other fuzzy sets in their ability to accommodate diverse uncertainties through the incorporation of flexible parameters r, s, and t. Considering these characteristics, this article explores sine trigonometric laws to enhance the applicability and theoretical foundation for r, s, t-SPF setting. Following these laws, several aggregation operators (AOs) are designed for aggregation of the r, s, t-SPF data. Meanwhile, the desired characteristics and relationships of these operators are studied under sine trigonometric functions. Furthermore, we build a group decision-making algorithm for addressing multiple attribute group decision-making (MAGDM) problems using the developed AOs. To exemplify the applicability of the proposed algorithm, we address a practical example regarding laptop selection. Finally, parameter analysis and a comprehensive comparison with existing operators are conducted to uncover the superiority and validity of the presented AOs. [ABSTRACT FROM AUTHOR]

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5–54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high. Bilevel form of triple decomposition (TriD) overcomes this issue by decomposing the Tucker core into three low-dimensional third-order factor tensors and plays an important role in the dimension reduction of data representation. TriD, on the other hand, is incapable of precisely encoding similarity relationships for tensor data with a complex manifold structure. To address this shortcoming, we take advantage of hypergraph learning and propose a novel hypergraph regularized nonnegative triple decomposition for multiway data analysis that employs the hypergraph to model the complex relationships among the raw data. Furthermore, we develop a multiplicative update algorithm to solve our optimization problem and theoretically prove its convergence. Finally, we perform extensive numerical tests on six real-world datasets, and the results show that our proposed algorithm outperforms some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Mini-Mental State Examination (MMSE) is a widely employed screening tool for the severity of cognitive impairment. Among the MMSE items, the pentagon copying test (PCT) requires participants to accurately replicate a sample of two interlocking pentagons. While the PCT is traditionally scored on a binary scale, there have been limited developments of granular scoring scale to assess task performance. In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP) which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. A set of 497 PCTs from 84 participants including their baseline and follow-up PCTs from the Rush Memory and Aging Project was selected blinded about their cognitive and clinical status. Analysis of the quantified data revealed a significant inverse relationship between age and balance ratio (beta = − 0.49, p = 0.0033), indicating that older age was associated with a smaller balance ratio. In addition, balance ratio was associated with perceptual speed (r = 0.71, p = 0.0135), vascular risk factors (beta = − 3.96, p = 0.0269), and medical conditions (beta = − 2.78, p = 0.0389). The QIP algorithm can serve as a useful tool for enhancing the scoring of performance in the PCT. [ABSTRACT FROM AUTHOR]

The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of communities, and uncovers a significant drawback associated with the metric's behavior as the number of communities increases. Our findings reveal a pronounced bias in the NMI as the number of communities escalates. While previous studies have noted this biased behavior, they have not provided a formal proof and have not addressed the causation of this problem, leaving a gap in the existing literature. In this study, we fill this gap by employing a mathematical approach to formally demonstrate why NMI exhibits biased behavior, thereby establishing its unsuitability as a metric for evaluating clustering and community detection algorithms. Crucially, our study exposes the vulnerability of entropy-based metrics that employ logarithmic functions to similar bias. [ABSTRACT FROM AUTHOR]

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect. [ABSTRACT FROM AUTHOR]

This paper presents a fusion algorithm based on the enhanced RRT* TEB algorithm. The enhanced RRT* algorithm is utilized for generating an optimal global path. Firstly, proposing an adaptive sampling function and extending node bias to accelerate global path generation and mitigate local optimality. Secondly, eliminating path redundancy to minimize path length. Thirdly, imposing constraints on the turning angle of the path to enhance path smoothness. Conducting kinematic modeling of the mobile robot and optimizing the TEB algorithm to align the trajectory with the mobile robot's kinematics. The integration of these two algorithms culminates in the development of a fusion algorithm. Simulation and experimental results demonstrate that, in contrast to the traditional RRT* algorithm, the enhanced RRT* algorithm achieves a 5.8% reduction in path length and a 62.5% decrease in the number of turning points. Utilizing the fusion algorithm for path planning, the mobile robot generates a superior, seamlessly smooth global path, adept at circumventing obstacles. Furthermore, the local trajectory meticulously conforms to the kinematic constraints of the mobile robot. [ABSTRACT FROM AUTHOR]

There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]