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Quantum mechanical calculations for material modeling using Kohn–Sham density functional theory (DFT) involve the solution of a nonlinear eigenvalue problem for N smallest eigenvector-eigenvalue pairs, with N proportional to the number of electrons in the material system. These calculations are computationally demanding and have asymptotic cubic scaling complexity with the number of electrons. Large-scale matrix eigenvalue problems arising from the discretization of the Kohn–Sham DFT equations employing a systematically convergent basis traditionally rely on iterative orthogonal projection methods, which are shown to be computationally efficient and scalable on massively parallel computing architectures. However, as the size of the material system increases, these methods are known to incur dominant computational costs through the Rayleigh–Ritz projection step of the discretized Kohn–Sham Hamiltonian matrix and the subsequent subspace diagonalization of the projected matrix. This work explores the potential of polynomial expansion approaches based on recursive Fermi-operator expansion as an alternative to the subspace diagonalization of the projected Hamiltonian matrix to reduce the computational cost. Subsequently, we perform a detailed comparison of various recursive polynomial expansion approaches to the traditional approach of explicit diagonalization on both multi-node central processing unit and graphics processing unit architectures and assess their relative performance in terms of accuracy, computational efficiency, scaling behavior, and energy efficiency. [ABSTRACT FROM AUTHOR]

Numerical radiation-hydrodynamics (RHD) for non-relativistic flows is a challenging problem because it encompasses processes acting over a very broad range of time-scales, and where the relative importance of these processes often varies by orders of magnitude across the computational domain. Here, we present a new implicit–explicit method for numerical RHD that has a number of desirable properties that have not previously been combined in a single method. Our scheme is based on moments and allows machine-precision conservation of energy and momentum, making it highly suitable for adaptive mesh refinement applications; it requires no more communication than hydrodynamics and includes no non-local iterative steps, making it highly suitable for massively parallel and Graphics Processing Unit (GPU)-based systems where communication is a bottleneck; and we show that it is asymptotically accurate in the streaming, static diffusion, and dynamic diffusion limits, including in the so-called asymptotic diffusion regime where the computational grid does not resolve the photon mean-free path. We implement our method in the GPU-accelerated RHD code quokka and show that it passes a wide range of numerical tests. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

DNA methylation analysis has become an important topic in the study of human health. In previous work, we developed a suite of tools to perform this analysis. It includes HPG-Dhunter, a web-based tool for automatic detection of differentially methylated regions (DMRs) between different samples. The back-end of that tool receives an undefined number of simultaneous requests to detect DMRs on different datasets. Currently, simultaneous requests are queued and processed one at a time. This paper proposes a parallel architecture where multiple daemons serve requests simultaneously. Daemons can also share the same physical GPUs. A scheduler manages requests and forwards them to daemons. The number of daemons per GPU is configurable, thus adapting the architecture to the available hardware. Results show that the proposed parallel architecture hugely reduces the execution time. Furthermore, the speedup increases proportionally to the number of available GPUs (up to 7.47x in our experimental setup). [ABSTRACT FROM AUTHOR]

GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times. [ABSTRACT FROM AUTHOR]

Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, which can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing, and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modeling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages that generate a detailed and realistic social model and one new visualization stage. This work also presents a description of the algorithms used in each stage, different optimization techniques that permit to reduce the application convergence time, and a practical evaluation of large workloads on HPC systems. Results show that this contribution can be efficiently executed in parallel architectures and the results allow to increase the simulation detail level, representing a significant advance in the simulator scenario modeling. As a summary of results, the first contribution of this paper is the development of two models (a spatial and a social one) that assign geographical and socioeconomic indicators to each simulated individual (i.e., agents), reproducing the real social distribution of the city of Madrid. The second contribution presents an improved parallel and distributed algorithm that executes the two aforementioned models using different parallelization strategies and preserving the load balance. [ABSTRACT FROM AUTHOR]

In order to improve the emotional expression effect of jewellery, this article combines artificial intelligence technology to analyse the emotional expression in jewellery design. Aiming at the characteristics of high‐gloss materials such as strong reflectivity and translucency, starting from the classic ray tracing algorithm, this article strengthens the reflected light and projected light on the surface of the material, and proposes a realistic rendering algorithm for high‐gloss materials. Moreover, this article introduces the CUDA parallel computing architecture to give full play to the computing power of multi‐core computers, accelerate the calculation speed of the ray tracing algorithm, save the consumption of storage space, and improve the speed of real‐time rendering under the premise of ensuring realism. The experimental research results show that the emotional expression system for smart jewellery design proposed in this article has a certain effect, which can effectively improve the effect of jewellery design. [ABSTRACT FROM AUTHOR]

One of the challenges of renewable energy is its uncertain nature. Community shared energy storage (CSES) is a solution to alleviate the uncertainty of renewable resources by aggregating excess energy during appropriate periods and discharging it when renewable generation is low. CSES involves multiple consumers or producers sharing an energy storage system. This work presents an optimal strategy for CSES operators and community members to determine their optimal energy trading strategy based on social welfare maximization. Energy communities contain diverse resources, demand patterns, and constraints. The Alternating Direction Method of Multipliers (ADMM) technique is an iterative nature that decomposes the original problem into smaller sub-problems and enables parallel processing of these sub-problems. The final decomposed subproblems enable community agents to determine their optimal strategies independently decreasing the solving time, speeding up convergence to the optimal solution, and improving the scalability. The performance of the proposed model is evaluated by a case study. The presented model demonstrates that by leasing the capacity of CSES, community members could decrease their energy supply costs. Moreover, the CSES can supply the required charging and discharging power of community members from the local grid. [ABSTRACT FROM AUTHOR]

Transposing two words in a sentence (e.g. “cat” and “was” in “the white cat was big”) creates a sequence that is harder to classify as ungrammatical than control sequences (e.g. “the white was cat slowly”), suggesting that word position coding is noisy and can be affected by syntactic expectations. In the present research, this transposed-word effect was examined more closely using Rapid Serial Visual Presentation (RSVP) formats which provided either clear temporal cues to word order but no spatial cues, or both types of cues. Compared to when all words were presented simultaneously, the two RSVP formats reduced the transposed-word effect to the same degree while having no parallel impact on another ungrammatical comparison condition involving no transposition. These results are discussed in the context of serial and parallel models of reading as well as models that propose a later processing stage for the locus of the transposed-word effect. [ABSTRACT FROM AUTHOR]

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade and is now commonly used in clinical applications for precision diagnostics. Many disease areas typically involve different kinds of sample specimens, sample qualities and quantities. The quality of the DNA can range from intact, high molecular weight molecules to degraded, damaged and very short molecules. The differences in quality and quantity pose challenges for downstream molecular analyses. To overcome the challenge with the need of different molecular methods for different types of samples, we have developed a joint procedure for preparing enriched DNA libraries from high molecular weight DNA and DNA from formalin-fixed, paraffin-embedded tissue, fresh frozen tissue material, as well as cell-free DNA. [ABSTRACT FROM AUTHOR]

ASPAC is an in-house computational fluid dynamics (CFD) software for the simulation of flow in turbomachinery. In this paper, with a dual-level hybrid and heterogeneous programming method, we optimized the ASPAC software and ran it on the Sunway TaihuLight supercomputer. Then the unsteady implicit simulation of the 13-stage twin-spool compressor with 6.116 billion grid cells was realized using 611,000 cores. This is an important step closer to solving one of the four challenges mentioned in the "CFD Vision 2030 Study" proposed by NASA. A general solution method for the twin-spool compressor was first proposed. In addition, an efficient large-scale parallel processing method of row interface data exchange was proposed. After that, a series of optimization methods, such as DMA transmission, data packaging, and linear equation system solving process reconstruction, were explored. The test results show that the proposed parallel processing method of row interface data exchange greatly reduces the memory occupation, and the speed of establishing the row interface exchange relationship is increased by two orders of magnitude. Strong scalability testing in the twin-spool 13-stage compressor showed that for 0.765 billion grid cells, ASPAC can achieve 85.0% parallel efficiency when the cores are increased from 10,400 to 624,000; for 6.161 billion grid cells, ASPAC can achieve nearly 100% parallel efficiency when the cores are increased from 208,000 to 611,000. The test shows that the heterogeneous parallel modification of ASPAC on Sunway does not affect the calculation accuracy, and has high scalability, which can play a major role in the large-scale simulation of compressors. [ABSTRACT FROM AUTHOR]

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems. [ABSTRACT FROM AUTHOR]

Motivation Since 2016, the number of microbial species with available reference genomes in NCBI has more than tripled. Multiple genome alignment, the process of identifying nucleotides across multiple genomes which share a common ancestor, is used as the input to numerous downstream comparative analysis methods. Parsnp is one of the few multiple genome alignment methods able to scale to the current era of genomic data; however, there has been no major release since its initial release in 2014. Results To address this gap, we developed Parsnp v2, which significantly improves on its original release. Parsnp v2 provides users with more control over executions of the program, allowing Parsnp to be better tailored for different use-cases. We introduce a partitioning option to Parsnp, which allows the input to be broken up into multiple parallel alignment processes which are then combined into a final alignment. The partitioning option can reduce memory usage by over 4× and reduce runtime by over 2×, all while maintaining a precise core-genome alignment. The partitioning workflow is also less susceptible to complications caused by assembly artifacts and minor variation, as alignment anchors only need to be conserved within their partition and not across the entire input set. We highlight the performance on datasets involving thousands of bacterial and viral genomes. Availability and implementation Parsnp v2 is available at https://github.com/marbl/parsnp. [ABSTRACT FROM AUTHOR]

Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model YOLOv5-PDLPR is proposed, which employs YOLOv5 target detection algorithm in the license plate detection part and uses the PDLPR algorithm proposed in this paper in the license plate recognition part. The PDLPR algorithm is mainly designed as follows: (1) A Multi-Head Attention mechanism is used to accurately recognize individual characters. (2) A global feature extractor network is designed to improve the completeness of the network for feature extraction. (3) The latest parallel decoder architecture is adopted to improve the inference efficiency. The experimental results show that the proposed algorithm has better accuracy and speed than the comparison algorithms, can achieve real-time recognition, and has high efficiency and robustness in complex scenes. [ABSTRACT FROM AUTHOR]

We studied the characteristics of a four-channel multi-task reservoir computing (RC) system based on mutually-coupled and optically pumped spin vertical-cavity surface-emitting lasers (Spin-VCSELs). By analyzing bifurcation and state diagrams, we investigated the polarization dynamics of the system. Additionally, we examined the effects of key parameters such as coupling strength, injection strength, frequency detuning, optical pumping intensity, and pump polarization ellipticity on RC performance, with one-step time-series prediction as the benchmark. Our findings indicate that an increase in coupling strength results in a reduced frequency detuning range, accompanied by a lower normalized mean squared error (NMSE). Excessive coupling strength and optical pumping intensity are detrimental to RC performance. The influence of pump polarization ellipticity is smaller and exhibits a symmetry feature. Furthermore, the parameter optimization regime for orthogonal polarized optical coupling (OPOC) is wider than that for parallel polarized optical coupling (PPOC). Under optimized parameters, both PPOC and OPOC-based RC system can achieve NMSE as low as 0.033 and 0.041 respectively. This work can provide a valuable insight on the design and mechanism analysis for parallel and multi-task processing RC systems based on the platform of Spin-VCSELs. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a population pharmacokinetic-pharmacodynamic model of subcutaneously administered bupivacaine in a novel extended-release microparticle formulation for postoperative pain management. Bupivacaine was administered subcutaneously in the lower leg to 28 healthy male subjects in doses from 150 to 600 mg in a phase 1 randomized, placebo-controlled, double-blind, dose-ascending study with two different microparticle formulations, LIQ865A and LIQ865B. Warmth detection threshold was used as a surrogate pharmacodynamic endpoint. Population pharmacokinetic-pharmacodynamic models were fitted to plasma concentration-effect-time data using non-linear mixed-effects modelling. The pharmacokinetics were best described by a two-compartment model with biphasic absorption as two parallel absorption processes: a fast, zero-order process and a slower, first-order process with two transit compartments. The slow absorption process was found to be dose-dependent and rate-limiting for elimination at higher doses. Apparent bupivacaine clearance and the transit rate constant describing the slow absorption process both appeared to decrease with increasing doses following a power function with a shared covariate effect. The pharmacokinetic-pharmacodynamic relationship between plasma concentrations and effect was best described by a linear function. This model gives new insight into the pharmacokinetics and pharmacodynamics of microparticle formulations of bupivacaine and the biphasic absorption seen for several local anaesthetics. [ABSTRACT FROM AUTHOR]

In this study, we present a novel interpolation scheme for chimera simulations in CFD that treats flows with discontinuities. This scheme is suitable for interpolation over polyhedral meshes using only scattered data and without the use of stencils. During the interpolation data transfer among overset meshes, both solution variables and their gradients are communicated and then used for the interpolation process. First, the overset topology problem in partitioned polyhedral meshes is addressed, and then a new interpolation algorithm for generally discontinuous fields is introduced. Then, we describe how the gradient approximation of the Finite Volume (FV) method is utilized in order to construct bounded approximations on values on the direction of discontinuities and enhance the accuracy of the low order Nearest Neighbor value (NNV) algorithm. The performance of the proposed algorithm is quantified and validated in various test cases, together with a comparison with NNV. Finally, scalability tests are presented to prove computational efficiency. The method is proved to be highly accurate in propagation cases and performs well in unsteady two-phase problems executed using parallel architectures. [ABSTRACT FROM AUTHOR]

This article addresses the challenging problem of identifying unstable system dynamics with time delay. In the proposed novel scheme, a recurrent neural network (RNN) with parallel delayed architecture in closed-loop has been employed, which is referred to as closed-loop-delayed-RNN (CLDRNN). The systematic mathematical formulation is done in easy to follow steps to calculate all the parameters of unstable delayed process models. Interestingly, in proposed algorithm, all model parameters are directly estimated in terms of the optimized CLDRNN weights only, without using any prior knowledge about the unknown process dynamics. The Lyapunov theory is incorporated to get efficient learning, and an accurate condition is derived to achieve guaranteed global convergence of the proposed algorithm. Various identification experiments are conducted on benchmark unstable process examples to show the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to realize the real-time processing and analysis of astronomical ultrawide bandwidth signals, this study proposes a sub-band division algorithm based on RFSoC. The algorithm uses Kaiser window to design FIR prototype low-pass filter, adopts critical sampling polyphase filter bank to decompose ultra-wide bandwidth signal into several sub-bands, and encapsulates each sub-band into VDIF data frame and sends it to GPU server. The algorithm is implemented on RFSoC platform, and its effectiveness is verified by simulation and actual observation. The experimental results show that the algorithm can divide the astronomical ultra-wide bandwidth signal into multiple sub-bands in real time, packetize and transmit them to GPU. This research provides reproducible design and project for ultra-wide bandwidth signal sub-band division with low spectrum leakage and aliasing, high data accuracy, and fast processing speed. [ABSTRACT FROM AUTHOR]

Subitizing is the ability to appraise a number of small quantities (up to four) rapidly and precisely. This system, however, can be impaired by distractors presented along with targets to be enumerated. To better understand whether this limitation arises in perceptual circuits or in the response selection stage, we investigated whether subitizing can endure in simultaneous comparison tasks. Participants were asked to compare the number of dots in two sets on the left and right sides of the screen, presented either simultaneously or sequentially. For comparing within the numerosity range (6–32 dots), both the error rate and reaction time increased steadily as the ratio between the two numbers compared approached “1.” Namely, a phenomenon labeled the ratio effect was revealed. For comparison with small numbers (<5), the sequential comparison task was errorless despite the ratio, suggesting the feature of subitizing. Individual efficiency (measured by the inverse efficiency score [IES]) did not correlate between number ranges in sequential comparison, suggesting that distinct mechanisms were involved. However, we found that in simultaneous tasks, error rate and efficiency showed an increase as the ratios of the two numbers compared approached “1.” This is similar to the ratio effect revealed in the comparison for moderate numbers. Individual efficiency within these two ranges correlated, indicating that the enumeration within these two ranges was based on a single mechanism. These results suggest that subitizing cannot process sets in parallel, and numerosity takes the job whenever subitizing fails. [ABSTRACT FROM AUTHOR]

In this paper, the implementation of a new pupil detection system based on artificial intelligence techniques suitable for real-time and real-word applications is presented. The proposed AI-based pupil detection system uses a classifier implemented with slim-type neural networks, with its classes being defined according to the possible positions of the pupil within the eye image. In order to reduce the complexity of the neural network, a new parallel architecture is used in which two independent classifiers deliver the pupil center coordinates. The training, testing, and validation of the proposed system were performed using almost 40,000 eye images with a resolution of 320 × 240 pixels and coming from 20 different databases, with a high degree of generality. The experimental results show a detection rate of 96.29% at five pixels with a standard deviation of 3.38 pixels for all eye images from all databases and a processing speed of 100 frames/s. These results indicate both high accuracy and high processing speed, and they allow us to use the proposed solution for different real-time applications in variable and non-uniform lighting conditions, in fields such as assistive technology to communicate with neuromotor-disabled patients by using eye typing, in computer gaming, and in the automotive industry for increasing traffic safety by monitoring the driver's cognitive state. [ABSTRACT FROM AUTHOR]

To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected. [ABSTRACT FROM AUTHOR]

Jamming recognition is a significant prior step to achieving effective jamming suppression, and the precise results of the jamming recognition will be beneficial to anti-jamming decisions. However, as the electromagnetic environment becomes more complex, the received signals may contain both suppression jamming and deception jamming, which is more challenging for existing methods focused on a single kind of jamming. In this paper, a recognition method for compound jamming based on a dual-channel neural network and feature fusion is proposed. First, feature images of compound jamming are extracted by the short-time Fourier transform and the wavelet transform. Feature images are then employed as inputs for the proposed network. During parallel processing in dual-channel, the proposed network can adaptively extract and learn task-relevant features via the attention modules. Finally, the output features in dual-channel are fused in the fusion subnetwork. Compared with existing methods, the proposed method can yield better recognition performance with less inference time. Additionally, compared with existing fusion strategies, the fusion subnetwork can further improve the recognition performance under low jamming-to-noise ratio conditions. Results with the semi-measured datasets also verify the feasibility and generalization performance of the proposed method. [ABSTRACT FROM AUTHOR]

Despite the current growing interest in rubber composites with natural organic fillers, there is a lack of kinetic analyses that describe the decomposition of these materials during pyrolysis. For this reason, the main objective of this study was the kinetic analysis and determination of formal kinetic parameters for the pyrolytic decomposition of NR–CEL composites with different cellulose content (0, 30, 45, and 55 phr). Thermogravimetric measurements were made at heating rates of 2, 4, 6, 8, 10, and 20 °C min–1 in the temperature range of 20–600 °C. First, Friedman and KAS model-free methods were applied. Therefore, model-based methods and the model-fitting procedure were used to find the optimal multi-step kinetic model. The proposed final model consists of two parallel processes, which are kinetically independent: A → B → C and D → E → F. For each step, a kinetic triplet was calculated: the apparent activation energy, the pre-exponential factor, and the kinetic parameters of the extended empirical Prout–Tompkins model. The master plots method was used to determine the kinetic decomposition mechanism of the individual steps. It was found that step A → B has the shape of an nth-order model, step B → C mainly follows the diffusion model, the mechanism of step D → E transfers from a random scission kinetics model to an nth-order model with an increasing amount of CEL, and step E → F obeys the chain scission mechanism. [ABSTRACT FROM AUTHOR]

Classical theories of reasoning equate System 1 with biases and System 2 with correct responses. Refined theories of reasoning propose the parallel model to explain the two systems. The first purpose of the present article is to give a contribution to the debate on the parallel and default-interventionfist models: we hypothesized when logic and belief conflict both logical validity and belief judgments will be affected with greater level of response errors and/or longer response times. The second purpose of this article is to assess the relationship between decisional styles and performance in deductive reasoning. Seventy-two participants participated in the experiment and completed 64 modus ponens and modus tollens syllogistic reasoning tasks. Accordingly, we found that belief and logic judgments were affected by the conflict condition, both in easy syllogisms (i.e., modus ponens) and in complex syllogisms (i.e., modus tollens). Findings showed also that participants with a rational decision-making style were more strongly influenced by logic than belief, whereas those with an intuitive decision-making style were more strongly influenced by belief than logic. [ABSTRACT FROM AUTHOR]

The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset. [ABSTRACT FROM AUTHOR]

Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. It fuses the benefits of HBA with parallel processing and efficient feedback with GA's excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The localization methods based on line cloud can protect scene privacy, but they also face the risk of being cracked by a privacy attack algorithm proposed by Kunal Chelani et al. This attack algorithm can recover approximate point clouds from line clouds, but Us computational efficiency is low. To address this issue, a parallel optimization algorithm is proposed and evaluated in terms of running time and speedup ratio. Specifically, the CPU multicore parallelism and the GPGPU parallelism are implemented using the SPMD pattern and the pipeline parallel pattern respectively. Furthermore, the data parallel pattern is adopted to implement heterogeneous computing, to achieve the highest degree of parallelism. Experimental results demonstrate that the maximum speedup ratio of the parallel optimization algorithm is 15. 11, and the minimum is 8. 20. Additionally, compared to the original algorithm, the parallel optimization algorithm ensures the relative error of the recovered point clouds within 0.4% of the original error, ensuring the accuracy of the algorithm. This research holds significant importance and reference value for line cloud privacy attack algorithms, as well as for privacy protection algorithms in Line Cloud under different scenarios and other density estimation problems. [ABSTRACT FROM AUTHOR]

This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge. [ABSTRACT FROM AUTHOR]

Summary Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707 043 cells and 1 154 611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. Availability and implementation The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits. [ABSTRACT FROM AUTHOR]

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders. [ABSTRACT FROM AUTHOR]

This work demonstrates that the electrodeposition of highly stressed films on compliant ribbons is a robust process to obtain helical structures with excellent mechanical stability and potentially high thermal and electrical conductance. Electrodeposition on end‐tethered ribbons alters their axial and bending stiffness while imparting mechanical stress to drive the formation of a helix with a microscale diameter and pitch in a controlled and scalable manner. The process generates helices with diameters and pitches between 80 and 200 µm and lengths as large as several millimeters. The approach is amenable to parallel processing a large number of 3D structures on any substrate, including large‐area semiconductor wafers. This phenomenon is explained in terms of the change of stress gradients as material is added. Applications of the fabricated helices include antennas, metamaterials, and slow‐wave structures in frequency ranges not previously attainable. [ABSTRACT FROM AUTHOR]

In hydroforming of parallel double-branch tubes, the material entering the branch zone is obstructed by material accumulation in the main tubes and corners, which decreases the branch height. A tube hydroforming approach is combined with pre-forming and crushing (THPC) to mitigate this problem. A larger diameter tube blank is flattened for pre-forming and then subjected to radial compression for crushing. In the next step, hydroforming forms the parallel double-branch tubes. Experiments and numerical simulations are then carried out to analyze the effect of traditional tube hydroforming (TTH) and the proposed THPC process on the formability of parallel double-branch tubes. The results show that for tubes obtained via THPC, the tube burst pressure increases by 27.5% and the branch height increases 2.37-fold compared to TTH. Additionally, the flattening, pre-forming, and crushing stages cause work hardening of the tube when using the TPHC process. Flattened tubes undergo radial compression to improve the material flowing into the branch tube. The formability of parallel double-branched tubes can be improved by using the TPHC process. Consequently, tube hydroforming, combined with pre-forming and crushing, has been confirmed as a feasible forming process for fabricating parallel double-branch tubes. [ABSTRACT FROM AUTHOR]

The balance of brain functional segregation (i.e., the process in specialized local subsystems) and integration (i.e., the process in global cooperation of the subsystems) is crucial for cognition in human beings, and many deep learning models have been used to evaluate the spatial information during EEG-based affective computing. However, acquiring the intrinsic spatial representation in the topology of EEG channels is still challenging. To further address the issue, we propose the FAformer to enhance spatial information in EEG signals with parallel-branch architectures based on a vision transformer (ViT). In the encoder, there is a branch that utilizes Adaptive Neural Fourier Operators (AFNO) to model global spatial patterns using the Fourier transform in the electrode channel dimension. The other branch utilizes multi-head self-attention (MSA) to explore the dependence of emotion on different channels, which is conducive to building key local networks. Additionally, a self-supervised learning (SSL) task of adaptive feature dissociation (AdaptiveFD) is developed to improve the distinctiveness of spatial features generated from the parallel branches and guarantee robustness in different subjects. FAformer achieves superior performance over the competitive models on the DREAMER and DEAP. Moreover, the rationality and hyperparameters analysis are conducted to demonstrate the effectiveness of the FAformer. Finally, the visualization of features reveals the spatial global connections and key local patterns during the deep learning process in FAformer, which benefits EEG-based affective computing. [ABSTRACT FROM AUTHOR]

Phosphate sorption on rendzina soil was studied by P-32 heterogeneous isotopic exchange under a steady-state. There are two types of sorbed phosphate, namely strongly and weakly bonded phosphate, the latter being able to exchange with phosphate (H232PO4−) ions in the soil solution. The experimental kinetic data was not fitted by the one exponential kinetic model. Starting from this observation, a new kinetic model is established by assuming two types of weakly bonded phosphate, which take part in the two parallel exchange processes. A biexponential kinetic equation is obtained, which fits the experimental data much better than the one exponential equation. [ABSTRACT FROM AUTHOR]

The suggested solution in this work makes use of the parallel processing capability of FPGA to enhance the efficiency of the K-Nearest Neighbor (KNN) algorithm on encrypted data. The suggested technique was assessed utilizing the breast cancer datasets and the findings indicate that the FPGA-based acceleration method provides significant performance improvements over software implementation. The Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme is used for the computation of ciphertext. After extensive simulation in Python and implementation in FPGA, it was found that the proposed architecture brings down the computational time of KNN on ciphertext to a realistic value in the order of the KNN classification algorithm over plaintext. For the FPGA implementation, we used the Intel Agilex7 FPGA (AGFB014R24B2E2V) development board and validated the speed of computation, latency, throughput, and logic utilization. It was observed that the KNN on encrypted data has a computational time of 41.72 ms which is 80 times slower than the KNN on plaintext whose computational time is of 0.518 ms. The main computation time for CKKS FHE schemes is 41.72 ms. With our architecture, we were able to reduce the calculation time of the CKKS-based KNN to 0.85 ms by using 32 parallel encryption hardware and reaching 300 MHz speed. [ABSTRACT FROM AUTHOR]

The huge amount and diversity of data generated by Internet of Things (IoT) devices and the need to store and process this data led to the development of fog computing alongside cloud computing. Fog computing is a new paradigm for providing service at the edge of the network and close to end users, so that it can support real-time IoT applications. Because fog involves heterogeneous and distributed infrastructure with limited resources, so efficient resource allocation to satisfy Quality of Service (QoS) is challenging. IoT application placement mechanisms have been developed to address these issues, in which the subordinate services of these applications are mapped to fog nodes. Despite extensive research to solve the Service Placement Problem (SPP) in fog computing, efforts are still ongoing due to the importance of the issue. Hence, this paper proposes an efficient and autonomous mechanism for solving SPP using Teaching Learning-Based Optimization (TLBO) called SPP-TLBO. SPP-TLBO is a multi-objective QoS-aware algorithm that manages resources on distributed and localized fog domains. In addition to the above, we improve the performance of TLBO by configuring the evolution process with a shared parallel architecture. Besides, SPP-TLBO can save more resources to handle future requests by considering application deadlines and extracting the dynamic distribution of resources required over time. The proposed algorithm is evaluated by simulation on a synthetic fog environment. The simulation results show that SPP-TLBO improves system performance and is between 8 and 19% better efficiency compared to some advanced methods such as CSA-FSPP, FSP-ODMA and, WOA-FSP. [ABSTRACT FROM AUTHOR]

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future. [ABSTRACT FROM AUTHOR]

The Von Neumann architecture has been the foundation of modern computing systems. Still, its limitations in processing large amounts of data and parallel processing have become more apparent as computing requirements increase. Neuromorphic computing, inspired by the architecture of the human brain, has emerged as a promising solution for developing next-generation computing and memory devices with unprecedented computational power and significantly lower energy consumption. In particular, the development of optoelectronic artificial synaptic devices has made significant progress toward emulating the functionality of biological synapses in the brain. Among them, the potential to mimic the function of the biological eye also paves the way for advancements in robot vision and artificial intelligence. This review focuses on the emerging field of optoelectronic artificial synapses and memristors based on low-dimensional nanomaterials. The unique photoelectric properties of these materials make them ideal for use in neuromorphic and optoelectronic storage devices, with advantages including high carrier mobility, size-tunable optical properties, and low resistor–capacitor circuit delay. The working mechanisms, device structure designs, and applications of these devices are also summarized to achieve truly sense-storage-computer integrated optoelectronic artificial synapses. [ABSTRACT FROM AUTHOR]

Ovarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women. [ABSTRACT FROM AUTHOR]

We present an implementation of a stage-parallel preconditioner for Radau IIA type fully implicit Runge--Kutta methods, which approximates the inverse of the Runge--Kutta matrix AQ from the Butcher tableau by the lower triangular matrix resulting from an LU decomposition and diagonalizes the system with as many blocks as stages. For the transformed system, we employ a block preconditioner where each block is distributed and solved by a subgroup of processes in parallel. For combination of partial results, we use either a communication pattern resembling Cannon's algorithm or shared memory. A performance model and a large set of performance studies (including strongscaling runs with up to 150k processes on 3k compute nodes) conducted for a time-dependent heat problem, using matrix-free finite element methods, indicate that the stage-parallel implementation can reach higher throughputs near the scaling limit. The achievable speedup increases linearly with the number of stages and is bounded by the number of stages. Furthermore, we show that the presented stage-parallel concepts are also applicable to the case that AQ is directly diagonalized, which requires either complex arithmetic or solutions of two-by-two blocks, both exposing about half the parallelism. Alternatively to distributing stages and assigning them to distinct processes, we discuss the possibility of batching operations from different stages together. [ABSTRACT FROM AUTHOR]

Yara Sharif is a Senior Lecturer and a practitioner with an interest in design as a means to facilitate and empower "forgotten" communities, while also interrogating the relationship between politics and architecture. Combining research with design, her work runs parallel between the architecture practice Golzari-NG Architects and the school of architecture at the University of Westminster. Sharif co-founded Palestine Regeneration Team (PART), which is a design-led research group that aims—through speculative and live projects—to search for creative and responsive spatial possibilities to heal the fractured landscape. Sharif's work has won a number of awards including the RIBA's President's Award for Research 2016 on "Cities and communities" category. Sharif has also contributed to several collaborative projects with Palestinian NGO Riwaq which won the Holcim Commendation Award for Sustainable Construction in the MENA Region 2014—Beit Iksa Eco Kitchen, and The Aga Khan Award for Architecture 2013 for the Regeneration of Birzeit Historic Centre. [ABSTRACT FROM AUTHOR]

Glioma brain tumour is one of the life‐threatening diseases in the world. Tumour substructure segmentation by physicians is a time‐consuming task with the magnetic resonance imaging (MRI) technique due to the size of clinical data. An automatic and well‐trained method is essential to detect and segment the tumour which increase the survival of the patients. The proposed work aims to produce high accuracy on glioma substructures segmentation with less computation time using deep learning. From the literature survey, the following challenges are found: (i) computing complex spatial boundaries between normal and tumour tissues, (ii) feature reduction and (iii) overfitting problems. Hence, we proposed a fully automatic glioma tumour segmentation using a residual‐inception block (RIB) with a modified 3D U‐Net (RIBM3DU‐Net). It includes three phases: pre‐processing, modified 3D U‐Net segmentation and post‐processing. From the results, RIB with U‐Net enhances the segmentation accuracy. GPU parallel architecture reduces the computation time while training and testing. For quantitative analysis, comprehensive experiment results were computed and compared with state‐of‐the‐art methods. It achieves better Dice scores on enhancing tumour, tumour core and complete tumour of 87%, 87% and 94%, respectively. GPU speedup folds yield up to 48× when compared with CPU. Quantitatively, 3D glioma volume is rendered from the obtained segmented results and estimated using the Cavalieri estimator. [ABSTRACT FROM AUTHOR]

Degrammaticalisation is an oft‐dismissed category of language change. In this paper evidence is provided for its existence, its triggers, and its conditions. This case study details the development of an understudied Old Italo‐Romance indefinite, covelle, a polarity‐sensitive item roughly translating as 'anything' which originated from a Latin free relative with Free‐Choice interpretation. It is shown that in Old Italo‐Romance the original Free Choice use was expanded to further nonveridical contexts. Subsequently, a curious development of covelle in Old Romagnol, a 16th‐century Gallo‐Italic variety, is examined whereby polar neutralisation (loss of sensitivity to polarity) of the indefinite was taking place. Contextually, a process of degrammaticalisation had begun through which the indefinite pronoun developed into the classifier 'thing'. In Modern and Contemporary Romagnol cvël is almost exclusively employed as a noun/classifier. It is argued that two parallel processes have initiated a cycle that led cvël to complete degrammaticalisation: (i) the grammaticalization of the old noun for 'thing', co(n)sa, into a wh‐word, calling for lexical replacement, and (ii) competition from the Negative Concord Item gnit. The development of covelle in Romagnol represents a well‐documented case of degrammaticalisation, whose causes and conditions can be thoroughly described and motivated within an interaction with cyclical change. [ABSTRACT FROM AUTHOR]

The act of gathering, looking over, and analyzing a lot of data to find patterns, insights, and market trends that can help businesses make more effective choices is known as big data analysis (BDA). Quick and effective access to this data allows businesses to be flexible in developing strategies to hold onto their competitive edge. To analyze massive amounts of data quickly through parallel processing, the structure of the Hadoop software employs the MapReduce methodology. Computational solid resources are necessary for BDA, although they are not always available. Developing new clustering techniques that could handle this kind of data processing became crucial. Therefore, in this research, we presented a novel, effective fuzzy-based Improved Multiview Fuzzy C-Means Algorithm (IMV-FCA) to boost the clustering strategy. To summarize, fuzzy-based IMV-FCA clustering presents the ensemble of the MobileNet V2 model, and three-layered stacked Bidirectional LSTM (MVSBiLSTM) to increase computing speed and effectiveness. It also presents a function that calculates the separation among the cluster center and the particular instance, to assist with better clustering. By simulating shared memory space and parallelizing on the framework known as MapReduce on the Hadoop cloud computing platform, the distributed database is utilized to improve the method’s effectiveness while reducing its time complexities. The experimental investigation was conducted on existing approaches, and the proposed approach was analyzed using three standard datasets. While differentiating from existing approaches, the presented approach yields greater performances in terms of various metrics. [ABSTRACT FROM AUTHOR]

Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment. [ABSTRACT FROM AUTHOR]

The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV. [ABSTRACT FROM AUTHOR]