Execution Time Research Articles

Energy-Efficient Rainfall Prediction Using Support Vector Machine on Edge Ai Platforms

The integration of AI into various sectors, including agriculture, has been advancing significantly. Implementing AI in the context of IoT and edge AI presents challenges due to resource limitations. Current climate changes affect planting strategies, pest management, and harvest timing. This study explores an SVM-based machine-learning model with multiple kernels to classify weather conditions as rainy or clear. The research includes two phases: model training on a PC-based system and model deployment on an edge AI device. The training phase includes preprocessing with PCA and fine-tuning of parameters, such as kernel types (linear, polynomial, sigmoid, and RBF), C and gamma. The development phase involves deploying the model on an ESP32, where execution time and power consumption are evaluated. The results show that the SVM model with an RBF kernel, C of 0.1, and gamma of 1 achieves a precision of 79.37%. Inference on the ESP32 yields an average execution time of 35.5 ms and a power consumption of 66 mA, showing a 202-fold reduction in power usage compared to the PC-based system and a 59-fold increase in execution time. This reduced power consumption supports the feasibility of edge AI for climate-based agricultural applications, enabling effective rainfall prediction. The findings contribute to the development of precision agriculture by providing insights into climate prediction, which can inform planting decisions, pest management, and harvest timing, thereby advancing the application of edge AI in response to global climate change.

Enhancing Transcriptomic Analysis by Influencing De Novo Assembly Using Parallel Computing

The efficient and accurate assembly of genomic data is a computationally intensive process that demands significant computational resources. Traditional sequential approaches often struggle to handle genomic data sets increasing volume and complexity, leading to prolonged execution times and suboptimal results. The study aims to leverage parallel computing capabilities by employing the ABySS and Velvet Assembler tools on the MD2 Pineapple dataset hosted on the Quanta server. By systematically evaluating the performance of these tools across varying thread counts, the study seeks to identify optimal configurations that can enhance the efficiency and accuracy of the de novo assembly process, ultimately enabling more rapid and precise genomic analysis. The study found that for the ABySS assembler, an 8-core and 8-thread configuration exhibited the shortest execution time and greatest speedup, while an 8-core and 12-thread setup produced similar outcomes, demonstrating ABySS's flexibility to adjust to various thread configurations. Velvet assembler demonstrated exceptional performance by utilizing 8 cores and 16 threads for the velvetg command, and 8 cores and 8 threads for the velveth command. Significantly, this study provided implications for advancing genomic analysis methodologies by providing valuable guidance on optimizing the efficiency and accuracy of de novo assembly processes through careful selection of parallelization configurations, paving the way for future studies and applications in genetic data analysis.

Analysis of Network Security Measures for Protecting Organizational Data and Implement Digital Ideas

Objectives: The goal of this study is to proposed analysis of Efficacious Intellectual Framework for Host based IDS improves the quality of the Association Rule Mining. Methods: For these 15 examiners were appropriated. To create great reactions, a solitary survey is utilized to quantify numerous hypothetical builds of the review. The poll is separated into three fundamental segments, where, the main area manages general inquiries. In the subsequent segment, the respondents are mentioned to pick one explicit elective that presents the situation with given secuity practice in their organization. The Herberos convention was utilized in this review. Results: Brute-Force method is used to reduce the total running time of the framework when the proposed framework with suggested IRG method is used for unknown rule generation and single keyword pattern matching. The proposed framework consumes less memory and less execution time. Conclusions: This approach is taken advantage of to increase the expectation precision. In this commitment, a creative Bright Characterization Structure is wanted to uplift the exactness of the grouping in the IDS. The Proposed PFSM technique prompted seize the unimportant component in the IDS dataset for the characterization of the organization. From the result achieved, it is validated that the proposed approach achieved its uniqueness over the normal element determination method executed in the Interruption Discovery Framework. Additionally, it likewise progresses the forecast exactness and reduces the mistake rates. The minimization of blunder rates is the result of incredible order precision.

Exploring K-Means Clustering Efficiency: Accuracy and Computational Time across Multiple Datasets

Exploring K-Means Clustering Efficiency: Accuracy and Computational Time across Multiple Datasets

Moving Beyond Oxford Nanopore Standard Procedures: New Insights from Water and Multiple Fish Microbiomes

Oxford Nanopore Technology (ONT) allows for the rapid profiling of aquaculture microbiomes. However, not all the experimental and downstream methodological possibilities have been benchmarked. Here, we aimed to offer novel insights into the use of different library preparation methods (standard-RAP and native barcoding-LIG), primers (V3–V4, V1–V3, and V1–V9), and basecalling models (fast-FAST, high-HAC, and super-accuracy-SUP) implemented in ONT to elucidate the microbiota associated with the aquatic environment and farmed fish, including faeces, skin, and intestinal mucus. Microbial DNA from water and faeces samples could be amplified regardless of the library–primer strategy, but only with LIG and V1–V3/V1–V9 primers in the case of skin and intestine mucus. Low taxonomic assignment levels were favoured by the use of full-length V1–V9 primers, though in silico hybridisation revealed a lower number of potential matching sequences in the SILVA database, especially evident with the increase in Actinobacteriota in real datasets. SUP execution allowed for a higher median Phred quality (24) than FAST (11) and HAC (17), but its execution time (6–8 h) was higher in comparison to the other models (0.6–7 h). Altogether, we optimised the use of ONT for water- and fish-related microbial analyses, validating, for the first time, the use of the LIG strategy. We consider that LIG–V1–V9-HAC is the optimal time/cost-effective option to amplify the microbial DNA from environmental samples. However, the use of V1–V3 could help to maximise the dataset microbiome diversity, representing an alternative when long amplicon sequences become compromised by microbial DNA quality and/or high host DNA loads interfere with the PCR amplification/sequencing procedures, especially in the case of gut mucus.

Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples

The rapid evolution of high-throughput mass spectrometry (HT-MS) technologies has positioned MS as a pivotal analytical tool across diverse disciplines. Its significance is particularly pronounced in high-throughput drug discovery and development, where MS plays a critical role throughout various phases. Acoustic ejection mass spectrometry (AEMS) is a recent addition to the HT-MS landscape, showcasing a balanced performance high analytical throughput and high data quality. Particularly, AEMS's in-line dilution feature allows the direct analysis of large-scale, complex reaction solutions without the need for sample cleanup, making it a popular choice for large-scale high-throughput screenings. However, the substantial volume of complex matrix introduces concerns about system robustness, specifically regarding the potential clogging of the sample transfer line. This study addresses this challenge by introducing an integrated automatic washing feature to the AEMS system. This enhancement significantly improves system robustness without imposing any additional demands on assay execution time. Demonstrating an extended electrode lifetime, the cleaning approach proves effective in maintaining system performance over prolonged periods, showcasing its potential for continuous large-sample-scale high-throughput analysis applications.

Mesh Smoothing of 3D Prosthesis and Orthosis Models

Introduction. Formation of a contact surface in prosthetics and orthotics is crucial for the restoration of human musculoskeletal functions. This paper considers specific features of methods currently used for denoising of a 3D model surface obtained by optical scanning. An algorithm for manufacturing an individual prosthetic and orthopedic product is developed. The current literature reports no similar methods, which may be explained by the widespread use of gypsum technology for the manufacture of prostheses and orthoses.Aim. Research and development of digital filtration methods for 3D meshes obtained by optical scanning for further modeling of individual prosthesis and orthosis modules.Materials and methods. It is proposed to optimize the pre-processing stage of 3D scanned models by applying denoising and smoothing processes. In total, 50 optical 3D scans were selected for testing via the following denoising algorithms: bilateral filtering, vertex-based anisotropic smoothing, mean and median filtering applied to face normals.Results. The study was conducted using 3D scans of lower limb stumps and Chenault brace orthoses and corsets provided by the Institute of Prosthetics and Orthotics, Albrecht Federal Scientific and Educational Centre of Medical and Social Expertise and Rehabilitation. А method for denoising and smoothing of the 3D model surfaces for the manufacture of prosthetic and orthopedic products is proposed. The SNR metric difference SNR – δ-SNR (with averaging by scans and SNR values) and average execution time were calculated. The bilateral filtration method with δ-SNR = 11.3362 dB and a runtime of 8.8900 s showed the highest efficiency.Conclusion. The proposed methods for the pre-processing stage of 3D optical scans showed high efficiency in the formation of 3D models of prosthesis and orthosis modules. The results obtained can be used for automating the process of manufacturing various prosthetic and orthopedic products, which is particularly relevant in the context of the modern geopolitical situation.

Ex-vivo evaluation of a percutaneous thread technique to palmar/plantar annular desmotomy in horses

Palmar/plantar annular desmotomy (PAD) is a surgical procedure to mitigate pain, lameness, and other clinical manifestations of annular ligament syndrome. This study aimed to describe a minimally invasive technique for PAD and evaluate potential damage to adjacent structures in 15 cadaveric limbs from sound horses (forelimb = 6 and hindlimb = 9). A 6 USP polyglycolic acid thread was positioned around the palmar/plantar annular ligament (PAL) on both sides of the limbs through two incisions (proximal and distal to PAL) through the digital sheath and subcutaneous tissue. Kelly forceps, Gerlach or Obwegeser needles were used to thread placement. A back-and-forth motion allowed the thread to cut the ligament until it exited through the proximal skin incision, and the time was registered. The degree of PAL section and tissue injuries were evaluated. The section of the ligament was complete in all limbs. However, 30% (10/30) of the procedures produced iatrogenic injuries on adjacent structures regardless of the instrument used (p > 0.1). There was a higher frequency of lesions in the neurovascular bundle (NVB) on the medial side regardless of the limb (p = 0.068). The odds of injury in the NVB were correlated to side (lateral or medial (p = 0.042) and limb (hind or forelimb) (p = 0.065) by logistic regression. The Gerlach needle had a shorter execution time than the Kelly forceps (p = 0.003). The percutaneous approach to PAD in specimens was practical, quick, and easy to perform. However, this posed a potential chance of iatrogenic trauma to the surrounding tissue. Although they are all reasonable options, the Obwegeser needle was more efficient for applying the thread than the others.

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

An Improved Light GBM using Bayesian Optimization for Vulnerability Exploitation Prediction

Despite the significant advances in software security research, exploitability prediction remains elusive due to the uncertainty of which vulnerability to be prioritized. Though many studies have been done on vulnerability prediction, some problems still persist such as efficient parameter optimization, which has significant effect on the algorithm performance and efficiency. To address these challenges, we proposed an Improved Light Gradient Boosting Machine (LGBM) model using Bayesian Optimization (BO) Method. Three experiments were conducted to compare prediction accuracy and computational cost of time and memory on LGBM, LGBM with Grid Search and LGBM with Bayesian Optimization models. The results demonstrated that our Improved BO- LGBM model has better prediction accuracy and lower computational cost than the comparative models. BO-LIGHT GBM rendered AUC of 83% measuring the model performance, accuracy of 81%, while in terms of time and memory consumption has definitely taken the lead of 0.23 min executional time and 32MiB system memory. Our results suggest promising future applications of our improved BO_ LGBM model for the prediction of vulnerability exploitation, that could be relevant for IT organizations and vendors or any organization that has limited computational resources in its premises if employed.

Enhancing keratoconus detection with transformer technology and multi-source integration

Keratoconus is a progressive eye disease characterized by the thinning and conical distortion of the cornea, leading to visual impairment. Early and accurate detection is essential for effective management and treatment. Traditional diagnostic methods, relying primarily on corneal topography, often fail to detect early-stage keratoconus due to their subjective nature and limited scope. In this research, we present a novel multi-source detection approach utilizing transformer technology to predict keratoconus progression more accurately. By integrating and analyzing diverse data sources, including corneal topography, aberrometry, pachymetry, and biomechanical properties, our method captures subtle changes indicative of disease progression. Transformer networks, known for their capability to model complex dependencies in data, are employed to handle the multimodal datasets effectively. Experimental results demonstrate that our approach significantly outperforms existing methods, such as SVM-based, Random Forests-based, and CNN-based models, in terms of accuracy, precision, recall, and F-score. Moreover, the proposed system exhibits lower execution times, highlighting its efficiency in clinical settings. This innovative methodology holds the potential to revolutionize keratoconus management by enabling earlier and more precise interventions, ultimately enhancing patient outcomes and contributing significantly to both the medical and machine learning communities.

Sparse Neighbor Joining: rapid phylogenetic inference using a sparse distance matrix.

Phylogenetic reconstruction is a fundamental problem in computational biology. The Neighbor Joining (NJ) algorithm offers an efficient distance-based solution to this problem, which often serves as the foundation for more advanced statistical methods. Despite prior efforts to enhance the speed of NJ, the computation of the n 2 entries of the distance matrix, where n is the number of phylogenetic tree leaves, continues to pose a limitation in scaling NJ to larger datasets. In this work, we propose a new algorithm which does not require computing a dense distance matrix. Instead, it dynamically determines a sparse set of at most O(n log n) distance matrix entries to be computed in its basic version, and up to O(n log 2n) entries in an enhanced version. We show by experiments that this approach reduces the execution time of NJ for large datasets, with a trade-off in accuracy. Sparse Neighbor Joining is implemented in Python and freely available at https://github.com/kurtsemih/SNJ. Supplementary data are available at Bioinformatics online.

Enhancing arrhythmia prediction through an adaptive deep reinforcement learning framework for ECG signal analysis

Heart diseases, particularly arrhythmia, remain a leading global cause of mortality. Electrocardiography (ECG) serves as a vital tool for monitoring and mitigating arrhythmia risks. Continuous proactive monitoring entails processing ECG time series data, offering critical insights to prevent severe outcomes like heart attacks and strokes. While deep learning models are widely employed for large-scale ECG data analysis, their effectiveness requires intricate hyperparameter tuning and architecture adjustments. This study presents an innovative framework incorporating a reinforcement learning-based approach to automatically fine-tune hyperparameters for a Convolutional Neural Network (CNN) model, addressing resource consumption and complexity issues. Our proposed model optimizes hyperparameters and network configurations by maximizing a reward function, overcoming challenges associated with resource-intensive and complex deep learning models. To assess its efficacy, we compare our automated fine-tuning model against both a non-optimized baseline model and a manually fine-tuned model. The findings demonstrate that our model outperforms the alternatives, achieving a higher accuracy of 97.4 %, reduced execution time (0.33 min), and lower mean square error (0.0081). Moreover, a comprehensive evaluation of resource consumption illustrates the lightweight nature of our proposed solution, minimizing overhead and complexity. Our model consistently converges to an optimal configuration, establishing its efficiency in predicting arrhythmia. In conclusion, our reinforcement learning-based hyperparameter tuning approach presents a lightweight and efficient solution for optimizing CNN models in ECG analysis. The results underscore its superiority over non-optimized and manually fine-tuned models, positioning it as a promising method for enhancing arrhythmia prediction accuracy while minimizing resource consumption and complexity.

UAV-Mounted RIS-Aided Multi-Target Localization System: An Efficient Sparse-Reconstruction-Based Approach

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.

Environmental Condition Aware Super-Resolution Acceleration Framework in Server-Client Hierarchies

In the current landscape, high-resolution (HR) videos have gained immense popularity, promising an elevated viewing experience. Recent research has demonstrated that the video super-resolution (SR) algorithm, empowered by deep neural networks (DNNs), can substantially enhance the quality of HR videos by processing low-resolution (LR) frames. However, the existing DNN models demand significant computational resources, posing challenges for the deployment of SR algorithms on client devices. While numerous accelerators have proposed solutions, their primary focus remains on client-side optimization. In contrast, our research recognizes that the HR video is originally stored in the cloud server and presents an untapped opportunity for achieving both high accuracy and performance improvements. Building on this insight, this article introduces an end-to-end video CODEC-assisted super-resolution (E 2 SR+) algorithm, which tightly integrates the cloud server with the client device to deliver a seamless and real-time video viewing experience. We propose the motion vector search algorithm executed in a cloud server, which can search the motion vectors and residuals for a part of the HR video frames and then pack them as add-ons. We also design an auto-encoder algorithm to down-sample the residuals to save the bitstream cost while guaranteeing the quality of the residuals. Lastly, we propose a reconstruction algorithm performed in the client to quickly reconstruct the corresponding HR frames using the add-ons to skip part of the DNN computations. To implement the E 2 SR+ algorithm, we design corresponding E 2 SR+ architecture in the client, which achieves significant speedup with minimal hardware overhead. Given that the environmental condition varies in the server–client hierarchies, we believe that simply applying E 2 SR+ to all frames is irrational. Accordingly, we offer an environmental condition–aware system to chase the best performance while adapting to the diverse environment. In the system, we design a linear programming (LP) model to simulate the environment and allocate frames to three existing mechanisms. Our experimental results demonstrate that the E 2 SR+ algorithm enhances the peak signal-to-noise ratio by 1.2, 2.5, and 2.3 compared with the state-of-the-art (SOTA) methods EDVR, BasicVSR, and BasicVSR++, respectively. In terms of performance, the E 2 SR+ architecture offers significant improvements over existing SOTA methods. For instance, while BasicVSR++ requires 98 ms on an NVIDIA V100 graphics processing unit (GPU) to generate a 1,280 × 720 HR frame, the E 2 SR+ architecture reduces the execution time to just 39 ms, highlighting the efficiency and effectiveness of our proposed method. Overall, the E 2 SR+ architecture respectively achieves 1.4×, 2.2×, 4.6×, and 442.0× performance improvement compared with ADAS, ISRAcc, the NVIDIA V100 GPU, and a central processing unit. Lastly, the proposed system showcases its superiority and surpasses all the existing mechanisms in terms of execution time when varying environmental conditions.

Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: an ex-vivo feasibility study

BackgroundWith the increasing number of surgeries utilizing spinal instrumentation, three-dimensional surgical navigation aims to improve the accuracy of implant placement. However, its widespread clinical adaption has been hindered by factors such as high radiation exposure and interference with standard surgical workflows.MethodsX23D is a novel AI-based fluoroscopy reconstruction technique that generates a 3D anatomical model of the spine from only four fluoroscopy images. Based on this technology, we developed a prototype for the surgical navigation of pedicle screws placement of the lumbar spine, visualizing the 3D-reconstructed spine anatomy and the surgical drill position in real-time. An ex-vivo study was conducted to compare the accuracy of the X23D-based navigation approach with fluoroscopy-aided freehand instrumentation. Five board-certified surgeons placed pedicle screws on six human torsi within a realistic surgical environment. Breach rate, site and extent (Gertzbein-Robbins) were evaluated in postoperative CT scans, as well as execution time, radiation dose, and user experience. Specimens, operating side, and surgeon were randomised.ResultsForty-nine pedicle screws (n = 24 × 23D, n = 25 2D-fluoroscopy) were evaluated, with six breaches occurring in the control group, one of which was considered clinically significant (medial breach grade C). Five breaches with one clinically significant breach were observed in the X23D group. Breach rate, execution time for each lumbar level (X23D 167 s vs. control 156 s), radiation dose (X23D 33.26 mGy vs. control 49.5 mGy), and user experience did not reveal significant differences (p > 0.05) between the groups.ConclusionsSpinal navigation using the X23D-based approach shows promise and performs well in a realistic surgical ex-vivo setting. With further refinements, its accuracy is expected to match clinical-grade navigation systems while reducing radiation dose.

Preserving privacy in association rule mining using multi-threshold particle swarm optimization

Healthcare data has become a powerful resource for generating insights that drive medical research. Association Rule Mining (ARM) techniques are widely used to identify relationships among diseases, treatments, and symptoms. However, sensitive information is often exposed, creating significant privacy challenges, particularly when data is integrated from multiple sources. Although Privacy-Preserving Association Rule Mining (PPARM) methods have been developed to address these issues, most rely on a single, predefined Minimum Support Threshold (MST) that is inflexible in adapting to diverse rule patterns. In this study, a Multi-Threshold Particle Swarm Optimization for Association Rule Mining (MPSO4ARM) model is introduced, integrating the Apriori and Particle Swarm Optimization (PSO) algorithms to perform data mining while protecting sensitive rules. A novel approach is employed by the proposed model to dynamically adjust the MST, allowing for more adaptive and effective privacy preservation. The MPSO4ARM model adjusts the MST on-the-fly based on rule length, improving its ability to safeguard sensitive data across various datasets. The proposed model was evaluated on the Chess, Mushroom, Retail, and Heart Disease datasets. The experimental results showed that the MPSO4ARM model outperforms traditional Apriori and conventional PSO algorithms, achieving higher fitness values and reducing side effects such as Hiding Failure (HF) and Missing Cost (MC), particularly in the Heart Disease and Mushroom datasets. Although the dynamic MST function introduces a moderate increase in computational runtime compared to Apriori and conventional PSO, this trade-off between execution time and enhanced privacy protection is considered acceptable, given the model's substantial improvements in data utility and rule sanitization.

Advancing the C[formula omitted] low-temperature oxidation chemistry through species measurements in a rapid compression machine, Part A: 1-Butene

Alkene chemistry plays a crucial role in the autoignition and oxidation of larger hydrocarbons. Unlike its other isomers, 1-butene is characterized by a two-stage ignition process. Various previous studies of 1-butene oxidation have used experimental techniques, including the measurement of ignition delay times in rapid compression machines (RCM) and in shock tubes, the determination of flame velocities, and the measurement of species concentrations in flames and in jet-stirred reactors (JSR). JSR studies provide an important insight into intermediate species formation at low temperatures but are constrained to low pressures and/or highly diluted conditions. To bridge the gap between JSR and engine-relevant conditions, this study presents species concentration measurements during the oxidation of 1-butene at 733 K and 30 bar under stoichiometric ’air-like’ conditions in an RCM, complemented by IDT measurements in the temperature range of 680–910 K. We designed an innovative 2-valve sampling setup to reduce quantitative uncertainties and the time required for species measurements. Our results indicate that existing 1-butene models fail to accurately predict the IDTs and the formation of the key oxidation intermediates. In response, potential optimizations for an improved kinetic model based on NUIGMech1.3 are discussed. Rate parameters for predominantly fuel consumption pathways, along with other reactions and thermochemical properties in the Waddington mechanism, have been altered within expected uncertainty limits to reflect the experimentally observed IDTs and species concentrations of this study and other validation data from the literature. However, the refined model does not predict the formation of 2-ethenyloxirane and ethene, indicating a gap in our understanding of the chemistry of these components. Overall, this study demonstrates the importance of measuring intermediates under the same conditions as IDTs to accurately address deficiencies in current kinetic mechanisms, and represents the first phase of a comprehensive investigation advancing the understanding of C4 oxidation chemistry.Novelty and significance statementThe novelty of this research lies in the design of an innovative sampling system for RCM species measurements, lowering the time for experimental execution and the uncertainties of the measurements. This enabled first-time species measurements during the oxidation of butene isomers in an RCM at high pressure and low level of dilution, contributing to the refinement of the 1-butene sub-mechanism within the NUIGMech1.3 framework. This research contributes to the understanding of the oxidation of alkenes, an important class of intermediates in gasoline and biofuel combustion. It emphasizes the need to measure intermediate species at the same conditions as ignition delay times, which are essential for understanding oxidation pathways under engine-relevant conditions. This research is part of a broader investigation of C4 oxidation chemistry, along with our companion work on n-butane. The resulting kinetic model is capable of reproducing most of the available n-butane and 1-butene validation targets.

Mathematical model based on nonlinear differential equations and its control algorithm

Abstract A practical examination of the traditional robotic arm (RA) in operation revealed a significant limitation in its ability to control the position of motion. This underscores the urgent need to enhance the current RA’s position control capabilities. Therefore, this study proposes the use of nonlinear differential equations (NDEs) to establish a mathematical model and the design of NDE-based RA motion control algorithms in conjunction with a central pattern generator neural network. A comparison of the control effects showed that the proposed method was highly fitted to the target trajectory. The joint node (JN) motion tracking trajectories of the three RAs were similar, up to 90–85% to the target trajectories of the JNs. In addition, the control of the motion position was similar up to 95–98% to the target motion position trajectories. The motion control algorithm based on NDEs was effective in improving the average execution time of the Pareto optimal frontier of the RA by 58.29%. The joint velocity and angle changes of the three types of RAs under the NDE control algorithm exhibited a high degree of similarity to the fluctuations observed in the expected and predicted curves. These observations contribute to an understanding of the effectiveness of the system observer in observing the joint angle changes. This indicates that the motion control based on NDEs can effectively enhance the tracking effectiveness of the JN positions of the RA, improve the control ability of the RA motion, and increase the joint stability of the RA.

An efficient single-shot global localization method for indoor mobile robots usingphase correlation

Purpose The purposes of this paper are to solve the low-efficiency problem caused by large search space in global localization and develop an efficient global localization method requiring only one 2D-LiDAR scan to match against the prior map for indoor mobile robots. Design/methodology/approach This paper solves the global localization problem using phase correlation as the underlying registration method. To obtain accurate rotation parameter, this paper exhaustively pre-rotates the prior map by a certain angle stride in advance. Then the input scan is matched against the pre-rotated maps one by one using phase correlation to determine translation parameters, and this paper constructs an orientation histogram by the correlation coefficients. The map rotation angle and corresponding translation parameters of the maximal peak value in the orientation histogram constitute the global pose. This paper applies a divide-and-conquer method to reduce the time consumption of single phase correlation and determines promising angle ranges where the maximal peak value may appear based on the periodicity of 90º in the orientation histogram with the signal-to-noise ratio (SNR) to reduce execution times of phase correlation. Findings Both simulated and real experimental results reveal that the proposed method achieves a high enough success rate and efficient (processing time in a second) global localization. Originality/value The proposed method constructs an orientation histogram to improve the global localization success rate and applies a divide-and-conquer method with SNR to improve efficiency, which will benefit the indoor mobile robots equipped with 2D-LiDAR.