Merging Method Research Articles

Insights into multiscale chemical characterisation for the understanding of berry maturation

Among secondary metabolites, phenolic and volatile compounds are major contributors to wine quality. Their quantities in wines will depend on several factors, their concentrations in grapes but also their extraction rates, along with a number of other factors. Extraction is a complicated process which involves interactions with other compounds; e.g., polysaccharides and proteins. The aim of this work was to evaluate the potential of merging different analytical methods for identifying different compounds at different scales. Known methods in wine analysis, such as phloroglucinolysis, were complemented with methods usually applied to other food products and rarely or never on grapes or wines, such as solid-state NMR and NMR water mobility by 1H MRI. The feasibility of merging these methods was validated on this first trial. While the results did not allow us to answer questions related to the study, they raised other interesting questions to be addressed in future experiments.

A method for merging and classifying higher mathematics teaching resources based on density clustering algorithm

A method for merging and classifying higher mathematics teaching resources based on density clustering algorithm

Automated Cancer Detection Using CNNs: Innovations and Applications

Large-scale datasets and complicated cell architectures provide challenges for traditional tumor identification approaches, which often rely on human analysis or traditional machine-learning techniques. This may result in errors and inefficiencies. The inconsistent shape of tumor cells may be a challenge for these methods, making border detection and classification inconsistent. This paper suggests an automated tumor detection technique based on convolutional neural networks (CNNs) to increase tumor cell border detection and classification accuracy and efficiency. Several experiments were carried out to confirm the suggested strategy's efficacy. This entails developing and refining the CNN architecture in addition to merging deep learning and data augmentation methods. According to the findings, the model has performed well in a variety of experimental settings, especially when it comes to classifying complicated cell samples. Furthermore, the feasibility of using this model for real-time border detection was explored, indicating its suitability for use in a medical setting. Subsequent investigations will concentrate on enhancing the capabilities of models, expanding their range, and tackling problems like disparities in data. This discovery is significant because it has the potential to transform tumor identification by providing a more accurate, scalable, and efficient approach that might be used extensively in clinical settings and eventually lead to better patient outcomes.

Application of Mixed-Mode Ventilation to Enhance Indoor Air Quality and Energy Efficiency in School Buildings

Indoor air quality and energy efficiency are instrumental aspects of school facility design and construction, as they directly affect the physical well-being, comfort, and academic output of both pupils and staff. The challenge of balancing the need for adequate ventilation to enhance indoor air quality with the goal of reducing energy consumption has long been a topic of debate. The implementation of mixed-mode ventilation systems with automated controls presents a promising solution to address this issue. However, a comprehensive literature review on this subject is still missing. To address this gap, this review examines the potential application of mixed-mode ventilation systems as a solution to attaining improved energy savings without compromising indoor air quality and thermal comfort in educational environments. Mixed-mode ventilation systems, which combine natural ventilation and mechanical ventilation, provide the versatility to alternate between or merge both methods based on real-time indoor and outdoor environmental conditions. By analyzing empirical studies, case studies, and theoretical models, this review investigates the efficacy of mixed-mode ventilation systems in minimizing energy use and enhancing indoor air quality. Essential elements such as operable windows, sensors, and sophisticated control technologies are evaluated to illustrate how mixed-mode ventilation systems dynamically optimize ventilation to sustain comfortable and healthy indoor climates. This paper further addresses the challenges linked to the design and implementation of mixed-mode ventilation systems, including complexities in control and the necessity for climate-adaptive strategies. The findings suggest that mixed-mode ventilation systems can considerably lower heating, ventilation, and air conditioning energy usage, with energy savings ranging from 20% to 60% across various climate zones, while also enhancing indoor air quality with advanced control systems and data-driven control strategies. In conclusion, mixed-mode ventilation systems offer a promising approach for school buildings to achieve energy efficiency and effective ventilation without sacrificing indoor environment quality.

A Comparative Analysis of Instruction Fine-Tuning Large Language Models for Financial Text Classification

Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tasks due to the technical and specialized nature of financial texts. This study investigates the efficacy of instruction fine-tuning smaller-scale LLMs, including Mistral-7B, Llama3-8B, and Phi3-mini, to enhance their performance in financial text classification tasks. We fine-tuned both instruction-tuned and base models across four financial classification tasks, achieving significant improvements in task-specific performance. Furthermore, we evaluated the zero-shot capabilities of these fine-tuned models on three unseen complex financial tasks, including argument classification, deal completeness classification, and causal classification. Our results indicate while base model fine-tuning led to greater degradation, instruction-tuned models maintained more robust performance. To address this degradation, we employed model merging techniques, integrating single-task domain-specific fine-tuned models with the base model. Using this merging method resulted in significant enhancements in zero-shot performance, even exceeding the original model’s accuracy on certain datasets. Our findings underscore the effectiveness of instruction fine-tuning and model merging for adapting LLMs to specialized financial text classification tasks.

RETRACTION: An Improved Differential Bond Energy Algorithm With Fuzzy Merging Method to Improve the Document Clustering for Information Mining

RETRACTION: S. Tejasree and B. Chandra Mohan, “,” Expert Systems 41, no. (2024): , https://doi.org/10.1111/exsy.13261. The above article, published online on 16 April 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Following publication, it has come to the attention of the journal that this article was accepted on the basis of a compromised peer review process. The editors have therefore decided to retract this article. The authors disagree with the retraction.

ITSxpress version 2: software to rapidly trim internal transcribed spacer sequences with quality scores for amplicon sequencing.

The nuclear ribosomal RNA (rRNA) internal transcribed spacer (ITS) regions are commonly used to identify fungi and other eukaryotic taxa in amplicon sequencing. The highly conserved rRNA regions flanking the ITS are often trimmed before being used for taxonomic assignment. The Python software package ITSxpress rapidly trims single-end or paired-end sequences in FASTQ format for use in amplicon sequence variant clustering methods like DADA2. This new major release of ITSxpress improves the paired-end merging method, simplifies installation of the QIIME 2 ITSxpress plugin, removes major dependencies, adds use cases, and is compatible with newer compression formats. This article discusses the modifications to ITSxpress that improve the output and user experience, leading to a major version increase.IMPORTANCEITSxpress is a sequence trimming method applied to internal transcribed spacer (ITS) amplicon sequences before calling amplicon sequence variants (ASVs). The ITS region is used to understand the composition of eukaryotic microbial communities. ITS sequences provide good taxonomic resolution due to their hypervariability, but are flanked by conserved regions that allow their primers to be more universal. Amplicons generated with such primers contain regions with different evolutionary rates, and trimming these conserved regions results in better taxonomic classification and a more valid set of ASVs. This package can be used for most amplicon sequencing methods including for newer long-read sequencing formats, such as PacBio. ITSxpress can be installed from Bioconda, used as a Docker image, or installed from source code. The package works well with high-performance computing clusters or laptops due to its low-resource requirements.

Spectral cluster supertree: fast and statistically robust merging of rooted phylogenetic trees.

The algorithms for phylogenetic reconstruction are central to computational molecular evolution. The relentless pace of data acquisition has exposed their poor scalability and the conclusion that the conventional application of these methods is impractical and not justifiable from an energy usage perspective. Furthermore, the drive to improve the statistical performance of phylogenetic methods produces increasingly parameter-rich models of sequence evolution, which worsens the computational performance. Established theoretical and algorithmic results identify supertree methods as critical to divide-and-conquer strategies for improving scalability of phylogenetic reconstruction. Of particular importance is the ability to explicitly accommodate rooted topologies. These can arise from the more biologically plausible non-stationary models of sequence evolution. We make a contribution to addressing this challenge with Spectral Cluster Supertree, a novel supertree method for merging a set of overlapping rooted phylogenetic trees. It offers significant improvements over Min-Cut supertree and previous state-of-the-art methods in terms of both time complexity and overall topological accuracy, particularly for problems of large size. We perform comparisons against Min-Cut supertree and Bad Clade Deletion. Leveraging two tree topology distance metrics, we demonstrate that while Bad Clade Deletion generates more correct clades in its resulting supertree, Spectral Cluster Supertree's generated tree is generally more topologically close to the true model tree. Over large datasets containing 10,000 taxa and 500 source trees, where Bad Clade Deletion usually takes 2 h to run, our method generates a supertree in on average 20 s. Spectral Cluster Supertree is released under an open source license and is available on the python package index as sc-supertree.

An automatic merging method for large kinetic mechanisms: Application to a surrogate fuel combustion model

An automatic merging method for large kinetic mechanisms: Application to a surrogate fuel combustion model

Experimental narratives: A comparison of human crowdsourced storytelling and AI storytelling

The paper proposes a framework that combines behavioral and computational experiments employing fictional prompts as a novel tool for investigating cultural artifacts and social biases in storytelling both by humans and generative AI. The study analyzes 250 stories authored by crowdworkers in June 2019 and 80 stories generated by GPT-3.5 and GPT-4 in March 2023 by merging methods from narratology and inferential statistics. Both crowdworkers and large language models responded to identical prompts about creating and falling in love with an artificial human. The proposed experimental paradigm allows a direct and controlled comparison between human and LLM-generated storytelling. Responses to the Pygmalionesque prompts confirm the pervasive presence of the Pygmalion myth in the collective imaginary of both humans and large language models. All solicited narratives present a scientific or technological pursuit. The analysis reveals that narratives from GPT-3.5 and particularly GPT-4 are more progressive in terms of gender roles and sexuality than those written by humans. While AI narratives with default settings and no additional prompting can occasionally provide innovative plot twists, they offer less imaginative scenarios and rhetoric than human-authored texts. The proposed framework argues that fiction can be used as a window into human and AI-based collective imaginary and social dimensions.

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods.

A Hybrid Parametrization Method for B‐Spline Curve Interpolation via Supervised Learning

AbstractB‐spline curve interpolation is a fundamental algorithm in computer‐aided geometric design. Determining suitable parameters based on data points distribution has always been an important issue for high‐quality interpolation curves generation. Various parameterization methods have been proposed. However, there is no universally satisfactory method that is applicable to data points with diverse distributions. In this work, a hybrid parametrization method is proposed to overcome the problem. For a given set of data points, a classifier via supervised learning identifies an optimal local parameterization method based on the local geometric distribution of four adjacent data points, and the optimal local parameters are computed using the selected optimal local parameterization method for the four adjacent data points. Then a merging method is employed to calculate global parameters which align closely with the local parameters. Experiments demonstrate that the proposed hybrid parameterization method well adapts the different distributions of data points statistically. The proposed method has a flexible and scalable framework, which can includes current and potential new parameterization methods as its components.

Trends and Future Directions in Analysing Attractiveness of Geoparks Using an Automated Merging Method of Multiple Databases—R-Based Bibliometric Analysis

Since their creation, geoparks have been among the fastest growing natural environments. Their attractiveness is one of the most important factors for the success of this natural destination. Despite their importance, a bibliometric analysis on geopark attractiveness is missing from the studied databases. The aim of this paper is to synthesise a heterogeneous body of knowledge of geoparks in terms of attractiveness, highlighting the evolution and breadth of the research field. To achieve this, the following objectives were set: (a) to adopt a method based on functions provided by the bibliometrix package to automatically combine databases, namely WoS, Scopus, PubMed and Dimensions, detailing the method used and (b) to analyse the bibliometric indicators in order to identify the trends in the literature and the possible directions for future research. The applied methodology was based on bibliometric analysis using R for non-coders. From the 707 documents retrieved, the validation process resulted in 349 eligible documents published between 2002 and 2024, on which the analysis was carried out. The current study elaborated a method and examined the key information on the topic trends, which were given by production performance, productivity trends, spatial analysis and abstract approach analysis. Additionally, strategic mapping of the conceptual context was performed. Thus, the result provides a description of the automatic method with practical applications. As discerned from the three-dimensional analysis (spatial, temporal and size), the emerging research directions within scientific creativity encompassed (1) forms of tourism practiced in geoparks, especially focused on ecotourism and volcanic tourism; (2) geomorphological features, mineral springs and mud volcanoes; (3) aesthetic aspects, scenic sites and mining heritage; and (4) methodology, data analysis and modelling methods across different regions and countries.

A Novel DC GCN with Attention Mechanism for Accurate Near Duplicate Video Data Cleaning

There has been a steady emergence of nearly identical recordings in the last several decades, thanks to the exponential development of video data. The use of regular videos has been impacted by data quality difficulties produced by near-duplicate movies, which are becoming increasingly noticeable. While there has been progress in the field of near-duplicate video detection, there is still no automated merging method for video data characterised by high-dimensional features. As a result, it is challenging to automatically clean near-duplicate videos in advance video dataset data quality. Research on removing near-duplicate video data is still in its early stages. The precision of near-duplicate video data cleaning is severely compromised by the delicate issues of video data organization besides initial clustering centres in the current research, which arise when the previous distribution is unknown. In tackle these problems, we offer a new kind of Graph Convolutional Neural Network (GCN) that uses dense influences and a categorization attention mechanism. Deeply connected graph convolutional networks (DC-GCNs) learn about faraway nodes by making GCNs deeper. By using dense connections, the DC-GCN is able to multiplex the small-scale features of shallow layers and generate features at diverse scales. Finally, an attention mechanism is incorporated to aid in feature combination and importance determination. Sparrow Search Optimisation Algorithm (SSA) is used to pick the parameters of the given model in the most optimal way. In the end, experiments are carried out using a coal mining video dataset and a widely known dataset called CC_WEB_VIDEO. The simulation findings show that the suggested strategy performs better than certain previous studies.

Two-Stage Satellite Combined-Task Scheduling Based on Task Merging Mechanism

Satellites adopt a single-task observation mode in traditional patterns. Although this mode boasts high imaging accuracy, it is accompanied by a limited number of observed tasks and a low utilization rate of satellite resources. This limitation becomes particularly pronounced when dealing with extensive and densely populated observation task sets because the inherent mobility of satellites often leads to conflicts among numerous tasks. To address this issue, this paper introduces a novel multi-task merging mechanism aimed at enhancing the observation rate of satellites by resolving task conflicts. Initially, this paper presents a task merging method based on the proposed multitask merging mechanism, referred to as the constrained graph (CG) task merging approach. This method can merge tasks while adhering to the minimal requirements specified by users. Subsequently, a multi-satellite merging scheduling model is established based on the combined task set. Considering the satellite combined-task scheduling problem (SCTSP), an enhanced fireworks algorithm (EFWA) is proposed that incorporates the CG task synthesis mechanism. This algorithm incorporates local search strategies and a population disturbance mechanism to enhance both the solution quality and convergence speed. Finally, the efficacy of the CG algorithm was validated through a multitude of simulation experiments. Moreover, the effectiveness of the EFWA is confirmed via extensive comparisons with other algorithms, including the basic ant colony optimization (ACO) algorithm, enhanced ant colony optimization (EACO) algorithm, fireworks algorithm (FWA), and EFWA. When the number of tasks in the observation area are dense, such as in the case where the number of tasks is 700, the CG + EFWA (CG is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage) method improves observation benefits by 70.35% (compared to CG + EACO, CG is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage), 78.93% (compared to MS + EFWA, MS is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage), and 39.03% (compared to MS + EACO, MS is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage).

Density clustering method based on k-nearest neighbor propagation

Abstract Density clustering, an effective data analysis tool, performs well on arbitrary shapes and non-convex datasets. However, it still has some limitations in identifying the cluster structures of datasets with irregular shapes and uneven density distribution. Aiming at the above problem, this paper proposes a density clustering method based on k-nearest neighbor propagation. Firstly, based on the theory of k-nearest neighbor, the nearest hub points are defined to replace the sample points within their k-nearest neighbors, and the cluster boundary is ascertained relying on the propagation of the nearest hub points with density gradient trend so as to divide the nearest hub points into multiple groups; secondly, a group merging method of the nearest hub points based on the set similarity is given to obtain the set of nearest hub points; and finally, the remaining sample points are assigned to the set of their nearest hub points to obtain the final clusters. The correctness and effectiveness of the algorithm are verified by experiments on synthetic and UCI datasets, and the results show that the algorithm can better cope with the clustering needs of multiple complex data distributions compared with other comparative algorithms.

3D printing single‐stroke path planning, local reinforcement and performance testing of MBB beams and cantilever beams

Abstract3D printing of continuous fiber reinforced thermoplastic composites (CFRTPCs) has been applied for the molding of composite complex structures due to the advantages of flexibility and one‐step forming. In this study, feature points simplification, merging and rearrangement were accomplished for Messerschmitt‐Bölkow‐Blohm (MBB) beam and cantilever beam to generate single‐stroke print paths for the specimens. For the significant stress areas in the finite element analysis results of these structures, a local reinforcement method using orthogonal layups was proposed, which was achieved by raising and lowering the print head and adjusting the fiber volume fraction during the printing. After experimental validation and failure modes analysis, the path planning significantly improved the peak load and structural stiffness of the specimens, and the local reinforcement further enhanced the equivalent specific strength and specific stiffness. This study provides a single‐stroke 3D printing strategy for complex structure molding in conjunction with parameter adjustments.Highlights A single‐stroke path planning was developed for MBB beams and cantilever beams, including simplification, merging and rearrangement methods for feature points. A local reinforcement method was proposed, which was achieved by raising and lowering the print head and adjusting the fiber volume fraction during the printing process. The effect of different print paths on specimen properties was investigated.

Modeling contextual goals and detecting context conflicts from BDD-based user stories

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development.

Pipe merging for transient gas network optimization problems

Pipe merging for transient gas network optimization problems

A DDoS Tracking Scheme Utilizing Adaptive Beam Search with Unmanned Aerial Vehicles in Smart Grid

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.