Efficient Comparison Research Articles

Utilizing machine learning algorithm and Carrera unified formulation for vibration analysis of nanocomposite-enhanced bridge structures rested on an innovative elastic foundation: verification of the results via nondestructive testing

This paper presents an advanced vibration analysis of Al2O3 nanocomposite-reinforced concrete bridge structures resting on an innovative elastic foundation using the Carrera Unified Formulation (CUF). The primary objective is to investigate the dynamic response of these bridges under various loading conditions, accounting for the reinforcing effects of Al2O3 nanocomposites within the concrete matrix. The formulation incorporates a novel elastic foundation model designed to more accurately simulate realistic boundary conditions and soil-structure interaction. The accuracy and reliability of the CUF-based vibration analysis are further validated using nondestructive testing (NDT) techniques, which enable the detection of potential damage and anomalies in the bridge structures. Moreover, a machine learning (ML) algorithm is employed to predict the vibrational characteristics, facilitating an efficient comparison with the results obtained from CUF and NDT. The combination of theoretical modeling, experimental verification, and ML predictions highlights the robustness of the proposed method. The results demonstrate the effectiveness of using Al2O3 nanocomposites to enhance the mechanical properties of bridge structures, improving their vibrational performance, stability, and longevity. This study provides a comprehensive framework for future applications in bridge engineering, combining high-fidelity numerical methods with state-of-the-art testing and computational techniques.

Efficient construction and comparison of Hadamard orderings for single-pixel imaging at large frame size

The Hadamard basis is widely used for single-pixel imaging due to its orthogonal and binary properties. To achieve rapid and efficient imaging, subsampling is commonly performed, which relies on the construction of various optimized matrix orderings. However, the complex construction and substantial memory consumption limit applications at large frame sizes. In this paper, a novel method is proposed for constructing Hadamard basis orderings based on the Gray code sequence, bypassing the need to generate huge complete Hadamard basis. A significant efficiency boost is achieved with a 1000-fold reduction in the time required to construct optimized orderings compared to existing methods. Furthermore, we conducted comprehensive comparisons of the performance of different orderings under large frame sizes through both simulations and experimental validations. Our approach not only simplifies the use of optimized orderings but also significantly improves sampling and reconstruction efficiency, especially for frame sizes of 256 × 256 and higher.

EBPVis: Visual Analytics of Economic Behavior Patterns in a Virtual Experimental Environment

AbstractExperimental economics is an important branch of economics to study human behaviours in a controlled laboratory setting or out in the field. Scientific experiments are conducted in experimental economics to collect what decisions people make in specific circumstances and verify economic theories. As a significant couple of variables in the virtual experimental environment, decisions and outcomes change with the subjective factors of participants and objective circumstances, making it a difficult task to capture human behaviour patterns and establish correlations to verify economic theories. In this paper, we present a visual analytics system, EBPVis, which enables economists to visually explore human behaviour patterns and faithfully verify economic theories, e.g. the vicious cycle of poverty and poverty trap. We utilize a Doc2Vec model to transform the economic behaviours of participants into a vectorized space according to their sequential decisions, where frequent sequences can be easily perceived and extracted to represent human behaviour patterns. To explore the correlation between decisions and outcomes, an Outcome View is designed to display the outcome variables for behaviour patterns. We also provide a Comparison View to support an efficient comparison between multiple behaviour patterns by revealing their differences in terms of decision combinations and time‐varying profits. Moreover, an Individual View is designed to illustrate the outcome accumulation and behaviour patterns of subjects. Case studies, expert feedback and user studies based on a real‐world dataset have demonstrated the effectiveness and practicability of EBPVis in the representation of economic behaviour patterns and certification of economic theories.

Applying MBSE to Optimize Satellite and Payload Interfaces in Early Mission Phases

The use of model-based systems engineering (MBSE) has been increasingly explored recently in industries that require multi-discipline engineering coordination. In the European space industry, applying MBSE for the engineering of space systems has been an ongoing undertaking on many missions. In the following paper, the MBSE activities in CAMEO conducted during the A/B1 phases of a typical Earth observation satellite engineered by Airbus are discussed in detail in the form of a case study. The analyses shown are based around the modeling of the spacecraft electrical interfaces in CAMEO. This model was used to automate electrical interface control documents (EICDs) and enable the control of electrical interface development. These methodologies were further put in the context of Airbus’ satellite design processes to assess the benefits of an MBSE approach to the current electrical interface engineering procedure and the potential for the reuse of CAMEO models between satellite projects. The reduction in system engineering effort through the reuse of models to modularly create similar satellite systems for efficient concept evaluation and comparison is a clear benefit.

K-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations.

One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex data sets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse data sets and be used as a standalone tool or as part of our MDANCE clustering package.

Optimization and standardization of procedures in Forensic Identification: A methodology for description and coding of tattoos in Mexico

Tattoos serve as a valuable tool for identification. In the forensic context, it is vital to establish a systematic approach for documenting tattoo-related information to facilitate efficient and fast comparisons, especially in postmortem cases. Despite some countries failing to recognize the potential significance of tattoos, this study presents a methodological framework for gathering comprehensive data on this form of body modification. This article presents the results of an investigation made in Mexico during 2019–2022. The proposed methodology introduces a systematic and distinct classification system tailored to the country in which it will be implemented. The proposal is accompanied by applying the methodology in a Forensic Medical Service (SEMEFO) in Mexico for a week to test its effectiveness and speed under high workloads and stressful conditions. The novelty of this article lies in emphasizing the need for established, replicable, and homologous methodologies for tattoo codification. Additionally, it presents a more in-depth codification, where the details of the tattoos to be classified are thoroughly analyzed.

Feature selection using metaheuristics made easy: Open source MAFESE library in Python

Artificial intelligence (AI) often relies on feature selection (FS) to recognize and highlight the most relevant and major features in a dataset. The procedure of training and optimizing AI systems with key data points is decisive for its development and efficacy. To address this challenge, the present study introduces MAFESE, an open-source Python library that employs metaheuristic algorithms for selecting the optimal set of attributes, particularly when dealing with complex and high-dimensional data. MAFESE encompasses a wide range of feature selection techniques, including unsupervised-based, filter-based, embedded-based, and wrapper-based methods. Notably, within the wrapper-based category, MAFESE offers users access to over 200 metaheuristic algorithms, empowering them to choose the most suitable algorithm based on their specific datasets and requirements. Additionally, MAFESE incorporates built-in evaluation metrics that enable efficient comparison among various algorithms. Open-source design of MAFESE encourages cooperation within the data science community, allowing for continuous upgrades. This collaborative environment promotes the sharing of ideas, proposals, and changes, resulting in a stronger and more adaptive feature selection framework. MAFESE distinguishes itself with an easy-to-use Python interface that follows object-oriented programming concepts. It supports both experienced researchers and practitioners. MAFESE offers many resources, including documentation, examples, and test cases, for a smooth user onboarding experience. The modular architecture enables users to enhance features and interface with other tools, such as scikit-learn. MAFESE can be a valuable tool for identifying meaningful features in complicated, high-dimensional datasets, making it a significant addition to feature selection. MAFESE utilizes metaheuristic algorithms to help users tackle complex feature selection problems more efficiently and accurately. Through openness and teamwork, MAFESE may become a comprehensive resource that responds to the evolving demands of the data science community. The source code and materials of MAFESE are publicly available in the official GitHub repository at https://github.com/thieu1995/mafese.

Efficient recovery of beta-mannanase fused with LysM3014 domain in Escherichia coliby glass bead based-cell disruption method

Toward the development of whole cell biocatalysts via non-GMO strategy, in this study, three cell disruption methods (sonication, freezingand thawing and bead-shaking) have been evaluatedto recover efficiently fusion protein LysM3014-ManB containing a beta-mannanase (ManB) from Bacillus lichenifomisfused with a single LysM3014 domain derived from a putative extracellulartransglycosylase Lp_3014 of Lactobacillus plantarumWCFS1. The results showed that the highest volumetric activity of fusion protein (~46651 U/lfermentation) was obtained when shaking the recombinant E. coliwith glass bead. Moreover, the enzymatic activity of LysM3014-ManB increased approximately 1.4 folds(~66000 U/lfermentation)under the optimum conditions of glass bead-based cell disruption (falcon 50 ml, glass bead ratio of 1.0 g/mland disruption time of 8 min). The study indicates that shaking the cells with glass bead is likely to be the simplest and the most efficient cell disruption methodin comparison with other disruption methods.

Investigation of pad radius effects on multiaxial fretting fatigue behavior of al2024 alloy

Fretting fatigue, a critical phenomenon prevalent in engineering applications, occurs at the interface of contacting surfaces under cyclic loading, presenting significant challenges. The advent of Finite Element Analysis (FEA) has transformed the investigation of fretting fatigue by offering detailed insights into stress distributions and fatigue damage mechanisms. This study delves into the impact of pad radius on the fretting fatigue behavior of Al2024 alloy using FEA, with a focus on hot spot analysis and the Smith-Watson-Topper (SWT) criterion. The analysis aims to validate numerical results against analytical findings and explore the role of pad radius in determining contact behavior, fatigue life, and hot spot locations. By incorporating "fe-safe" software, computations are streamlined, facilitating efficient comparison of different fatigue initiation criteria. The results demonstrate that variations in pad radius significantly influence fretting fatigue behavior. Larger pad radii tend to distribute stresses more uniformly across the contact interface, resulting in reduced fatigue damage and longer fatigue life compared to smaller pad radii. Hot spot analysis reveals that smaller pad radii concentrate stresses at specific regions, accelerating fatigue damage initiation. The findings contribute to a deeper understanding of fretting fatigue mechanisms, shedding light on the importance of pad radius in designing more durable engineering components. Through professional scientific methodology, this study provides valuable insights into optimizing pad design to mitigate fretting fatigue and enhance the reliability of mechanical systems.

Intraoral Scanning for Monitoring Dental Wear and Its Risk Factors: A Prospective Study.

Dental wear arises from mechanical (attrition or abrasion) and chemical (erosion) factors. Despite its prevalence and clinical significance, accurately measuring and understanding its causes remain challenging in everyday practice. This one-year study with 39 participants involved comprehensive examinations and full-arch intraoral scans at the start and after 12 months. Volume loss exceeding 100 µ on each tooth's surfaces (buccal, lingual/palatine and incisal/occlusal) was measured by comparing three-dimensional scans from both time points. This study also assessed factors such as abrasion and erosion through clinical exams and questionnaires. There were no significant differences in dental wear in participants with sleep bruxism. However, noticeable wear occurred in the front teeth of those with waking bruxism and joint-related symptoms. Increased wear was associated with frequent consumption of acidic drinks, regular swimming, dry mouth, nocturnal drooling and heartburn, while no significant wear was found in patients with reflux. The used methodology proved effective in accurately assessing the progression of dental wear, which is important as many patients may initially be asymptomatic. The variability observed in dental wear patterns underscores the need to develop specific software applications that allow immediate and efficient comparison of wear areas based on extensive analysis of patient databases.

IMVis: Visual analytics for influence maximization algorithm evaluation in hypergraphs

Influence maximization (IM) algorithms play a significant role in hypergraph analysis tasks, such as epidemic control analysis, viral marketing, and social influence analysis, and various IM algorithms have been proposed. The main challenge lies in IM algorithm evaluation, due to the complexity and diversity of the spreading processes of different IM algorithms in different hypergraphs. Existing evaluation methods mainly leverage statistical metrics, such as influence spread, to quantify overall performance, but do not fully unravel spreading characteristics and patterns. In this paper, we propose an exploratory visual analytics system, IMVis, to assist users in exploring and evaluating IM algorithms at the overview, pattern, and node levels. A spreading pattern mining method is first proposed to characterize spreading processes and extract important spreading patterns to facilitate efficient analysis and comparison of IM algorithms. Novel visualization glyphs are designed to comprehensively reveal both temporal and structural features of IM algorithms’ spreading processes in hypergraphs at multiple levels. The effectiveness and usefulness of IMVis are demonstrated through two case studies and expert interviews.

Efficient Quantum Private Comparison Based on GHZ States.

Quantum private comparison (QPC) is a fundamental cryptographic protocol that allows two parties to compare the equality of their private inputs without revealing any information about those inputs to each other. In recent years, QPC protocols utilizing various quantum resources have been proposed. However, these QPC protocols have lower utilization of quantum resources and qubit efficiency. To address this issue, we propose an efficient QPC protocol based on GHZ states, which leverages the unique properties of GHZ states and rotation operations to achieve secure and efficient private comparison. The secret information is encoded in the rotation angles of rotation operations performed on the received quantum sequence transmitted along the circular mode. This results in the multiplexing of quantum resources and enhances the utilization of quantum resources. Our protocol does not require quantum key distribution (QKD) for sharing a secret key to ensure the security of the inputs, resulting in no consumption of quantum resources for key sharing. One GHZ state can be compared to three bits of classical information in each comparison, leading to qubit efficiency reaching 100%. Compared with the existing QPC protocol, our protocol does not require quantum resources for sharing a secret key. It also demonstrates enhanced performance in qubit efficiency and the utilization of quantum resources.

Energy Metabolism in Human Pluripotent Stem and Differentiated Cells Compared Using a Seahorse XF96 Extracellular Flux Analyzer.

Evaluating cell metabolism is crucial during pluripotent stem cell (PSC) differentiation and somatic cell reprogramming as it affects cell fate. As cultured stem cells are heterogeneous, a comparative analysis of relative metabolism using existing metabolic analysis methods is difficult, resulting in inaccuracies. In this study, we measured human PSC basal metabolic levels using a Seahorse analyzer. We used fibroblasts, human induced PSCs, and human embryonic stem cells to monitor changes in basal metabolic levels according to cell number and determine the number of cells suitable for analysis. We evaluated normalization methods using glucose and selected the most suitable for the metabolic analysis of heterogeneous PSCs during the reprogramming stage. The response of fibroblasts to glucose increased with starvation time, with oxygen consumption rate and extracellular acidification rate responding most effectively to glucose 4 hours after starvation and declining after 5 hours of starvation. Fibroblasts and PSCs achieved appropriate responses to glucose without damaging their metabolism 2∼4 and 2∼3 hours after starvation, respectively. We developed a novel method for comparing basal metabolic rates of fibroblasts and PSCs, focusing on quantitative analysis of glycolysis and oxidative phosphorylation using glucose without enzyme inhibitors. This protocol enables efficient comparison of energy metabolism among cell types, including undifferentiated PSCs, differentiated cells, and cells undergoing cellular reprogramming, and addresses critical issues, such as differences in basal metabolic levels and sensitivity to normalization, providing valuable insights into cellular energetics.

Enhanced MADM framework for fuzzy comprehensive evaluation of attack ability of basketball defenders with the triangular fuzzy neutrosophic numbers

With the continuous improvement of modern basketball technology, higher requirements have been put forward for the personal abilities of basketball players. As the core of an organization, the offensive ability of a defender largely determines the team’s performance. Therefore, it is necessary to objectively evaluate the attacking ability of defenders. Traditional techniques cannot objectively reflect the true level of players due to their strong subjectivity. Therefore, establishing a scientific evaluation technique is particularly important. The fuzzy comprehensive evaluation of attack ability of basketball defenders is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) technique is designed with help of cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE technique is addressed to solve the MADM. Finally, a numerical example for fuzzy comprehensive evaluation of attack ability of basketball defenders is given and some comparisons are conducted to r illustrate advantages of the designed technique. The main contribution of this paper is addressed: (1) The TFNN-CE technique is addressed under TFNSs; (2) the TFNN-CE technique is addressed for MADM under TFNSs; (2) the TFNN-CE technique for fuzzy comprehensive evaluation of attack ability of basketball defenders is addressed; (3) Through the several efficient comparisons, it is addressed that TFNN-CE technique is effective for fuzzy comprehensive evaluation of attack ability of basketball defenders.

Vertically averaged and moment equations: New derivation, efficient numerical solution and comparison with other physical approximations for modeling non-hydrostatic free surface flows

Efficient modeling of flow physics is a prerequisite for a reliable computation of free-surface environmental flows. Non-hydrostatic flows are often present in shallow water environments, making the task challenging. In this work, we use the method of weighted residuals for modeling non-hydrostatic free surface flows in a depth-averaged framework. In particular, we focus on the Vertically Averaged and Moment (VAM) equations model. First, a new derivation of the model is presented using expansions of the field variables in sigma-coordinates with Legendre polynomials basis. Second, an efficient two-step numerical scheme is proposed: the first step corresponds to solving the hyperbolic part with a second-order path-conservative PVM scheme. Then, in a second step, non-hydrostatic terms are corrected by solving a linear Poisson-like system using an iterative method, thereby resulting in an accurate and efficient algorithm. The computational effort is similar to the one required for the well-known Serre-Green-Naghdi (SGN) system, while the results are largely improved. Finally, the physical aspects of the model are compared to the SGN system and a multilayer model, demonstrating that VAM is comparable in physical accuracy to a two-layer model.

Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat.

Intraspecific variability in cold hardiness of Taurus cedar (Cedruslibani A. Rich.) in Türkiye

To better guide Cedruslibani provenances movement to northerly and higher-altitude sites for afforestation, the limits of hardiness to cold should be better known. In this study, we quantified the cold hardiness of seven C.libani provenances sampled from a provenance trial in Ankara, Türkiye, a site located outside the natural range of distribution of the species. Visual damage observation and chlorophyll fluorometry screening methods were used to assess variation in cold hardiness (LT50, the temperature estimated at which 50% of the needle tissue is damaged) among populations. Overall, C.libani can tolerate winter temperatures down to − 21.5 °C. Even though there were significant differences in cold hardiness among populations, the maximum difference was only 1.9 °C between the most and the least cold-resistant provenances. Cold tolerance was generally greater for provenances that experienced colder temperatures in March and lower levels of total precipitation in their native ranges. We also conclude that the fluorometry method provides a more efficient and stable comparison of cold hardiness than visual observation for C.libani. The results of this study may be useful for assisted migration and breeding programs, as well as for developing guidelines for genetic materials transfer, to increase productivity or adaptability of C.libani.

Exploring commercial water electrolyser systems: a data-based analysis of product characteristics

Abstract The urgency for energy transition is evident through the increasing demand for new technologies such as water electrolysers (WEs), which have the potential to generate green hydrogen using renewable electricity. This paper aims to provide a comprehensive overview of the technical capabilities of commercially available WE system products. The analysis is based on publicly accessible data gathered from 28 WE manufacturers worldwide with a total of 186 products, focusing on technology types and various technical characteristics of each WE system, including capacity, footprint, hydrogen output pressure, hydrogen purity and conversion rate. The analysis reveals that the current WE system solutions in the market exhibit diverse and varied characteristics. Further, there is a lack of standardized product specifications adopted by manufacturers. This underscores the urgent need for the development of frameworks and standards. Implementing such standards is crucial for enhancing clarity and understanding, facilitating efficient comparisons and selection processes, and supporting the future advancement of WE technologies and WE-enabled Power-to-X applications on a global scale.

Face Recognition Using Convolutional Neural Network

This study proposes a Real-time Face Recognition leveraging deep learning techniques to address the computational constraints and processing speed requirements of such applications. This model focuses on efficient feature extraction and comparison mechanisms to enable rapid and accurate face verification in real-time scenarios. By incorporating techniques like transfer learning and model compression, the proposed network achieves a balance between accuracy and computational efficiency. The utilization of deep learning enables the network to automatically learn discriminative features from facial images, enhancing the overall verification performance. Additionally, the lightweight design of the network ensures minimal resource consumption, making it suitable for deployment on low-power devices without compromising on performance. Experimental results demonstrate the effectiveness of the proposed model in achieving real-time face verification while maintaining high accuracy levels. Overall, the model presented in this study offers a promising solution for real-time face verification applications where speed and efficiency are crucial factors, showcasing the potential of deep learning techniques in enhancing biometric authentication systems.

Blue phosphorene on Au(111): theoretical, spectroscopic and diffraction analysis reveal the role of single Au adatoms.

In investigating the monoatomic layers of P, several stable two-dimensional (2D) allotropes have been theoretically predicted. Among them, single-layer blue phosphorus (BlueP) appears to deliver promising properties. After initial success, where the structure of BlueP triangular patches on Au(111) was conceived on the basis of scanning tunneling microscopy (STM) and density functional theory (DFT), the surface structure model was revisited multiple times with increasing accuracy and insight of theoretical calculations and experimental datasets. Interestingly, the quest for a reliable atomic structure model of BlueP on Au(111) turned out to be very contentious and challenging, particularly considering the possible incorporation of Au atoms in the 2D sheet of P. This article proposes an extended report on theoretical findings that can be extracted from DFT calculations of the orbital projected band structure and employed for an efficient comparison protocol between the calculations and experimental datasets obtained from angle-resolved photoemission spectroscopy (ARPES). The findings, together with experimental and simulated data from STM imaging and surface X-ray diffraction (SXRD), show a clear way to verify the presence and characterize the stabilizing effect of foreign atoms in 2D materials.