Abstract General type-2 fuzzy logic systems (GT2 FLSs) based on the $$\alpha$$ α -planes representation of general T2 fuzzy sets (FSs) have become more accessible to FL investigators in recent years. Type reduction (TR) is the most important block for GT2 FLSs. Here the weighted type-reduction algorithms based on the Newton and Cotes quadrature formulas of numerical methods of integration technique are first given, and the searching spaces are divided. Then a type of weighted divided search enhanced Karnik–Mendel (WDEKM) algorithms is shown to complete the centroid TR. In contrast to the WEKM algorithms, four simulation instances show that the WDEKM algorithms get lesser absolute errors and faster calculational speeds, which may offer the potentially application values for applying T2 FLSs.
Electronic computers. Computer science, Information technology
Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel partitioning algorithm that utilizes competition between models to detect and separate these functional patterns. This competition is induced by multiple models iteratively submitting their predictions for the dataset, with the best prediction for each data point being rewarded with training on that data point. This reward mechanism amplifies each model's strengths and encourages specialization in different patterns. The specializations can then be translated into a partitioning scheme. We validate our concept with datasets with clearly distinct functional patterns, such as mechanical stress and strain data in a porous structure. Our partitioning algorithm produces valuable insights into the datasets' structure, which can serve various further applications. As a demonstration of one exemplary usage, we set up modular models consisting of multiple expert models, each learning a single partition, and compare their performance on more than twenty popular regression problems with single models learning all partitions simultaneously. Our results show significant improvements, with up to 56% loss reduction, confirming our algorithm's utility.
This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO<sub>2</sub> emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO<sub>2</sub> emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO<sub>2</sub> equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R<sup>2</sup> ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications.
Production is a key component of every nation’s economy, yet the manufacturing sector faces major challenges and opportunities due to rapid digital transformation. Many companies have not fully adapted to these technological shifts, limiting their ability to gain competitive advantages. Research indicates that integrating digital approaches into production processes can enhance efficiency and create significant value, turning digital transformation from a strategic recommendation into a necessity. However, there is still limited guidance on how to systematically assess the digital maturity of manufacturing firms and support their progress toward higher maturity levels. This study aims to develop a comprehensive framework for evaluating digital maturity in the manufacturing sector. Drawing on both literature and empirical data, the framework was designed and validated to help organizations understand their current digital status and identify areas for improvement. It defines evaluation domains, maturity levels, and assessment criteria, along with a structured evaluation method to guide practitioners in achieving higher levels of digital transformation.
Abstract Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.
Materials of engineering and construction. Mechanics of materials, Computer software
Maria Zakirova, Alexander Korchagin, Tatyana Lazovskaya
et al.
In recent years, artificial neural networks have been used to solve a wide range of practical tasks. Neural networks can be utilized for modeling the behavior of physical systems, forecasting process dynamics, analyzing experimental data, and optimizing physical processes. The application of neural networks can be beneficial for modeling various phenomena, ranging from simple physical models to complex nonlinear systems, describing, for example, the behavior of composite mechanisms. This study demonstrates the effectiveness of a neural network approach for modeling the asynchronous motor AIR56A2. Motors of this type are well-suited for compatibility with pump equipment used in the intricate mechanical structures of facilities such as drawbridge hydraulic drives. The neural network was employed to approximate the mechanical characteristics of the motor, representing the relationship between the developed torque and rotational speed. The obtained results are compared with the approximation of this relationship using a parabola, constructed using the classical statistical method of least squares. Subsequently, the obtained approximations are employed in the numerical solution using the Euler method for the nonlinear differential equation describing the engine's dynamics. The results are then assessed against a predefined value of the steady-state angular velocity for this motor. Numerical experiments demonstrate a significant difference in outcomes when applying a multilayer perceptron to such a task compared to the classical approach. Because the neural network approximates the mechanical characteristics much more accurately, the neural network approach enables more precise results in the mathematical modeling of the motor itself.
Amina Benabid, Jing Yuan, Mohammed A.M. Elhassan
et al.
Learning multi-scale feature representations is essential for medical image segmentation. Most existing frameworks are based on U-shape architecture in which the high-resolution representation is recovered progressively by connecting different levels of the decoder with the low-resolution representation from the encoder. However, intrinsic defects in complementary feature fusion inhibit the U-shape from aggregating efficient global and discriminative features along object boundaries. While Transformer can help model the global features, their computation complexity limits the application in real-time medical scenarios. To address these issues, we propose a Cross-scale Fusion Network (CFNet), combining a cross-scale attention module and pyramidal module to fuse multi-stage/global context information. Specifically, we first utilize large kernel convolution to design the basic building block capable of extracting global and local information. Then, we propose a Bidirectional Atrous Spatial Pyramid Pooling (BiASPP), which employs atrous convolution in the bidirectional paths to capture various shapes and sizes of brain tumors. Furthermore, we introduce a cross-stage attention mechanism to reduce redundant information when merging features from two stages with different semantics. Extensive evaluation was performed on five medical image segmentation datasets: a 3D volumetric dataset, namely Brats benchmarks. CFNet-L achieves 85.74% and 90.98% dice score for Enhanced Tumor and Whole Tumor on Brats2018, respectively. Furthermore, our largest model CFNet-L outperformed other methods on 2D medical image. It achieved 71.95%, 82.79%, and 80.79% SE for STARE, DRIVE, and CHASEDB1, respectively. The code will be available at https://github.com/aminabenabid/CFNet
Yazeed Alabdulkarim, Khalid Alruwayti, Hamad Alsaleh
et al.
The demand for computing education increases due to the rapid development of technology and its involvement in most daily activities. Academic institutes offer a variety of computing majors, such as Computer Engineering, Computer Science, Information Systems, Information Technology, Software Engineering, Cybersecurity, and Data Science. Since a major objective of earning a bachelor's degree is to improve career opportunities, it is crucial to understand how the job market perceives these computing majors. This study analyzed the relationships between various computing majors and the job market in Saudi Arabia, using LinkedIn public profile data, discovering insights into the strong relationship between the focus of certain computing majors and the employment of relevant job positions. Moreover, job category trends were analyzed over the past ten years, observing that demands for System Admin and Technical Support positions declined while demands for Business Analysis and Artificial Intelligence and Data Science inclined. This study also compared earned professional certifications between different computing major graduates that correspond to job position findings.
Sometimes only some digits of a numerical product or some terms of a polynomial or series product are required. Frequently these constitute the most significant or least significant part of the value, for example when computing initial values or refinement steps in iterative approximation schemes. Other situations require the middle portion. In this paper we provide algorithms for the general problem of computing a given span of coefficients within a product, that is the terms within a range of degrees for univariate polynomials or range digits of an integer. This generalizes the "middle product" concept of Hanrot, Quercia and Zimmerman. We are primarily interested in problems of modest size where constant speed up factors can improve overall system performance, and therefore focus the discussion on classical and Karatsuba multiplication and how methods may be combined.
Samuel A. Donkor, Matthew E. Walsh, Alexander J. Titus
Computing in the life sciences has undergone a transformative evolution, from early computational models in the 1950s to the applications of artificial intelligence (AI) and machine learning (ML) seen today. This paper highlights key milestones and technological advancements through the historical development of computing in the life sciences. The discussion includes the inception of computational models for biological processes, the advent of bioinformatics tools, and the integration of AI/ML in modern life sciences research. Attention is given to AI-enabled tools used in the life sciences, such as scientific large language models and bio-AI tools, examining their capabilities, limitations, and impact to biological risk. This paper seeks to clarify and establish essential terminology and concepts to ensure informed decision-making and effective communication across disciplines.
Rajesh Natarajan, Santosh Reddy P, Subash Chandra Bose
et al.
In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control.
People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt systems. Cybersecurity refers to a collection of technical, organizational, and executive means for preventing the unauthorized use or misuse of electronic information and communication systems to ensure the continuity of their work, guarantee the confidentiality and privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this article explores the cybersecurity practices that protect computer systems from attacks, hacking, and data thefts and investigates the role of artificial intelligence in this domain. This article also summarizes the most significant literature that explore the roles and effects of machine learning and deep learning techniques in cybersecurity. Results show that machine learning and deep learning techniques play significant roles in protecting computer systems from unauthorized entry and in controlling system penetration by predicting and understanding the behaviour and traffic of malicious software.
BackgroundGlobally, the digital sources developed and available in antenatal care differ, and infrastructure challenges may impede the further development of such sources. Challenges accompanying digital developments can include the commonly occurring high workload, which affects healthcare professionals' ability to acquire professional knowledge about how to best support parents in using digital sources. Including healthcare professionals in the development process of digital sources may increase the likelihood that such sources will be adopted and employed by these professionals in their future care work. Therefore, the present research explored healthcare professionals' perceptions of the digital support intervention Childbirth Journey, which was constructed as a serious game for expectant parents.MethodsData were collected through semi-structured focus-group interviews with 11 midwives at antenatal, labour and postnatal clinics as well as with child healthcare nurses. Prior to the interviews, all participants were provided the intervention, Childbirth Journey, which is a serious game in a mobile application format consisting of two distinct parts: (1) a story-driven game and (2) a Knowledge Portal. The data were analysed using phenomenographic methods.ResultsThe perceptions of Childbirth Journey by healthcare professionals, midwives and child healthcare nurses are presented in four descriptive categories: extended professional support, trustworthy contents, diversity or individuality, and both appealing and in need of development.ConclusionsCurrent study revealed that Childbirth Journey may be utilised as a digital support for parents, allowing healthcare professionals to offer a digital solution as a complementary support to standard, face-to-face meetings with caregivers. However, the research results also revealed that some elements of Childbirth Journey must be improved, thereby representing a main contribution of this study: insights into how to better develop digital tools under the umbrella of health care. Thus, we conclude that in order to create sustainable and safe digital care solutions that function as trustworthy professional supports instead of technical products that risk harming users, the perspectives of both patients and healthcare professionals should be considered in the exploration and development of these solutions.
Uesli Alushi, Tomás Ramos, Juan José García-Ripoll
et al.
Quadratic light-matter interactions are nonlinear couplings such that quantum emitters interact with photonic or phononic modes exclusively via the exchange of excitation pairs. Implementable with atomic and solid-state systems, these couplings lead to a plethora of phenomena that have been characterized in the context of cavity QED, where quantum emitters interact with localized bosonic modes. Here, we explore quadratic interactions in a waveguide QED setting, where quantum emitters interact with propagating fields confined in a one-dimensional environment. We develop a general scattering theory under the Markov approximation and discuss paradigmatic examples for spontaneous emission and scattering of biphoton states. Our analytical and semianalytical results unveil fundamental differences with respect to conventional waveguide QED systems, such as the spontaneous emission frequency-entangled photon pairs or the full transparency of the emitter to single-photon inputs. This unlocks new opportunities in quantum information processing with propagating photons. As a striking example, we show that a single quadratically coupled emitter can implement a two-photon logic gate with unit fidelity, circumventing a no-go theorem derived for conventional waveguide-QED interactions.
Joseph T. Wong, Almaz Mesghina, Edward Chen
et al.
The COVID-19 pandemic necessitated a systematic change in course modalities due to the nationwide suspension of in-person instruction, resulting in the transition to emergency remote distance learning via Zoom. This transition certainly facilitated affordances of flexibility and continuity, but with it brought issues of unfamiliarity, lack of confidence, anxiety, distractions, and validity from both the instructors and the student perspectives. This in situ study aimed to better understand the students' learning experiences with Zoom by assessing the social, cognitive, and behavioral factors influencing learner's mind-wandering and its effect on online engagement. Undergraduate students from 14 classes across two research institutions in California (N = 633) were recruited to participate in an online survey while distance learning through a pandemic. Structural equation modeling was used to conduct a path analysis to explain the factors impacting students' online engagement mediated by students' frequency to mind-wander. Study findings revealed that (1) self-efficacy and trait anxiety had significant direct effects on students' mind-wandering; (2) self-efficacy, trait anxiety, task-value beliefs, and mind-wandering had significant direct effects on students' online engagement; and finally (3) the frequency of students' mind-wandering partially mediated the relationship between self-efficacy and engagement and between trait anxiety and engagement. Identifying these structural relationships further confirmed our hypotheses on sources contributing to students' mind-wandering while learning remotely, provided insights into potential mechanisms underpinning students' online engagement, and suggests practical pedagogical learning experience design recommendations for instructors to immediately implement while teaching and learning with Zoom..
Electronic computers. Computer science, Theory and practice of education
Jacob Penney, João Felipe Pimentel, Igor Steinmacher
et al.
Computational thinking, and by extension, computer programming, is notoriously challenging to learn. Conversational agents and generative artificial intelligence (genAI) have the potential to facilitate this learning process by offering personalized guidance, interactive learning experiences, and code generation. However, current genAI-based chatbots focus on professional developers and may not adequately consider educational needs. Involving educators in conceiving educational tools is critical for ensuring usefulness and usability. We enlisted nine instructors to engage in design fiction sessions in which we elicited abilities such a conversational agent supported by genAI should display. Participants envisioned a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences. The insights obtained in this paper can guide future implementations of tutoring conversational agents oriented toward teaching computational thinking and computer programming.
The rapid growth of technology and computer science, which has led to a surge in demand for skilled professionals in this field. The skill set required for computer science jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the market. In this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computer science job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computer science job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computer science job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices.
Genoveva Vargas-Solar, Santiago Negrete-Yankelevich, Javier A. Espinosa-Oviedo
et al.
We argue for the need for a new generation of data science solutions that can democratize recent advances in data engineering and artificial intelligence for non-technical users from various disciplines, enabling them to unlock the full potential of these solutions. To do so, we adopt an approach whereby computational creativity and conversational computing are combined to guide non-specialists intuitively to explore and extract knowledge from data collections. The paper introduces MATILDA, a creativity-based data science design platform, showing how it can support the design process of data science pipelines guided by human and computational creativity.