bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainable Manufacturing of Precision Miniature Gears by Abrasive Water Jet Machining – An Experimental Study
Authors: Phokane Thobi  , Gupta Kapil
Published: February 2025

The accelerated global competitiveness and strict environmental regulations have largely been responsible to encourage manufacturers to adopt sustainable or green practices in manufacturing. Whether it is manufacturing of macro or micro products, sustainable techniques are being given considerable importance to improve quality, and enhance productivity with keeping the environment clean and green simultaneously. This article reports on the sustainable manufacturing of precision miniature gears by abrasive waterjet machining (AWJM) technology. The fabricated spur gear has 8400 μm pitch diameter, 12 teeth, and 5000 μm thickness. Considering its cleaner production aspects and significant benefits over other manufacturing techniques, AWJM process was selected to manufacture precision miniature gears. The manufactured gears were undergone a series of post manufacturing inspection and testing to evaluate their geometric accuracy and surface integrity. It resulted in high manufacturing quality (profile error-14.15 μm), superfine surface finish (having average roughness-1.03 μm), and compressive residual stresses with high hardness at the tooth flanks for the fabricated miniature gear. Investigation reported is also accompanied by a short comparative study between AWJM and gear hobbing to manufacture precision miniature gears of the same specification. It reveals that AWJM is capable to fabricate miniature gears of high quality and surface integrity at significantly lower cost and with less environmental footprints compared to the conventional processes.
Prediction of Soil Sand and Clay Contents via Visible and Near-Infrared (Vis-NIR) Spectroscopy
Authors: Tümsavaş Zeynal  , Tekin Yücel  , Ulusoy Yahya  , Mouazen Abdul M.
Published: February 2025

Visible and near infrared (Vis-NIR) spectroscopy is a non-destructive analytical method that can be used to complement, enhance or potentially replace conventional methods of soil analysis. The aim of this research was to predict the particle size distribution (PSD) of soils using a Vis-NIR) spectrophotometry in one irrigate field having a vertisol clay texture in the Karacabey district of Bursa Province, Turkey. A total of 86 soil samples collected from the study area were subjected to optical scanning in the laboratory with a portable, fiber-type Vis–NIR spectrophotometer (AgroSpec, tec5 Technology for Spectroscopy, Germany). Before the partial least square regression (PLSR) analysis, the entire reflectance spectra were randomly split into calibration (80%) and validation (20%) sets. A leave-one-out cross-validation PLSR analysis was carried out using the calibration set with Unscrambler® software, whereas the model prediction ability was tested using the validation (prediction) set. Models developed were used to predict sand and clay content using on-line collected spectra from the field. Results showed an “excellent” laboratory prediction performance for both sand (R2= 0.81, RMSEP = 3.84% and RPD = 2.32 in cross-validation; R2= 0.90, RMSEP = 2.91% and RPD = 2.99 in the prediction set) and clay (R2= 0.86, RMSEP = 3.4% and RPD = 2.66 in cross validation; R2= 0.92, RMSEP = 2.67% and RPD = 3.14 in the prediction set). Modelling of silt did not result in any meaningful correlations. Less accurate on-line predictions were recorded compared to the laboratory results, although the on-line predictions were very good (RPD = 2.24-2.31). On-line predicted maps showed reasonable spatial similarity to corresponding laboratory measured maps. This study proved that soil sand and clay content can be successfully measured and mapped using Vis-NIR spectroscopy under both laboratory and on-line scanning conditions.
Monitoring Two Mangrove Forest of Rio Grande do Norte, Brazil, Using Multi-Temporal Satellite Data
Authors: Lima Matheus Cardim Ferreira  , de Souza Flavo Elano Soares  , Lima João Cardim Ferreira  , de Oliveira Josenalde Barbosa
Published: February 2025

This study aims to detect changes in vegetation and evaluate the geographic distribution of two different mangroves of Rio Grande do Norte, Brazil. Data were obtained through visual interpretation of high resolution satellite imagery (Astrium / QuickBird / DigitalGlobe / Landsat) available in Google EarthTM. The visual interpretation resulted in the demarcation of polygons representing vegetated area of mangroves. Data were compared to the images of global distribution of mangroves in 2011, provided by United Nations Environment Programme of Global Conservation Monitoring Centre using GIS. The mangrove forest present in Guaraíras lagoon complex had an increase in vegetated area over the last four years of 5.01 km (52.73%) while the mangrove forest located in Potengi estuary presented a 1.03km (6.06%) of forest gain in the same period. Anthropogenic activities such as shrimp farming, artificial construction, tourism and discharge of effluents are directly related with the conservation status of Brazilian mangroves. The mangrove ecosystem present in the metropolitan region of the Rio Grande do Norte (largest population area), showed a slow growth rate, while the mangrove located further away from urban centers, showed impressive growth of vegetated area.
Designing Systems for Terroristic Indicators Analysis Based on Visible, Social and Dark Web Data Sources
Authors: Soldatos John
Published: February 2025

This article introduces the main principles and the design of a system for identifying terroristic indicators and predicting terroristic actions based on processing of information from social media and the dark web. The presented system (which is conveniently called VIGILANT) is aimed at responding to recent challenges stemming from the increased exploitation of the internet by terroristic groups, as a means of conducting psychological warfare, gaining publicity, spreading propaganda, mining/collecting information about their targets, raising funds for their activities, recruiting and mobilizing supporters, as well as planning and coordinating their attacks. The article is not confined to the presentation of the technical design of a platform for social media and dark web information processing, but discusses legal and privacy aspects as well.
Benchmarking Telemedicine: Developing Metrics That Matter
Authors: Stone Carla Sydney
Published: February 2025

In the early stages of an emerging technology, such as telemedicine, there is no single universal model of service. Benchmarking the use of telemedicine within a country, a region or in a variety of locations is valuable as usage is guided by local conditions. Benchmarked results can be used to establish commonalities of practice, to improve the monitoring and evaluation of individual practices, to reduce risk, and to improve system sustainability. The key to measuring actionable information is the development of appropriate metrics. This chapter proposes a set of metrics that can be used in benchmarking the analytics so they can be used to build a sustainable telemedicine system measured in improved patient outcomes.
Telemedicine in Disasters – A Review of International Efforts
Authors: Doarn Charles R.
Published: February 2025

Disasters occur when we least expect and when we are often not prepared. There are many differences across the globe on the ability to respond and these differences are based almost entirely on socioeconomic conditions in the affected society or region. A highly developed nation has a lower risk of catastrophic destruction and death as compared to a developing nation. Nevertheless, healthcare in the immediate and post or chronic phase of a disaster is vital to recovery, stability and security. Over the past 5–6 decades, telemedicine has been integrated successfully in disasters and has been shown to be effective. This chapter summarizes this previous work and highlights the NATO-developed Multinational Telemedicine Systems for Disasters. It lays out the award winning strategy of Initiate, Build, Operate, and Transfer, developed by the International Virtual e-Hospital and implemented across several Balkans countries. It provides a summary of the challenges telemedicine faces in the integration of healthcare. The chapter closes out with a summary of the many resources that are available for using telemedicine in disasters.
Scala Programming for Big-Data Application
Authors: Balamurugan B  , Sridhar D  , Vignesh S
Published: February 2025

Big data has several applications like page bank algorithm, recommendation system, sentimental analysis, and many more. Recommendation is the key to success for discovering and retrieval of content in this era of huge data [1]. Association Rule Mining (ARM) algorithm is mostly used for recommendation systems. User behaviour is given input to ARM in key/value pair format which can be generated by Map-Reduce function [2]. ARM attempts to find frequent item sets among large datasets and describes the association relationships among different attributes. Market-Basket analysis is the major application of ARM used to predict customer's purchasing behaviour and recommend product to customer [3]. For implementing this recommendation systems there are several programming languages and scala is one among them which is an object oriented and also supports functional programming [4]. For processing large amount of data, functional programming is faster and easier, but in some cases we need to provide security for sensitive data. Also scala runs on Java Virtual Machine (JVM) and also scala can execute Java code since scala code is converted to Java Byte code when compiled. So scala will have a better advantage to implement big data applications.
Patterns for High Performance Computing and Big Data: State of the Art and Semantic Approaches
Authors: Di Martino Beniamino  , Esposito Antonio
Published: February 2025

Because of the steadily growing volume and complexity of the data exchanged through the Internet and among connected devices, and the need to rapidly elaborate the incoming information, new challenges have been posed to High Performance Computing (HPC). Several architectures, programming languages, Cloud services and software have been proposed to efficiently deal with modern HPC applications, in particular with Big Data, and solve the related issues that arise. As a consequence, users often find it difficult to select the right platform, application or service fitting their requirements. In order to support them in deploying their applications, Patterns have been adopted and exploited, providing a base canvas on which users can develop their own applications. In this paper we offer an overview of modern Pattern advancements for HPC and Big Data, also providing an insight on semantic-based technologies which have been successfully applied to provide a flexible representation thereof.
Programming Big Data Analysis on Clouds and Extreme Scale Systems
Authors: Talia Domenico
Published: February 2025

A main research goal of IT nowadays is to investigate and design new scalable solutions for big data analysis. This goal asks for coupling scalable algorithms with high-performance programming tools and platforms. Addressing these challenges requires a seamless integration of the scalable computing techniques and big data analytics research approaches and frameworks. In fact, scalability is a key item for big data analysis and machine learning applications. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of HPC systems and clouds, whereas in the next future exascale systems will be used to implement extreme scale data analysis. This chapter introduces and discusses cloud models that support the design and development of scalable data mining applications and report on challenges and issues to be addressed and solved for developing data analysis algorithms on extreme-scale systems.
How Data Analysis Supports Crime Prediction in Smart Cities
Authors: Altomare Albino  , Catlett Charlie  , Cesario Eugenio  , Talia Domenico
Published: February 2025

Today, about 55 per cent of the world's population lives in urban areas, a proportion that is expected to increase to 66 per cent by 2050. Such a steadily increasing urbanization is already bringing huge social, economic and environmental transformations and, at the same time, poses big challenges in city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. To face such challenges, the exploitation of information coming from urban environments and the development of Smart City applications to enhance quality, improve performance and safety of urban services, are key elements. This chapter discusses how the analysis of urban data may be exploited for forecasting crimes and presents an approach, based on seasonal auto-regressive models, for reliably forecasting crime trends in urban areas. In particular, the main goal of this work is to discuss the impact of data mining on urban crime analysis and design a predictive model to forecast the number of crimes that will happen in rolling time horizons. As a case study, we present the analysis performed on an area of Chicago. Experimental evaluation results show that the proposed methodology can achieve high predictive accuracy for long term crime forecasting, thus can be successfully exploited to predict the time evolution of the number of crimes in urban environments.
Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SF-CNNLS) for Intra-Class Variation of Vehicle Type Recognition
Authors: Awang Suryanti  , Nik Azmi Nik Mohamad Aizuddin
Published: February 2025

Vehicle type recognition has become an important application in Intelligence Transportation Systems (ITSs) to provide a safe and efficient road and transportation infrastructure. There are some challenges in implementing this technology including the complexity of the image that will distract accuracy performance, and how to differentiate intra-class variation of the vehicle, for instance, taxi and car. In this paper, we propose to use a deep learning framework that consists of a Sparse-Filtered Convolutional Neural Network with Layer Skipping (SF-CNNLS) strategy to recognize the vehicle type. We implemented 64 sparse filters in Sparse Filtering to extract discriminative features of the vehicle and 2 hidden layers of CNNLS for further processes. The SF-CNNLS can recognize the different types of vehicles due to the combined advantages of each approach. We have evaluated the SF-CNNLS using various classes of vehicle namely car, taxi, and truck. The implementation of the evaluation is during daylight time with different weather conditions and frontal view of the vehicle. From that evaluation, we able to correctly recognize the classes with almost 91% of average accuracy and successfully recognize the taxi as a different class of car.
Virtual Robotic Arm Control with Hand Gesture Recognition and Deep Learning Strategies
Authors: Martin Sagayam K.  , Vedha Viyas T.  , Ho Chiung Ching  , Henesey Lawrence E.
Published: February 2025

Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-time applications. A robotic arm which is portable to carry various places and which can be easily programmed to do any work of a hand and is controlled by using deep learning techniques. Deep hand is a combination of both virtual reality and deep learning techniques. It estimated the active spatio-temporal feature and the corresponding pose parameter for various hand movements, to determine the unknown pose parameter of hand gestures by using various deep learning algorithms. A novel framework for hand gestures has been made to estimate by using a deep convolution neural network (CNN) and a deep belief network (DBN). A comparison in terms of accuracy and recognition rate has been drawn. This helps in analyzing the movement of a hand and its fingers which can be made to control a robotic arm with high recognition rate and less error rate.
Imaging of External Corrosion in Nonmagnetic Pipes via GPEC
Authors: Yan Bei  , Li Yong  , Ren Shutin  , Li Wenjia  , Zainal Abidin Ilham Mukriz  , Chen Zhenmao
Published: February 2025

Due to corrosive and hostile environment, in-service nonmagnetic pipes are prone to such anomalies as the external corrosion which poses a severe threat to structural integrity. Gradient-field Pulsed Eddy Current technique (GPEC) has been found superior to other non-destructive evaluation (NDE) techniques particularly pulsed eddy current testing in evaluation and imaging of subsurface corrosion in planar conductors. In this paper, GPEC for evaluation and imaging of external corrosion in nonmagnetic pipes is investigated. Two orthogonal gradient-field signals are simultaneously acquired and used for visualization of external corrosion. Through experimental investigation, it has been found that: (1) the depth of the external corrosion can be implied by the acquired corrosion images; and (2) the corrosion boundaries can be detected and identified via GPEC.
Product Uniformity Control – A Research Collaboration of European Steel Industries to Non-Destructive Evaluation of Microstructure and Mechanical Properties
Authors: Van Den Berg F.D.  , Kok P.J.J.  , Yang H.  , Aarnts M.P.  , Meilland P.  , Kebe T.  , Stolzenberg M.  , Krix D.  , Zhu W.  , Peyton A.J.  , Martinez-De-Guerenu A.  , Gutierrez I.  , Jorge-Badiola D.  , Malmström M.  , Volker A.  , Duijster A.  , Wirdelius H.  , Boström A.  , Mocci C.  , Vannucci M.  , Colla V.  , Davis C.  , Zhou L.  , Schmidt R.  , Labbé S.  , Reboud C.  , Skarlatos A.  , Leconte V.  , Lombard P.
Published: February 2025

In steel manufacturing, the conventional method to determine the mechanical properties and microstructure is by offline, destructive (lab-)characterisation of sample material that is typically taken from the head or the tail of the coil. Since coils can be up to 7 km long, the samples are not always representative for the main coil body. Also, the time delay (typically a few days) between the actual production and the availability of the characterisation results implies that these results cannot be exploited for real-time adaptation of the process settings.
Characterizing of Corrosion Backside of Steel Plates Using Extremely Low-Frequency Eddy Current Testing with Multiple-Frequency Magnetic-Field Exposure
Authors: Goda Tsuyoshi  , Haga Yuta  , Tomioka Takuya  , Sakai Kenji  , Kiwa Toshihiko  , Tsukada Keiji
Published: February 2025

In recent years, aging and deterioration of social infrastructure has become a major problem. A non-destructive testing technique for early detection of degradation (such as internal corrosion) is required to extend the lifetime of these structures. Currently, there are various methods used for inspection. Among these, non-destructive testing using magnetic fields is widely used due to advantages such as ease of use and high speed. However, magnetic inspection is generally employed only for inspection of the surface and subsurface, since it is difficult to inspect internal corrosion using this technique. Therefore, we have developed a device for detecting thinning of steel plates due to corrosion using multiple extremely-low frequencies and a high-sensitivity magnetic sensor. However, it is necessary to sweep multiple frequencies to obtain a frequency spectrum, and this is very time-consuming, especially in extremely low-frequency ranges. In this study, we have developed an analysis method based on a multiple-frequency applied magnetic field and FFT (fast Fourier transform) analysis of the detection signal. Thus, we have successfully reduced the measurement time while maintaining high detection accuracy.
A New Probe for Velocity Induced Eddy Current Inspection
Authors: Ramos Helena Geirinhas  , Rocha Tiago  , Ribeiro Artur Lopes
Published: February 2025

Velocity induced eddy current inspection is a technique to inspect conductive materials. It has a good detection performance and is particularly well suited to situations where the speed is a factor to be considered because its sensibility increases with the moving speed. This paper assesses the field produced by several probe geometries in the detection of linear machined defects. A more detailed analysis is carried out for a probe that includes an array of cubic magnets to induce the eddy currents inside the sample and Hall sensors to measure the resultant magnetic field.
Detect Dampness in Building Walls by Solving the Inverse Problem in Electrical Impedance Tomography
Authors: Rymarczyk Tomasz  , Tchórzewski Paweł  , Sikora Jan
Published: February 2025

The paper presents a measurement system based on electrical tomography. It will be an innovative solution for an evaluation study of the level of dampness of the walls and the condition of the building. Both in terms of the measuring method as the reconstruction algorithm. The application of modern tomographic techniques in conjunction with topological algorithms will allow to perform a non-invasive and very accurate spatial assessment of the dampness level. The authors of this paper propose a new kind of connection with the use of imaging techniques hybrid solution with surface electrodes.
Linear Sampling Method for Localizing Small Anomalies in Microwave Imaging
Authors: Park Won-Kwang
Published: February 2025

We design a linear sampling method to retrieve the shape of an anomaly in microwave imaging. It is is based on the structure of nonzero singular vectors associated with the nonzero singular values of a complex-symmetric matrix, whose elements are measured S-parameters. Simulation results shows the effectiveness and limitations of the linear sampling method in real-world microwave imaging.
Nondestructive Evaluation of Hardening Degree of Epoxy Resin in CFRP with Eddy Current Testing
Authors: Kosukegawa Hiroyuki  , Yamada Rie  , Tamonoki Shinya  , Sato Noriyuki  , Ura Keisuke  , Takagi Toshiyuki
Published: February 2025

Chemiluminescence anlaysis and high-frequency eddy current testing (HF-ECT) were examined as nondestructive evaluation techniques to assess the hardening degree of epoxy resin in carbon fiber-reinforced plastic. Both techniques showed a correlation with the hardening degree of epoxy resin measured by differential scanning calorimetry. The results also indicated that the application of HF-ECT at frequencies of >50 MHz is suited for hardening degree determination.
Evaluation of Electrical Conductivity of Metals via Monotonicity of Time Constants
Authors: Su Zhiyi  , Ye Chaofeng  , Giovinco Gaspare  , Ventre Salvatore  , Udpa Lalita  , Tamburrino Antonello
Published: February 2025

This manuscript addresses the “classical” problem of estimating electrical conductivity of metals. The novel contribution of this work is the use of a new set of features, namely time constants of the source-free response in a pulsed eddy current testing (PECT) experiment. Time constants characterize the source-free response and increase monotonically with the electrical conductivity of the specimen. Time constants form a particularly important set of features because they do not depend upon the probing system and, hence, they are not sensitive to probe lift-off and tilting which are responsible for significant experimental errors.
A Parallel Simulator of Quench in Superconducting Magnets
Authors: Calvelli Valerio  , Ciaccio Giuseppe  , Di Benedetto Fabio
Published: February 2025

In a superconducting magnet the null resistivity of the cable allows to run a very intense electrical current without dissipating heat. During operation, a region of the superconducting wire can turn to normal state (quench) with possible damage due to intense heat dissipation. Quench simulation is thus a necessary step during magnet design; it is a multi-physics computational problem that ends up very easily with tens of millions of equations in as many unknowns. In this paper we attempt an HPC approach for quench simulation.
SPUX: Scalable Particle Markov Chain Monte Carlo for Uncertainty Quantification in Stochastic Ecological Models
Authors: Šukys Jonas  , Kattwinkel Mira
Published: February 2025

Calibration of individual based models (IBMs), successful in modeling complex ecological dynamical systems, is often performed only ad-hoc. Bayesian inference can be used for both parameter estimation and uncertainty quantification, but its successful application to realistic scenarios has been hindered by the complex stochastic nature of IBMs. Computationally expensive techniques such as Particle Filter (PF) provide marginal likelihood estimates, where multiple model simulations (particles) are required to get a sample from the state distribution conditional on the observed data. Particle ensembles are re-sampled at each data observation time, requiring particle destruction and replication, which lead to an increase in algorithmic complexity. We present SPUX, a Python implementation of parallel Particle Markov Chain Monte Carlo (PMCMC) algorithm, which mitigates high computational costs by distributing particles over multiple computational units. Adaptive load re-balancing techniques are used to mitigate computational work imbalances introduced by re-sampling. Framework performance is investigated and significant speed-ups are observed for a simple predator-prey IBM model.
Performance Evaluation and Optimization of MagnetoHydroDynamic Simulation for Planetary Magnetosphere with Xeon Phi KNL
Authors: Fukazawa Keiichiro  , Soga Takeshi  , Umeda Takayuki  , Nanri Takeshi
Published: February 2025

The magnetohydrodynamic (MHD) simulation is often applied to study the global dynamics and configuration of a planetary magnetosphere for the space weather. In this paper, the computational performance of MHD code is evaluated with 128 nodes Xeon Phi KNL of Cray XC40. As the results, the 2D and 3D domain decompositions of SoA (structure of array) make the effective performances and AoS (array of structure) and hybrid parallel computation become low performances. Adding the performance optimizations for Xeon Phi to our MHD simulation code, then we have obtained 2.4 % increase of execution efficiency in total and we achieved 3 TFlops performance gain using 128 nodes.
State-Aware Concurrency Throttling
Authors: De Sensi Daniele  , Kilpatrick Peter  , Torquati Massimo
Published: February 2025

Reconfiguration of parallel applications has gained traction with the increasing emphasis on energy/performance trade-off. The ability to dynamically change the amount of resources used by an application allows reaction to changes in the environment, in the application behavior or in the user's requirements. A popular technique consists in changing the number of threads used by the application (Dynamic Concurrency Throttling). Although this provides good control of application performance and power consumption, managing the technique can impose a significant burden on the application programmer, mainly due to state management and redistribution following the addition or removal of a thread. Nevertheless, some common state access patterns have been identified in some popular applications. By leveraging on this knowledge, we will describe how it is possible to simplify the state management procedures following a Concurrency Throttling operation.
Communication Avoiding Neumann Expansion Preconditioner for LOBPCG Method: Convergence Property of Exact Diagonalization Method for Hubbard Model
Authors: Yamada Susumu  , Imamura Toshiyuki  , Machida Masahiko
Published: February 2025

The exact diagonalization is the most accurate approach for solving the Hubbard model. The approach calculates the ground state of the Hamiltonian derived exactly from the model. Since the Hamiltonian is a large sparse symmetric matrix, we usually utilize an iteration method. It has been reported that LOBPCG method with a shift-and-invert preconditioner using an approximate eigenvalue can solve the problem efficiently. However, the preconditioner does not take effect for the Hamiltonian whose off-diagonal elements are predominant. In this research, we apply the preconditioner using the Neumann expansion and propose the communication avoiding strategy in consideration of the physical property of the Hubbard model. We show that the preconditioner improves the convergence property and decreases the elapsed time for the Hamiltonian with the predominant off-diagonal elements.
Energy Saving and Thermal Management Opportunities in a Workload-Aware MPI Runtime for a Scientific HPC Computing Node
Authors: Cesarini Daniele  , Bartolini Andrea  , Benini Luca
Published: February 2025

With the advent of a new generation of supercomputers characterized by tightly-coupled integration of a large-number of powerful processing cores in the same die, energy and temperature walls are looming threats to the growth in computational power.
Optimizing a RBF Interpolation Solver for Energy on Heterogeneous Systems
Authors: Schiffmann Patrick  , Martin Dirk  , Haase Gundolf  , Offner Günter
Published: February 2025

Mesh deformation is a performance critical part of many problems in fluid dynamics. Radial basis function (RBF) interpolation based methods for mesh deformation have addressed the increasing complexity for larger data sets. Recently a domain decomposition method has been introduced which allows mapping these algorithms well to distributed memory systems. Because heterogeneous systems have proven to be more time and energy efficient for some applications, the HPC resources available to engineering users and researchers become increasingly heterogeneous.
GPU-Accelerated and Storage-Efficient Implementation of the QR Decomposition
Authors: Benner Peter  , Köhler Martin  , Penke Carolin
Published: February 2025

The LAPACK routines GEQRT2 and GEQRT3 can be used to compute the QR decomposition of a matrix of size m×n as well as the storage-efficient representation of the orthogonal factor. A GPU-accelerated algorithm is presented that expands a blocked CPU-GPU hybrid QR decomposition to compute the triangular matrix T. The storage-efficient representation is used in particular to access blocks of the matrix Q without having to generate all of it. The algorithm is presented in two variants using one and two GPUs, respectively. To avoid redundant computations or communication between devices, a scheme is developed to additionally computeduring the iteration. As a result the algorithm outperforms the standard LAPACK routine by a factor of 3 for square matrices on a single GPU and by a factor of 5 on two GPUs.
Long Range Forces in a Performance Portable Molecular Dynamics Framework
Authors: Saunders William Robert  , Grant James  , Müller Eike Hermann
Published: February 2025

Molecular Dynamics (MD) codes predict the fundamental properties of matter by following the trajectories of a collection of interacting model particles. To exploit diverse modern manycore hardware, efficient codes must use all available parallelism. At the same time they need to be portable and easily extendible by the domain specialist (physicist/chemist) without detailed knowledge of this hardware. To address this challenge, we recently described a new Domain Specific Language (DSL) for the development of performance portable MD codes based on a “Separation of Concerns”: a Python framework automatically generates efficient parallel code for a range of target architectures.
Towards a Unified CPU–GPU Code Hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures
Authors: Oteski Ludomir  , Colin De Verdière Guillaume  , Contassot-Vivier Sylvain  , Vialle Stéphane  , Ryan Juliet
Published: February 2025

In this paper, we suggest a different methodology to shorten the code optimization development time while getting a unified code with good performance on different targeted devices. In the scope of this study, experiments are illustrated on a Discontinuous Galerkin code applied to Computational Fluid Dynamics. Tests are performed on CPUs, KNL Xeon-Phi and GPUs where performance comparison confirms that the GPU optimization guideline leads to efficient versions on CPU and Xeon-Phi for this kind of scientific applications. Based on these results, we finally suggest a methodology to end-up with an efficient hybridized CPU–GPU implementation.
Vectorization Strategies for Ant Colony Optimization on Intel Architectures
Authors: Montesinos Victoriano  , García José M.
Published: February 2025

This paper presents an efficient parallel and vectorized implementation of three different selection functions (Roulette Wheel, I-Roulette and DS-Roulette) for tour construction (the most time-consuming part of the Ant Colony Optimization bio-inspired metaheuristic) targeting two Intel multi-core processors and the Knights Corner Intel Xeon Phi coprocessor. The results show that our best implementation (with I-Roulette as selection function) on Xeon Phi 7120P runs up to 78.98x faster compared to its sequential counterpart on a Xeon v2 CPU.
Task Based Parallelism with OpenMP: A Case Study with DL_POLY_4
Authors: Chalk Aidan B.G.  , Elena Alin M.
Published: February 2025

When performing computations where load balancing is complex, dynamic load balancing is becoming increasingly necessary. In this paper we examine one method to perform dynamic load balancing, known as Task-Based Parallelism.
A Tool to Support FastFlow Program Design
Authors: Gazzarri Leonardo  , Danelutto Marco
Published: February 2025

We describe the implementation of ff-RPL, a shell to support structured parallel programming development in FastFlow. The shell provides ways to explore the space of functionally equivalent, alternative parallel implementations of the same application with different non functional properties. The tool is entirely written in C++ and has been designed in such a way it can be easily extended to take into account new non functional features, refactoring and optimization rules, as well as different parallel patterns. Preliminary experimental results are shown relatively to the code generation part.
Power Modelling for Heterogeneous Cloud-Edge Data Centers
Authors: Chen Kai  , Varghese Blesson  , Kilpatrick Peter  , Nikolopoulos Dimitrios S.
Published: February 2025

Existing power modelling research focuses not on the method used for developing models but rather on the model itself. This paper aims to develop a method for deploying power models on emerging processors that will be used, for example, in cloud-edge data centers. Our research first develops a hardware counter selection method that appropriately selects counters most correlated to power on ARM and Intel processors. Then, we propose a two stage power model that works across multiple architectures. The key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual selection methods reported in literature, and (ii) the two stage power model can predict dynamic power more accurately on both ARM and Intel processors when compared to classic power models.
A Blood Test for Alzheimer's Disease: Progress, Challenges, and Recommendations
Authors: Kiddle Steven J.  , Voyle Nicola  , Dobson Richard J.B.
Published: February 2025

Ever since the discovery of APOE ϵ4 around 25 years ago, researchers have been excited about the potential of a blood test for Alzheimer's disease (AD). Since then researchers have looked for genetic, protein, metabolite, and/or gene expression markers of AD and related phenotypes. However, no blood test for AD is yet being used in the clinical setting. We first review the trends and challenges in AD blood biomarker research, before giving our personal recommendations to help researchers overcome these challenges. While some degree of consistency and replication has been seen across independent studies, several high-profile studies have seemingly failed to replicate. Partly due to academic incentives, there is a reluctance in the field to report predictive ability, to publish negative findings, and to independently replicate the work of others. If this can be addressed, then we will know sooner whether a blood test for AD or related phenotypes with clinical utility can be developed.
High Performance Computing and Big Data Convergence: A Technical Review
Authors: Mirtaheri Seyedeh Leili  , Grandinetti Lucio
Published: February 2025

The convergence of High Performance Computing (HPC) and Big Data Analytics (BDA) has been the center of attention for past few years. HPC and BDA have separate software stacks and from financial point, it is impossible to invest in both categories at the same time. HPC is traditionally compute intensive while BDA is data intensive. In this paper we investigate the convergence of HPC and BDA from a technical perspective. We first review the state of the art and introduce the common frameworks of HPC and BDA. We also compare these frameworks in terms of scalability, data rate, data size, fault tolerance and real-time processing. We further compare the software stacks for HPC and BDA as convergence challenges and discuss on existing solutions to convergence.
GPU-Based Iterative Hill Climbing Algorithm to Solve Symmetric Traveling Salesman Problem
Authors: Talawar Basavaraj  , Yelmewad Pramod
Published: February 2025

Traveling Salesman Problem (TSP) is an NP-hard combinatorial optimization problem. Approximation algorithms have been used to reduce the worst-case factorial time complexity of TSP to non-deterministic polynomial time successfully. However, approximation methods result in a sub-optimal solution as they do not cover the search space adequately. Further, CPU implementations of approximation methods are too time consuming for large input instances. On the other hand, GPUs have been shown to be effective in exploiting data and memory level parallelism in large, complex problems.
Glencoe – A Tool for Specification, Visualization and Formal Analysis of Product Lines
Authors: Schmitt Anna  , Bettinger Christian  , Rock Georg
Published: February 2025

Manufacturers of all business areas are faced with growing customer demands and the corresponding complexity of their products. The need to offer highly customized variants becomes obvious for example in the automotive industry: innovative cars with configurable features move customers to buy newer models in increasingly shorter intervals. However, the costs are expected to be nearly stable from the customer's perspective. An industry approved approach to systematical design customizable products at reasonable costs are multivariant product lines: product line engineers compose models describing common and distinctive product features as well as logical constraints. Subsequently these models can be analyzed and optimized. In this paper, we present a free variant management tool called Glencoe. Glencoe is a web application developed and hosted at Trier University of Applied Sciences. Therefore, it is not necessary to install any software; it is accessible from any computer or mobile device. Glencoe allows the user to specify and visualize feature models using different views which emphasize specific aspects. The application is connected to several well-known and freely available theorem provers and SAT solvers. Thus, the user is able to analyze the product line for consistency, the number of product configurations and the existence of dead features. A set of metrics helps to evaluate the product line healthiness. Glencoe is intuitive to use since it is built according to state-of-the-art user interaction paradigms originating from mobile application development. As a rapid prototyping tool, it allows beginners as well as experts to build and analyze product lines.
Modelling Stepped-Lap Adhesive Joints by the Extended Finite Element Method
Authors: Machado R.M.D.  , Campilho R.D.S.G.  , Rocha R.J.B.  , Silva F.J.G.
Published: February 2025

For the joint strength prediction of bonded joints, Fracture Mechanics-based techniques are often used. In this context, the tensile (GIC) and shear toughness (GIIC) are two of the most important parameters to predict the joint behaviour. The Finite Element Method (FEM) has been used for strength prediction in the last decades. Cohesive zone modelling (CZM) coupled to a FEM analysis is generally accepted as an accurate method. More recently, the Extended Finite Element Method (XFEM) has emerged. This work aims to validate the XFEM to predict the behaviour of stepped-lap joints with different overlap lengths (LO). Three adhesives, Araldite®AV138, Araldite®2015 and Sikaforce®7752, whose properties are quite different, were used in the analysis. For the XFEM strength prediction, different damage initiation criteria were used using either stresses or strains. The XFEM was found to be adequate to predict the joint strength using the Quadratic Stress (QUADS) and Maximum Stress (MAXS) damage initiation criteria.
Industry 4.0 Through Organizational Interoperability Perspective: A Multicriteria Decision Analysis
Authors: Gomes Pedro F.O.  , Bordini Giovani A.  , Loures Eduardo F.R.  , Santos Eduardo A.P.
Published: February 2025

Technologies have been increasingly integrated into productive systems as a strategic model to change the paradigms of the current productive structure and accelerate the process of attending market requirements. In this context, organizational interoperability allows the barriers to non-integration to be identified and promote more effective actions to contribute to cooperation between productive systems. The 4th industrial revolution – Industry 4.0 – operationalizes this integration in the sense of approaching the process and the market through the dissemination of information and effective communication among stakeholders in the supply chain. Aiming this, the required attributes for Industry 4.0 implementation must be adequately identified in order to promote the success of the supply chain evolution. The multicriteria decision analysis methods – Promethee and DEMATEL – were adopted with the purpose of evaluating and prioritizing the components of the Industry 4.0 domain and associating them with the Automotive Supply Chain domain, structured from the Organizational Interoperability perspectives. The results were generated by a survey developed with academic and industry experts from the automotive supply. As a result, it was possible to identify which Automotive Supply Chain attributes are most influenced by the elements of Industry 4.0, finally allowing a greater understanding throughout interrelations between such domains.
Service Design of Urban Bloodmobile Based on PSS Design Support Tools
Authors: Wang Wei  , Hu Fei
Published: February 2025

Extensive research has been conducted on product–service systems (PSS) in Business Management, Engineering & Design and Information Systems since the 1990s. The core definition of PSS across all disciplines can be formulated as follows: An integrated bundle of products and services which aims at creating customer utility and generating value. In the Engineering & Design field, PSS emphasises concepts such as PSS engineering approach, sustainability and PSS design. Focusing on PSS design, several design support tools have been developed. In this study, affordance-based PSS design support tools are used in a preliminary screening machine design for urban bloodmobile. First, a service blueprint of PSS on the urban bloodmobile was constructed, and then the service and product elements of ‘provide preliminary screening service’ were analysed. Using a function–activity interaction matrix, the localisation solution of quality service experience for urban bloodmobile from the perspective of PSS was explored in this study, and the effectiveness of PSS design method was demonstrated by integrating product and service elements using affordances. At the same time, appropriate adjustment and expansion were necessary when these methods were applied to localisation design.
Digital Manufacturing and Virtual Commissioning of Intelligent Factories and Industry 4.0 Systems Using Graph-Based Design Languages
Authors: Beisheim Nicolai  , Kiesel Markus  , Rudolph Stephan
Published: February 2025

The development process for the new kind of Intelligent Factories and Industry 4.0 systems requires a different approach than before, because the complexity and flexibility of all industrial systems increases. The development process has to ensure that every product is manufactured at the end with an efficient production process. Under these conditions, data management and the use of simulations become more important. Furthermore, the couplings and interrelations between product and means of production play a key role in the areas of digital factory and virtual commissioning. Only by using both simulation methods it will become apparent how the product can be manufactured. But to create a virtual commissioning model, a lot of data is needed from the entire development process. This data is usually generated interdisciplinarily by people from various departments. However, today's form of data management as information transfer is only conditionally suitable for efficiently building up a digital factory as a simulation model or even making a virtual commissioning of a production system. Due to the fact that in the development process a lot of IT systems with interfaces inbetween are used, loss of information is quite common experience. This is where graph-based design languages come in. Using this modeling language approach, the product is completely digitally described and then the means of production are derived from the product properties automatically. This creates a consistent digital product life cycle from the initial product requirements to virtual commissioning.
Creating Strategies to Improve the Use of IT- and IS-Systems in Final Assembly
Authors: Fast-Berglund Åsa  , Li Dan  , Åkerman Magnus
Published: February 2025

Assembly systems are complex due to mass customization. In order to be able to produce a variance of products a well functional information system is vital. Companies need a digital strategy in order to design adaptive systems to increase flexibility and usage of information support systems (IS-Systems) among operators. In case studies from Swedish industries, it is shown that operators often use their own experience when assembling products. This paper aims to describe how a digital strategy can be developed, combining IT-systems and IS-system to create dynamic systems for operators. The paper will combine two methods, MEET-model that has been used to improve meeting situations but could also be used to create digital strategies at companies. An additional use of the Delone and McLean model will be used to create the bridge between the IT-system and the IS-system. Results show that combining these models gives a clearer and broader view of the digital strategy and a possible bridge between IT- and IS-developers to create a better and more dynamic shop-floor IT system.
Assembly Line Balancing Type-E with Technological Requirement: A Mathematical Model
Authors: Fathi Masood  , Nourmohammadi Amir  , Ng Amos H.C.
Published: February 2025

This study is motivated by a real-world assembly line in an automotive manufacturing company and it addresses the simple assembly line balancing problem type-E (SALBPE). The SALBPE aims to maximize the balance efficiency (BE) through determining the best combinations of cycle time and station number. To cope with the problem, a mixed integer nonlinear programming (MINLP) model is proposed. The MINLP model differs from the existing ALBPE models as it includes the technological requirements of assembly tasks and optimizes the variation of workload beside the BE. The validity of the proposed model is tested by solving the real-world case study and a set of benchmark problems.