bluuebunny/crossref_metadata_2025 huggingface dataset:
Perspective of Future Multinational Telemedicine Efforts
Authors: Arafat Raed  , Zoicas Claudiu  , Hostiuc Filip  , Doarn Charles R.  , Latifi Rifat
Published: February 2025

Now that the concept of the Multinational Telemedicine System (MnTS) was been tested and proved to be operational, the need to go ahead and transform the research project into a functional multinational telemedicine system is the next logical step. We face several challenges and we need answers as soon as possible so momentum is not lost. Who will oversee the whole system? Who will be the main beneficiaries? How will reliable satellite communications be provided during a disaster to operate the system? How will we overpass the legal challenges regarding the practice of medicine by the physicians providing advice at a distance crossing borders virtually? All these are questions we need to answer and find solutions that allow us to transform an idea and a project to reality.
Recent Processor Technologies and Co-Scheduling
Authors: Klemm Michael  , Dahnken Christopher
Published: February 2025

Moore's law has driven processor development for several decades and has increased the available space for transistors on the area of the processor dies. Despite this increase of space, the operational frequency of the processors has stagnated or is even decreasing, due to energy and thermal constraints. The available transistors are nowadays being used to increase the number of cores, the width of SIMD parallel units, and to integrate adjacent technologies such as memory controllers and network fabric. Except for rare cases, typical applications do not utilize all available resources of large supercomputers. Some parts of the available resources are under-utilized, while others are stressed by the application. Co-scheduling strives to solve this problem by scheduling several applications that demand different components of the same resource. This can lead to a better overall utilization of the system. For effective co-scheduling, however, the execution environment has to provide quality-of-service measures to ensure that applications are not inadvertently influencing each other. Cache Allocation Technology is one of the building blocks to achieve this isolation.
Allocation-Internal Co-Scheduling
Authors: Clauss Carsten  , Moschny Thomas  , Eicker Norbert
Published: February 2025

Heading towards exascale, the challenges for process management with respect to flexibility and efficiency grow accordingly. Running more than one application simultaneously on a node can be the solution for better resource utilization. However, this approach of co-scheduling can also be the way to go for gaining a degree of flexibility with respect to process management that can enable some kind of interactivity even in the domain of high-performance computing. This chapter gives an introduction into such co-scheduling policies for running multiple MPI sessions concurrently and interactively within a single user allocation. The chapter initially introduces a taxonomy for classifying the different characteristics of such a flexible process management, and discusses actual manifestations thereof during the course of the reading. In doing so, real world examples are motivated and presented by means of ParaStation MPI, a high-performance MPI library supplemented by a complete framework comprising a scalable and dynamic process manager. In particular, four scheduling policies, implemented in ParaStation MPI, are detailed and evaluated by applying a benchmarking tool that has especially been developed for measuring interactivity and dynamicity metrics of job schedulers and process managers for high-performance computing.
New Technology Options and Threats to Detect and Combat Terrorism
Authors: Werbos Paul J.
Published: February 2025

New algorithms and hardware technology offer possibilities for the pre-detection of terrorism far beyond even the imagination and salesmanship of people hoping to apply forms of deep learning studied in the IEEE Computational Intelligence Society (CIS) decades ago. For example, new developments in Analog Quantum Computing (AQC) give us a concrete pathway to options like a forwards time camera or backwards time telegraph, a pathway which offers about a 50% probability of success for a well-focused effort over just a few years. However, many of the new technologies come with severe risks, and/or important opportunities in other sectors. This paper discusses the possibilities, risks and tradeoffs relevant to several different forms of terrorism.
Preventing Terrorism Through International Support for Border Security
Authors: Madain Jamal  , Welander Frej
Published: February 2025

Open borders ease the flow of exports and imports, of goods and services, and of individuals. Since 9/11, controlling and managing international borders has been a challenge for border control and immigration agencies around the world. It is generally agreed that in a globalized world, borders should be as open as possible, but threats from terrorists and criminals remain a problem – especially in the so-called fragile states.
Meeting Them in the Field: A Closer Look at French Foreign Fighters in Ukraine
Authors: Sautreuil Pierre
Published: February 2025

This contribution deals with the phenomenon of French volunteers in Ukraine fighting on the pro-Russian side. Here is an account of the author's encounter with some of these French foreign fighters. The author describes their background, motivations and expectations. This paper compares the involvement of French citizens on the pro-Russian side with other recent conflicts featuring French foreign fighters, and outlines the continuities and originalities of the Ukrainian case.
Sweden's National Action Plan for the Implementation of the UN Security Council's Resolutions on Women, Peace and Security (2016–2020)
Authors: Schabbauer Annika
Published: February 2025

This article explains the political context of the Swedish 1325 national action plan (NAP), how the third-generation NAP came about, and how civil society was involved in it. Its evaluation draws on evidence from practitioners, civil society organisations (CSOs), academics, and policymakers, as well as government reports. It is written from the perspective of a CSO that is contributing to debate and awareness around UNSCR 1325, both in Sweden and internationally. Sweden's background in relation to women, peace and security and its policy frameworks are discussed and the processes leading up to the adoption of the third national action plan are reviewed with attention to civil society participation. The content of the third NAP is assessed, omissions are identified, and recommendations are made about what a 1325 national action plan for Sweden should take into consideration.
Development of Pulsed Eddy Current Technique for Testing Thick Stainless Steel Plates
Authors: Sambasiva Rao K.  , Purna Chandra Rao B.  , Thirunavakkarsu S.  , Mahadevan S.
Published: February 2025

Pulsed eddy current (PEC) technique is used for detection of surface and near surface flaws in electrically conducting materials. To extend the capability of the PEC technique to detect deep sub-surface flaws in thick non-magnetic materials, it is essential to enhance the sensitivity of the PEC instrument and PEC probe. In this paper, a high-sensitive PEC instrument that driver higher excitation currents is designed and developed. Further, finite element model is used to compare the detection performance of three different PEC probe configurations and to select the optimal probe configuration for detection of deep sub-surface flaws. Using the developed PEC instrument and optimized probe, it is possible to detect flaws located at 6.0 mm below the surface in an 8.0 mm thick stainless steel (SS) plate.
The Application of 3D Printing Technology to Fabricate an Imitative Crack for Electromagnetic NDT&E Studies
Authors: Yusa Noritaka  , Chen Weixi  , Hashizume Hidetoshi
Published: February 2025

This study evaluates the applicability of 3D printing technology for creating artificial flaws that imitate real cracks for the development of electromagnetic nondestructive testing methods. A plate containing an artificial flaw with branching and electrical contact is fabricated using a powderbed-based laser metal additive manufacturing machine. Eddy current testing using an absolute pancake probe is conducted to gather the signals due to the flaw. Subsequent finite element simulations on the basis of the results of destructive testing suggests that one should design a flaw whose signals do not change so significantly even though the profile of the flaw changes almost 0.1 mm.
Variable Bandwidth Chopper Amplifier for Eddy-Current Non-Destructive Testing
Authors: Silva João  , Oliveira David  , Caetano Diogo M.  , Rabuske Taimur  , Fernandes Jorge
Published: February 2025

This work focuses on an amplifier to be integrated in an Eddy Current (EC) Non-Destructive Testing (NDT) system presented in [1]. This system is designed for detection of superficial and internal flaws on conductive materials, using magnetoresistive (MR) sensors. The sensors can be biased with a DC+AC current to allow signal heterodyning. Biasing signal is analogically removed without the use of any digital circuit. Minimum working frequency is 1.5 Hz, allowing larger penetration depths. At such low frequencies, the amplifier presents large flicker (or 1/f) noise, which is mitigated using Chopper Stabilization [2]. The amplifier rejects DC and has a controllable pole frequency that can be tuned from 1.5 Hz to 1 Mhz. This tunability is achieved by implementing a novel tunable pseudo-resistor, to reduce large settling times when changing between sensors. The amplifier has a 12 nV/at 10 Hz with flicker corner frequency at 10 kHz and a noise floor of 4 nV/. Simulations were performed in AMS 0.35 μm CMOS technology.
Inspection of Illumination Pillars Using Electromagnetic Ultrasonic Guided Wave
Authors: Nakamoto Hiroyuki  , Kojima Fumio  , Nakamura Daichi  , Miwa Arihiro  , Iwasaki Yoshitaka  , Tomita Hideaki
Published: February 2025

Maintenance of aging infrastructure facilities in urban area is a critical issue. Actually, in Kobe city, two illumination pillars fell down because they had corrosion at the base of them. This study deals an ultrasonic guided wave to detect corrosion in illumination pillars. Guided wave inspection enables quick and long-range inspection of piping systems and is also suitable for nondestructive testing of the illumination pillars. In this study, the guided wave is generated by magnetic device using a magnetostrictive effect. An acoustic emission sensor detects the reflected wave from the base of illumination pillars or corrosion. Effectiveness of the proposed method for screening of corrosion in the illumination pillars is verified through laboratory experiments using retired illumination pillars.
Dependency of Pulsed Remote Field Eddy Current Inspection of Subsurface Corrosion in Ferromagnetic Double-Casing Pipes on Parameters of Excitation Current
Authors: Li Yong  , Zhang Xiyu  , Yan Bei  , Li Wenjia  , Abidin Ilham Zainal  , Chen Zhenmao
Published: February 2025

The in-service Ferromagnetic Double-casing Pipe (FDP) employed in oil-production wells is prone to Subsurface Corrosion (SSC) which poses a severe threat to structural integrity and demands to be evaluated periodically. Pulsed Remote Field Eddy Current (PRFEC) Testing has been found capable of non-destructively evaluating SSC in FDPs. Whereas, previous research is focused on external corrosion in either inner or outer casing pipe, and lack of relevant investigation regarding the influence of parameters of the excitation current on PRFEC sensitivity to SSC. In light of this, in this paper the dependency of PRFEC sensitivity to SSC on the excitation current parameters is intensively studied, whilst 3 SSC scenarios (corrosion on external surfaces of inner and outer casing pipes; corrosion on the internal surface of the outer casing pipe) are concerned. The optimal excitation current parameters are sought via theoretical and experimental investigation.
Magnetic Flux Leakage Testing Method for Micro-Crack in Bearings Using Magnetic Inductive Head Probes
Authors: Li Erlong  , Kang Yihua  , Yan Yeliang  , Wu Wenbo
Published: February 2025

In this paper, a magnetic inductive head probe is used to detect micro-cracks in bearings. For the width of the crack in bearings is less than 15μm and the leakage filed is so weak, a flexible probe system is designed to keep the magnetic inductive head probe fit with the bearing surface closely. Micro cracks in a magnetic particle testing block are detected with the magnetic inductive head probe. The limit detecting crack size of magnetic particle inspection (MPI) is calculated by Boltzmann distribution function. Magnetic inductive head has a higher sensitivity than MPT in bearing parts inspection system.
Nondestructive Evaluation of Structural Change due to Creep Degradation in P91 Steel by Micromagnetic Properties
Authors: Matsumoto Takanori  , Uchimoto Tetsuya  , Takagi Toshiyuki  , Szielasko Klaus  , Rabung Madalina
Published: February 2025

In this study, to evaluate the structural change due to creep degradation in P91 steels nondestructively by means of the micromagnetic multiparameter microstructure and stress analysis (3MA) device, information of dislocation density change is extracted from micromagnetic properties. Micromagnetic properties of the creep and thermal aging samples are measured by 3MA device. The relationship between measured micromagnetic properties and the low-angle grain boundary length of the samples quantified as the alternative value of dislocation density by Electron Back Scatter Diffraction (EBSD) equipment are investigated. Change of dislocation density due to creep degradation can be evaluated by some micromagnetic properties which are independent on precipitation change.
NDE & SHM of Technical Objects Using Low-Cost Impedance Method
Authors: Witos Miroslaw  , Roskosz Maciej  , Fryczowski Krzysztof  , Zurek Zbigniew Hilary
Published: February 2025

The theoretical base of the low frequency impedance method and possibilities of its use in the diagnostics of paramagnetic and ferromagnetic alloys (including welds) used in transport and power engineering has been explained in the article. The main attention has been paid to the measurement of stresses in the material using low-cost measurement path, e.g. handheld automated LCR meter. During laboratory tests with five signal frequencies designated relationships describing the impact of stress on the parameters of impedance. Example results are illustrated. For quantitative analysis of observed changes in impedance used precise mathematical formulas that describe the measured resistance and inductance core with its geometry and identifiable material parameters (permeability, conductivity and permittivity). Based on the obtained results it demonstrated the possibility of using low-frequency impedance spectroscopy (multi-frequency eddy currents) and low-cost measuring equipment to monitor the level of stresses. The method can be used in NDE and SHM applications.
Eddy Current and Magnetic Barkhausen Noise Measurement Techniques as the Tools for Evaluation of Material Property Changes Induced by Metal Working
Authors: Borik Stefan  , Cap Ivo  , Neslusan Miroslav  , Capova Klara
Published: February 2025

This work deals with investigation of material property changes during metal working – concretely during milling. For eddy current detection, we are using the advanced single chip impedance converter. The device consists of AD5933 impedance converter, voltage controlled current source and MCU. The changes in impedance of a sensing probe are detected by using of four electrodes (couple of voltage, couple of current electrodes). The MBN measurements are performed by us designed device. The MBN device offers MBN measurements for wide range of excitation frequencies. A magnetization coil on a ferrite U – core is connected to an amplifier which powers this part. The MBN signal is sensed by coil placed in U – core gap and it is attached to the sample. The main aim of the work is to obtain information about the scanned surface properties using eddy current and MBN measurements.
Automation of Designing Car Safety Belts
Authors: Skarka Wojciech  , Kądzielawa Damian
Published: February 2025

Designing seat belts for modern cars is largely a routine process. Supporting the design process by conventional CAD techniques despite many benefits does not shorten time-consuming design tasks as significantly as we would expect. Only the use of Generative Modeling method can considerably automate routine part of the design process of safety belts. The key to the development of Generative Model is to develop a basis for a seat belt retractor, which determines the whole structure of seat belt assembly. The paper describes in details the design of the Generative Model of the base of a seat belt retractor and the impact of its individual parts on the final form of the belt assembly. In addition, the base is equipped with a Poka-Yoke system, integrated into the Generative Model which ensures the elimination of assembly errors. To build the Generative Model, CATIA system and Knowledgeware tools were used. The paper also shows examples of CAD models of belts assemblies developed using elaborated Generative Models.
Key Technologies for Knowledge-Based Cross-Border E-Commerce Risk Assessment – Accurate Commodity Classification and Efficient Knowledge Acquisition
Authors: Song Bo  , He Junliang  , Yan Wei  , Hu Qi  , Zhang Tianjiao
Published: February 2025

The potentially conflicting regulations of different nations add risks to cross-border e-commerce (CBEC), and the characteristic of small volume, multi-batch commodity transactions makes CBEC hard to be inspected by human experts. In this paper a knowledge-based CBEC risk assessment system is proposed and special attention is paid to the improvement of system functions including commodity classification and knowledge acquisition. While the former focuses on the accurate recognition of product types in order that right regulations can be applied to the product, the latter accomplishes the transformation from textual regulations into formatted, computer understandable rules with the least mistakes and redundancies. These two techniques collectively improves the usability of the knowledge-based CBEC risk assessment system to a practical level.
Influence of Spatial Information for the Representation of Temporal Order Information
Authors: Zhou Xiaozhou  , Xue Chengqi  , Zhou Lei  , Zhang Jing
Published: February 2025

In this age of big data, there is a discrepancy between limited display space on devices and the virtually unlimited information available to the user. This research focused on this contradiction, which we have called “space limitations”. This study employs the theories of working memory and cognitive load in two experiments that investigate the consistency of temporal order and space series to seek possible spatial representation methods for dynamic temporal order information. Using the judgments of the relative recency experiment paradigm, this study collected data on working memory performance and eye movement for consistency or inconsistency of temporal order and spatial sequence under two different conditions of system complexity. Data analysis revealed that the information of temporal order and spatial sequence were stored separately in working memory. However, in the complex system conditions, the sequence features of temporal order and spatial sequence were related, and an ordered spatial sequence improved the recall of the temporal order of information. Therefore, to improve cognitive performance, we should consider the orderly arrangement of the spacial position during the process of temporal order information visualization.
Zachman Framework in the Agile Digital Transformation
Authors: Bondar Sergej  , Hsu John C.  , Pfouga Alain  , Stjepandić Josip
Published: February 2025

Emergent behavior is behavior of a system that does not depend on its individual parts, but on their relationships to one another. Such behavior exists in biological systems, physical systems as well as in the human performance. It is an inherited nature of a System-of-Systems (SoS). A suitable framework is needed to guide the development of SoS architecture, which includes emergent behavior. Enterprise architecture (EA) is a discipline driving change within organizations. Aligning and integrating business and IT thereby belongs to strategic management. The management of EA change is a challenging task for enterprise architects, due to complex dependencies amongst EA models, when evolving towards different alternatives. In this paper, various architecture frameworks are explored for an application on SoS architecture. The use of Zachman Framework to guide the architecture development is described in step-by-step details in this paper. The agent-based simulation is recommended to develop the SoS architectural models following the Zachman Framework guidance. Ultimately, SysML and UML should be integrated with the agent-based model. An example with the collaborative engineering services for the global automotive supply chain is hereby described.
Autonomous Machining System for Optimizing Feedrates Based on Cutting Force Control
Authors: Park Hong-Seok  , Dang Duc-Viet  , Qi Bowen
Published: February 2025

Reducing machining time is one of the ways to improve the global competitiveness and productivity. The objective of this paper is to optimize feedrates of milling operation via process modeling, numerical experiments, and an intelligent algorithm under multiple constraints. The approach proposed attempts to overcome drawbacks of human error and computer-aided manufacturing (CAM) software through efficient cutting parameters of given toolpaths. First, machining simulations were performed with the support of CAM system in order to generate the NC program. The milling process was analyzed and re-simulated using the experimental platform to calculate material removal rates and instantaneous forces at the specific time. To obtain the machined accuracy, a new mechanism for milling process has been considered. The cutting force and acceleration as well as deceleration time were considered as machining constraints and integrated into the developed system. The optimizing strategy is the feedrates scheduling, which increases the feedrates to an allowable level to make the process more efficient, taking into consideration the required constraints. Subsequently, an intelligent algorithm was proposed to find real-optimal feedrates and generate more-efficient toolpaths. Finally, machining simulations using optimal values were conducted to evaluate time saving potentials. The results indicated that machining time could be reduced approximately 22%, compared to un-optimal case. Consequently, this work is expected as a contribution to make the milling process more efficient
K: A Semantic Framework for Programming Languages and Formal Analysis Tools
Authors: Rosu Grigore
Published: February 2025

We give an overview of theframework, following the lecture notes presented by the author at the Marktoberdorf Summer School in year 2016.
Big Data Analysis Using Machine Learning Approach to Compute Data
Authors: Parashar Anubha  , Parashar Apoorva  , Goyal Somya
Published: February 2025

When machine learning algorithms are applied to huge amount of data, we found difficulties to process such huge data. Now new approaches are being adopted because existing machine learning libraries doesn't have enough resources to process large datasets. So new libraries (CUDA, MapReduce, and Dryad) are adding up for concepts like parallel computing. Here we will take account of GraphLab, Apache MahoutTM, and Jubatus to get the exposure of famous academics and industrial results. Looking at the traditional machine learning techniques, tasks like to handle the data which is distributed identically or in batch mode becomes impossible and there is requirement to develop new algorithms to overcome with the existing difficulties faced by these traditional ML algorithms. The objective of this chapter is to provide overall view of developed algorithms and paradigm shifts of current big data analysis using machine learning approach to compute data. Here we will explore that the machine learning field has great impact on cloud computing paradigm. In first step we deploy various tool to the cloud like libraries and statistics tools. In second step we embed plugins with current tools in order to make Hadoop cluster on the cloud so that working programs can run on it. In third step libraries of machine learning algorithms are deployed and used for data intensive computing.
Data Intensive Computing Application for Big Data
Authors: Indira Ms.K.  , Ramya T.
Published: February 2025

The massively parallel architecture of GPUs, coming from its graphics heritage, is now delivering transformative results for scientists and researchers all over the world. For some of the world's most challenging problems in medical research, drug discovery, weather modeling, and seismic exploration – computation is the ultimate tool. Without it, research would still be confined to trial and error-based physical experiments and observation. Parallel computing: use of multiple computers or processors working together on a common task. Each processor works on its section of the problem. Processors can exchange information. In this we discuss about the parallel architecture, parallel programming models, MPI and OpenMP and CUDA programming.
Uncertainty Detection in Unstructured Big Data
Authors: Dhingra Vandana  , Datir Aniket  , Patwardhan Manasi
Published: February 2025

It's a world of full of information. Data is one of the important element of this era. One of the major sources of data is social media platforms like Twitter, Facebook etc. Everyday social media generates lot of data. It's a free form of communication where people communicate with each other without any restriction. Users can post anything on social media. However, human tendency of speaking about non-existing or source-less thing makes it unclear or create unuseful information and becomes unreliable information. This casual and word-of mouth form of communication leads to generate uncertain data and quality of information from factuality point of view becomes primary concern in social media. So, uncertainty detection is important in social media. Uncertainty detection in natural language text becomes challenging because dealing with natural language text is pretty complicated thing. Uncertainty is an important field of linguistics. Basically, it means “lack of information”. So, a statement whose truth value cannot be determined is considered as uncertain. Linguistics and Natural Language Processing aims at classifying factual and uncertain proposition. In this chapter, comparisons of classification algorithm have been experimented to detect uncertain propositions in Twitter data of food price crisis. Comparative analysis of classification algorithm Naive Bayes and Support Vector Machine approach is done to detect uncertain propositions of tweets related to food price crisis. A model is trained to classify certain or uncertain proposition using a training file which is annotated using cue words available in English language text. Output of algorithm is the class showing given proposition is certain or uncertain. The objective of this chapter is to have a comparative analysis of text classification approach to detect uncertain events of Twitter data of food price crisis and to improve the accuracy of uncertainty classification approaches in order to detect uncertain events in natural language processing.
Big Data Analytics for Prevention and Control of HIV/AIDS
Authors: Ramasamy V  , Gomathi B  , Sarkar Joy Lal  , Panigrahi Chhabi Rani  , Pati Bibudhendu
Published: February 2025

The objective of this chapter will be a key reference for researchers in the field of HIV/AIDS research with huge amount of data collection across the world with medical and all general information related to HIV/AIDS research. It overcomes (replaces) the existing system huge data management, processing problems with Big Data Analytics. This chapter provides the current HIV/AIDS research activities, issues and solutions, government policies and census information across the world. Also provides and concludes the information regarding HIV/AIDS Big Data Analytics, Models, Security and Privacy features, Big Data management techniques, quick decision making approach, Algorithms for the Prediction, Classification, Visualization, Clustering, Optimization and Distributed Processing problems and future scope.
Accurate Prediction of Life Style Based Disorders by Smart Healthcare Using Machine Learning and Prescriptive Big Data Analytics
Authors: Sharma Manik  , Singh Gurvinder  , Singh Rajinder
Published: February 2025

IT based healthcare industry is succeeding in leaps and bounds. Nowadays, the massive volume and variety of healthcare data is available. Numbers of procedures have been designed for early diagnosis of human disorders. Here, the critical analysis of different machine learning algorithms in early lifestyle based disease diagnosis has been carried out. Authors have used different techniques like naïve bayes, random forest, neural network, k-NN, C4.5, decision tree etc. Depending upon the sample size of data, the rate of accuracy in forecasting diabetes, cardio and cancer lies in 67–100%, 85–100% and 90–98% respectively. With the passage of time nature, volume, variety and veracity have tremendously changed. Therefore, it is intricate to envision the way the big data may influence the social, corporate and health industries. The effective use of Machine Learning (ML) and Prescriptive Big Data Analytics (PBDA) may assist in developing smart and complete healthcare solution for early diagnosis, treatment and prevention of diseases. Therefore, a smart healthcare framework based upon machine learning and prescriptive big data analytics is designed for accurate prediction of lifestyle based human disorders. The use of prescriptive analytics will improve the accuracy of different machine learning techniques in diagnosis of different life style based human disorders. Finally, the novelty lies in the coexistence of big data, data warehouse and machine learning techniques.
S-ARRAY: Highly Scalable Parallel Sorting Algorithm
Authors: Sharma Shubham  , Mishra Priyanka  , Mittal Mamta
Published: February 2025

The problem of sorting a sequence of N elements on a parallel computer with K processors has been discussed since 1975 when parallel computing came into existence. Presently, with exponential increase in amount of data and computing power, a sorting algorithm is required where all the N available processors are utilized without an increase in overhead of merging. Due to increase in computation power, research of new sorting algorithms has been slowed down. Our proposal S-Array algorithm, is an approach of parallel execution of sort operation based on “Divide and Conquer” principal in a general multiprocessor architecture like NUMA (Non Uniform Multiprocessor Architecture). S-Array is based on master–slave configuration that consists of processors in multiples of four where input is given in form of 22n. S-Array is designed in such a way that even if we increase number of input data and number of processing units, time complexity to sort increased amount of input data remains same i.e. O(log(log(n))/2)+c. Our future work lies in computing performance of S-Array when it sorts huge data in different architectures.
Analysis of Tuberculosis Images Using Differential Evolutionary Extreme Learning Machines (DE-ELM)
Authors: Priya E.  , Srinivasan S.
Published: February 2025

In this work, an attempt has been made to demarcate Tuberculosis (TB) sputum smear positive and negative images using statistical method based on Gray Level Co-occurrence Matrix (GLCM). The sputum smear images (N=100) recorded under standard image acquisition protocol are considered for this work. Haralick descriptor based statistical features are calculated from the sputum smear images. The most relevant features are ranked by principal component analysis. It is observed that the first five principal components contribute more than 96% of the variance for the chosen significant features. These features are further utilized to demarcate the positive from negative smear images using Support Vector Machines (SVM) and Differential Evolution based Extreme Learning Machines (DE-ELM). Results demonstrate that DE-ELM performs better than SVM in terms of performance estimators such as sensitivity, specificity and accuracy. It is also observed that the generalization learning capacity of DE-ELM is better in terms of number of hidden neurons utilized than the number of support vectors used by SVM. Thus it appears that this method could be useful for mass discrimination of positive and negative TB sputum smear images.
Big Data & Deep Data: Minding the Challenges
Authors: Bhatia Madhulika  , Mittal Mamta  , Madhurima
Published: February 2025

Now a day's big data is an important topic in corporate as well as in academics. The root of big data is the ability to study and analyze large sections of information to search for patterns and finding the trends. The root of big data is analytics, After applying the analytics in will lead to many findings that were undiscovered before. Big data simply take existing data and looks into a different way. Deep data on the other hand gathered data on daily basis and lined it with experts of industry. The main role of deep data is to section down the massive amount of data in Exabyte's or perabytes exclude the information that is duplicate or use less. But there are many challenges in switching the current scenario from Big data to Deep data. We have many machine learning approaches that can be applied to Big data. Deep learning is one of those machine learning approaches. But there are many challenges that are to be addressed. The objective is to discuss the various challenges in analyzing Big data as well as Deep data using Deep learning.
Depth Crack Evaluation Using Multi-Harmonic Excitation and GMR Sensors
Authors: Ribeiro A.L.  , Pasadas D.J.  , Ramos H.G.
Published: February 2025

This paper reports an experimental study to detect and distinguish surface and sub-surface cracks in non-ferromagnetic materials using eddy current testing with a multi-harmonic excitation signal. A minimization of the crest factor of the excitation signal was done using a Shapiro-Rudin phase sequence. The chosen range of frequencies was based on a corresponding range of standard depths of penetration (SDP) in order to ensure that the eddy currents penetrate more or less an aluminum plate with 4 mm of total thickness. Experimental tests were realized on surface and sub-surface cracks with depths of 0.5, 1, 1.5 and 2 mm. The results obtained by the ECT probe are analyzed in the frequency domain.
EM Sensor System for Characterisation of Advanced High Strength Strip Steels
Authors: Jolfaei Mohsen  , Shen Jialong  , Smith Andrew  , Zhou Lei  , Davis Claire
Published: February 2025

In order to obtain accurate quality control of steel products, it is desirable to be able to monitor the mechanical properties non-destructively. This study proposes using an electromagnetic (EM) sensor, suitable for use on strip samples, as a tool for non-destructive steel characterisation, in particular phase balance and grain size in dual phase (DP) steels and hence strength. It is known that the low frequency inductance measured using an EM sensor depends on the relative permeability of the sample and that the permeability is affected by microstructural features (i.e. phase fraction/distribution and, to a lesser extent, grain size are the important features in DP steel). EM sensors can be used to characterise austenite and ferrite fraction in hot strip mills (EMspecTMsystem) and for statistical correlations to mechanical properties (IMPOC and HACOM systems) in cold strip mills. In this paper an EM sensor system and a finite element based sensor – microstructure model have been used to characterize DP microstructures (using commercial DP600, DP800 and DP1000 grades and a heat treated DP600 grade) taking into account the effect of strip thickness on the signals. This paper also reports on the relationship between the ferrite fraction and tensile strength, which follows the expected relationship from the literature, and the magnetic permeability (determined from the EM sensor signal which is directly related to ferrite fraction and grain size) and tensile strength. The ability of the EM sensor to determine the tensile strength is therefore illustrated taking into account strip thickness and microstructure.
Eddy Current System for Clad Pipe Inspection
Authors: Camerini Cesar  , Rebello João M.A.  , Braga Lucas C.  , Santos Rafael W.F.  , Santos João M.C.  , Chady Tomasz  , Pereira Gabriela R.
Published: February 2025

Orthogonal eddy current sensors operating in differential mode was applied to evaluate fatigue cracks in clad pipelines circumferential welds. A dedicated electronic hardware was developed to drive the sensors and measure the electrical impedance. In the preliminary experiments, an automated inspection was performed with the goal to evaluate sensors detectability and different scanning speed was tested to reproduce in service situation. The results have shown that the presented eddy current transducer is a potential solution for a fatigue crack detection on a clad circumferential weld bead.
Implications of Reduced-Precision Computations in HPC: Performance, Energy and Error
Authors: Cherubin Stefano  , Agosta Giovanni  , Lasri Imane  , Rohou Erven  , Sentieys Olivier
Published: February 2025

Error-tolerating applications are increasingly common in the emerging field of real-time HPC. Proposals have been made at the hardware level to take advantage of inherent perceptual limitations, redundant data, or reduced precision input [20], as well as to reduce system costs or improve power efficiency [19]. At the same time, works on floating-point to fixed-point conversion tools [9] allow us to trade-off the algorithm exactness for a more efficient implementation. In this work, we aim at leveraging existing, HPC-oriented hardware architectures, while including in the precision tuning an adaptive selection of floating- and fixed-point arithmetic.
Design-Time Analysis for the READEX Tool Suite
Authors: Kumaraswamy Madhura  , Chowdhury Anamika  , Gerndt Michael
Published: February 2025

Energy efficiency and consumption are now the most important and challenging issues in current Petascale and in designing future Exascale computing systems. The European Union Horizon 2020 READEX project uses an online approach to exploit application dynamism and tune large-scale HPC applications to improve energy efficiency and performance. The paper presents the READEX methodology, consisting of the Design-Time Analysis and Runtime Application Tuning, and describes the pre-analysis steps involving application dynamism and significant region detection. During design-time, the READEX tuning plugin evaluates configurations of hardware and software tuning parameters to determine the best settings for instances of application regions. The runtime tuning dynamically switches to the best configuration for an application region during production runs. Finally, the energy savings obtained for LULESH on the Taurus supercomputer highlight the effectiveness of this methodology.
Deeply Heterogeneous Many-Accelerator Infrastructure for HPC Architecture Exploration
Authors: Flich J.  , Cilardo A.  , Kovaç M.  , Tornero R.  , Gagliardi M.  , Fusella E.  , Martínez J.M.  , Picornell T.
Published: February 2025

It is well known that heterogeneous computing is being adopted in HPC systems, with the adoption of GPGPUs and manycore accelerators, and currently with the early adoption of FPGA systems. The FETHPC MANGO project emerged to analyze future heterogeneous manycore architectures for HPC. In this paper we describe the details of the heterogeneous architecture proposed in the MANGO project. The paper focuses mainly on the description of the interconnect design, the accelerators layout strategy, and the design of manycore accelerators for specific target applications within MANGO. We conclude the paper with the description of one of the targeted applications within MANGO.
Highly Parallel Lattice QCD Wilson Dirac Operator with FPGAs
Authors: Janson Thomas  , Kebschull Udo
Published: February 2025

In this paper, we discuss a highly parallel implementation of the lattice QCD Wilson Dirac operator on FPGAs. The operator is described as a data-flow graph using OpenSPL and acts on a four-dimensional lattice collecting all next neighbour terms. The so implemented kernel fits on an Altera Stratix V FPGA using fixed-point arithmetic. This allows us to compute all arithmetic operations simultaneously. In addition, the OpenSPL language shows also an implicit optimized locality expressed by the offset operator which is studied on the presented implementation. The lattice is held in DDR3 memory on the FPGA accelerator card and streamed into the FPGA over six memory channels where we use the MAX4 card and the software framework from Maxeler. The operator is memory bound and has an equivalent arithmetic intensity of 0.92 FLOPs/Byte. With a clock frequency of 133 MHz, we get an equivalent theoretical peak performance of 176 GFLOP/s. Therefore, we also address the memory interface and memory access pattern.
The INFN COSA Project: Low-Power Computing and Storage
Authors: Cesini Daniele  , Corni Elena  , Falabella Antonio  , Ferraro Andrea  , Lama Luca  , Morganti Lucia  , Calore Enrico  , Schifano Sebastiano Fabio  , Michelotto Michele  , Alfieri Roberto  , De Pietri Roberto  , Boccali Tommaso  , Biagioni Andrea  , Lo Cicero Francesca  , Lonardo Alessandro  , Martinelli Michele  , Paolucci Pier Stanislao  , Pastorelli Elena  , Vicini Piero
Published: February 2025

In the context of energy-efficient “green” computing, we present the experience of the COSA project (Computing On SoC Architecture, www.cosa-project.it), started in 2014 and funded by the Italian Institute for Nuclear Physics (INFN).
Multifactorial Hypothesis and Multi-Targets for Alzheimer's Disease
Authors: Gong Cheng-Xin  , Liu Fei  , Iqbal Khalid
Published: February 2025

The amyloid cascade hypothesis has been dominating drug discovery for Alzheimer's disease (AD) for the last two decades. The failure of the development of effective drugs for slowing down or reversing the progression of AD warrants the AD field to consider out-of-the-box thinking and therapeutic approaches. We propose the multifactorial hypothesis of AD, emphasizing that AD is caused by multiple etiological factors, which may result in common brain pathology and functional consequences through several separate but integrated molecular pathways. More than one etiological factor and mechanistic pathway may be involved in a single individual with sporadic AD, and different individuals may have different etiological factors, involving different mechanisms/pathways. We urge the recognition of the multifactorial nature of AD and the paradigm shift of AD drug development from a single target to multiple targets, either with the multitarget-directed ligands approach or the cocktail therapy approach. We believe that patient stratification and the use of the precision medicine model will also benefit AD drug discovery.
Dementia Research: Populations, Progress, Problems, and Predictions
Authors: Hunter Sally  , Smailagic Nadja  , Brayne Carol
Published: February 2025

Alzheimer's disease (AD) is a clinicopathologically defined syndrome leading to cognitive impairment. Following the recent failures of amyloid-based randomized controlled trials to change the course of AD, there are growing calls for a re-evaluation of basic AD research. Epidemiology offers one approach to integrating the available evidence. Here we examine relationships between evidence from population-based, clinicopathological studies of brain aging and a range of hypotheses from all areas of AD research. We identify various problems, including a lack of systematic approach to measurement of clinical and neuropathological factors associated with dementia in experimental and clinical settings, poor understanding of the strengths and weaknesses of different observational and experimental designs, a lack of clarity in relation to disease definitions from the clinical, neuropathological, and molecular perspectives, inadequate characterization of brain aging in the human population, difficulties in translation between laboratory-based and population-based evidence bases, and a lack of communication between different sections of the dementia research community. Population studies highlight complexity and predict that therapeutic approaches based on single disease features will not be successful. Better characterization of brain aging in the human population is urgently required to select biomarkers and therapeutic targets that are meaningful to human disease. The generation of detailed and reliable evidence must be addressed before progress toward therapeutic interventions can be made.
The Relationship between Obstructive Sleep Apnea and Alzheimer's Disease
Authors: Andrade Andreia G.  , Bubu Omonigho M.  , Varga Andrew W.  , Osorio Ricardo S.
Published: February 2025

Obstructive sleep apnea (OSA) and Alzheimer's disease (AD) are highly prevalent conditions with growing impact on our aging society. While the causes of OSA are now better characterized, the mechanisms underlying AD are still largely unknown, challenging the development of effective treatments. Cognitive impairment, especially affecting attention and executive functions, is a recognized clinical consequence of OSA. A deeper contribution of OSA to AD pathogenesis is now gaining support from several lines of research. OSA is intrinsically associated with disruptions of sleep architecture, intermittent hypoxia and oxidative stress, intrathoracic and hemodynamic changes as well as cardiovascular comorbidities. All of these could increase the risk for AD, rendering OSA as a potential modifiable target for AD prevention. Evidence supporting the relevance of each of these mechanisms for AD risk, as well as a possible effect of AD in OSA expression, will be explored in this review.
Brain Inflammation Connects Cognitive and Non-Cognitive Symptoms in Alzheimer's Disease
Authors: Selles M. Clara  , Oliveira Mauricio M.  , Ferreira Sergio T.
Published: February 2025

Alzheimer's disease (AD) is the main form of dementia in the elderly and affects greater than 47 million people worldwide. Care for AD patients poses very significant personal and economic demands on individuals and society, and the situation is expected to get even more dramatic in the coming decades unless effective treatments are found to halt the progression of the disease. Although AD is most commonly regarded as a disease of the memory, the entire brain is eventually affected by neuronal dysfunction or neurodegeneration, which brings about a host of other behavioral disturbances. AD patients often present with apathy, depression, eating and sleeping disorders, aggressive behavior, and other non-cognitive symptoms, which deeply affect not only the patient but also the caregiver's health. These symptoms are usually associated with AD pathology but are often neglected as part of disease progression due to the early and profound impact of disease on memory centers such as the hippocampus and entorhinal cortex. Yet, a collection of findings offers biochemical insight into mechanisms underlying non-cognitive symptoms in AD, and indicate that, at the molecular level, such symptoms share common mechanisms. Here, we review evidence indicating mechanistic links between memory loss and non-cognitive symptoms of AD. We highlight the central role of the pro-inflammatory activity of microglia in behavioral alterations in AD patients and in experimental models of the disease. We suggest that a deeper understanding of non-cognitive symptoms of AD may illuminate a new beginning in AD research, offering a fresh approach to elucidate mechanisms involved in disease progression and potentially unveiling yet unexplored therapeutic targets.