bluuebunny/crossref_metadata_2025 huggingface dataset:
Suction – swelling relations for Warsaw clays
Authors: Skutnik Z.  , Garbulewski K.
Published: February 2025

The paper presents recent experimental testing of expansive clays distinguished in foundations of the Warsaw subway, a high-rise hotel building and the water intake at the Stegny test site. Swelling parameters of clays were assessed by classification methods and direct laboratory measurements (standard consolidometer), in which soil volume and stress changes were measured during specimen saturation with water. Moreover, the swelling potential of Warsaw clays was determined by soil suction measurement. The tests were carried out using suction controlled UPC consolidometer, in which the “axis – translation” technique was applied. The results of tests were interpreted within the context of a constitutive model for expansive, partially saturated soils. After analysis of test results, the relationship between soil suction and swelling potential for Warsaw clays is discussed. The soil suction concept provides more reliable estimation of the clay volume change (swelling) than other methods used in the geotechnical engineering practice.
Test study on behavior of interface between structure and coarse grained soil
Authors: Zhang Ga  , Zhang Jian-Min
Published: February 2025

The monotonic and cyclic behavior of the interface between structure and coarse grained soil was investigated through tests. A series of tests on the interfaces between ideal rough steel plate and gravel were conducted with a large-scale test apparatus. The stress and deformation of the interface were measured. The behavior of such kind of interface were therefore discovered or summarized, including: (1) the shear strength was well proportional to the normal stress; (2) no obvious strain softening behavior was exhibited; (3) the normal displacement gradually accumulated with well-regulated variation within a shear cycle; (4) the behavior evolved to a stable state; (5) the mechanical response was related to the shear direction; and (6) the roughness of structure, the behavior of the soil and the normal stress were main influence factors on the behavior.
Moving factors of regional geodynamics of the Caspian Sea
Authors: Aitaliev Sh.M.  , Baimakhan R.B.  , Sydykov A.A.  , Muzdakbaev M.M.
Published: February 2025

Methods for description of moving factors in geological environment reflecting natural evolution of geological deformation and moving. With reference to regional tectonics of the north-east section of the Caspian Sea, man-caused influence due to development of a large-scale oil field is shown in a diagram.
Development of an in situ method to measure the nonlinear shear modulus of soil
Authors: Stokoe II Kenneth H.  , Rathje Ellen M.  , Axtell Paul J.
Published: February 2025

One of the key variables in evaluating the response of geotechnical materials during strong earthquake shaking is their nonlinear behavior. This behavior is often expressed in terms of the nonlinear reduction in shear modulus with increasing shearing strain and the corresponding increase in material damping ratio. Currently, there is total dependency on laboratory testing to measure these properties. A generalized test method is under development at the University of Texas at Austin to measure nonlinear soil properties in situ. The method involves applying static and dynamic loads at the surface of a soil deposit using a mobile, electro-hydraulic shaker and measuring the dynamic response of the soil mass beneath the loaded area using embedded instrumentation. The resulting field test is a load-controlled dynamic test that induces nonlinearity in the soil. Initial prototype studies have focused on measuring linear and nonlinear shear moduli over a range in stress states. These studies are discussed, and measurments in a sand deposit are presented. The linearity and nonlinearity measured in situ compare well with laboratory results and empirical trends.
3D finite element analysis of bearing capacity failure in clay
Authors: Evans Mark D.  , Griffiths D.V.
Published: February 2025

Three-dimensional finite element analysis is a useful tool for practical problem solving as well as for parametric analysis and failure mechanism visualization. In this paper, Mohr-Coulomb, elasto-viscoplastic, bearing capacity analyses were performed using finite element code running on massively parallel computers. With the recent advent of PC Clusters, parallel computing is readily available to those who may not have access to parallel supercomputers. Square, rectangular, and strip footings are considered to determine bearing capacity factors. Values obtained by these finite element analyses are comparable to those values reported in the literature. Speedup values for increased numbers of processors in the parallel environment are also presented and discussed.
Image analysis of shallow foundation tests on sand ground and their FE-analysis using a new elasto-plastic model
Authors: Sreng S.  , Ueno K.  , Mochizuki A.  , Ma X.
Published: February 2025

In this study a series of loading tests on dense sand under a centrifugal force field was carried out, and a newly developed image analysis method with an accuracy of 0.2 pixels was used to observe detailed deformation of sand models. Loading tests were also analyzed using a two-dimensional FE-analysis with a developed double yield surface model, referred as the MMX-model. The calculated bearing capacity factor, Nγ, compares well with other reported data and that obtained from classical theory. Load-settlement relationships, displacement and strain distributions at pre-peak, peak, and post-peak stages obtained from the FE-analysis agree well with those obtained from the loading tests. The FE-analysis using MMX-model is concluded to be applicable to practical design work with sufficient accuracy to permit prediction of bearing capacity and deformation behavior of ground.
Online Auctions for Dynamic Assignment: Theory and Empirical Evaluation
Authors: Gujar Sujit  , Faltings Boi
Published: February 2025

Dynamic resource assignment is a common problem in multi-agent systems. We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions. We study the trade-off between the following online auction properties: (i) truthfulness, (ii) expressiveness, (iii) efficiency, and (iv) average case performance. We theoretically and empirically compare four different online auctions: (i) Arrival Priority Serial Dictatorship, (ii) Split Dynamic VCG, (iii) e-Action, and (iv) Online Ranked Competition Auction. The latter is a novel design based on the competitive secretary problem. We show that, in addition to truthfulness and algorithmic efficiency, the degree of competition also plays an important role in selecting the best algorithm for a given context.
Interruptible Task Execution with Resumption in Golog
Authors: Gierse Gesche  , Niemueller Tim  , Claßen Jens  , Lakemeyer Gerhard
Published: February 2025

Mobile robots should perform a growing number of tasks and react to time-critical events. Thus, the ability to interrupt a task and resume it later is crucial. While interleaved execution occurs often in robotics, existing approaches do not consider the fact that interrupting a task and resuming an interrupted task often requires intermediate steps. In this paper we present an approach to interruptible task execution with resumption. We propose INTRGOLOG which extends INDIGOLOG by task interruption and resumption through introducing new constructs to determine and fulfill the requirements of tasks. Our experiments on a service robot and in simulation show that the ability to switch to another task enables a robot to react in a swift and reliable fashion to new events.
Towards Online Concept Drift Detection with Feature Selection for Data Stream Classification
Authors: Hammoodi Mahmood  , Stahl Frederic  , Tennant Mark
Published: February 2025

Data Streams are unbounded, sequential data instances that are generated very rapidly. The storage, querying and mining of such rapid flows of data is computationally very challenging. Data Stream Mining (DSM) is concerned with the mining of such data streams in real-time using techniques that require only one pass through the data. DSM techniques need to be adaptive to reflect changes of the pattern encoded in the stream (concept drift). The relevance of features for a DSM classification task may change due to concept drifts and this paper describes the first step towards a concept drift detection method with online feature tracking capabilities.
Mixed Strategy Extraction from UCT Tree in Security Games
Authors: Karwowski Jan  , Mańdziuk Jacek
Published: February 2025

In this paper a simulation-based approach to finding optimal defender strategy in multi-act Security Games (SG) played on a graph is proposed. The method employs the Upper Confidence Bounds applied to Trees (UCT) algorithm which relies on massive simulations of possible game scenarios. Three different variants of the algorithm are presented and compared with each other as well as against the Mixed Integer Linear Program (MILP) exact solution in terms of computational efficiency and memory requirements. Experimental evaluation shows that the method has a few times lower memory demands and is faster than MILP approach in majority of test cases while preserving quality of the resulting mixed strategies.
Analysing Approximability and Heuristics in Planning Using the Exponential-Time Hypothesis
Authors: Aghighi Meysam  , Bäckström Christer  , Jonsson Peter  , Ståhlberg Simon
Published: February 2025

Cost-optimal planning has become a very well-studied topic within planning. Needless to say, cost-optimal planning has proven to be computationally hard both theoretically and in practice. Since cost-optimal planning is an optimisation problem, it is natural to analyse it from an approximation point of view. Even though such studies may be valuable in themselves, additional motivation is provided by the fact that there is a very close link between approximability and the performance of heuristics used in heuristic search. The aim of this paper is to analyse approximability (and indirectly the performance of heuristics) with respect to lower time bounds. That is, we are not content by merely classifying problems into complexity classes — we also study their time complexity. This is achieved by replacing standard complexity-theoretic assumptions (such as P ≠ NP) with the exponential time hypothesis (ETH). This enables us to analyse, for instance, the performance of the h+heuristic and obtain general trade-off results that correlate approximability bounds with bounds on time complexity.
Situation Calculus Game Structures and GDL
Authors: De Giacomo Giuseppe  , Lespérance Yves  , Pearce Adrian R.
Published: February 2025

We present a situation calculus-based account of multi-players synchronous games in the style of general game playing. Such games can be represented as action theories of a special form, situation calculus synchronous game structures (SCSGSs), in which we have a single action tick whose effects depend on the combination of moves selected by the players. Then one can express properties of the game, e.g., winning conditions, playability, weak and strong winnability, etc. in a first-order alternating-time μ-calculus. We discuss verification in this framework considering computational effectiveness. We also show that SCSGSs can be considered as a first-order variant of the Game Description Language (GDL) that supports infinite domains and possibly non-terminating games. We do so by giving a translation of GDL specifications into SCSGSs and showing its correctness. Finally, we show how a player's possible moves can be specified in a Golog-like programming language.
Length of Stay in Intensive Care Units – A Case Base Evaluation
Authors: Silva Ana  , Vicente Henrique  , Abelha António  , Santos M. Filipe  , Machado José  , Neves João  , Neves José
Published: February 2025

As a matter of fact, an Intensive Care Unit (ICU) stands for a hospital facility where patients require close observation and monitoring. Indeed, predicting Length-of-Stay (LoS) at ICUs is essential not only to provide them with improved Quality-of-Care, but also to help the hospital management to cope with hospital resources. Therefore, in this work one‘s aim is to present an Artificial Intelligence based Decision Support System to assist on the prediction of LoS at ICUs, which will be centered on a formal framework based on a Logic Programming acquaintance for knowledge representation and reasoning, complemented with a Case Based approach to computing, and able to handle unknown, incomplete, or even contradictory data, information or knowledge.
P300 Spelling and EEG Compressed Sensed Based on a Universal Megadictionary
Authors: Fira Monica  , Maiorescu Victor-Andrei
Published: February 2025

In this work, we propose, discuss, analyze and compare the results for a new EEG signal compressed sensed method. The EEG signal is recorded during a P300 based spelling experiment. The decompressed EEG signal is analyzed both in terms of decompression errors and spelling accuracy. The proposed method is based on building a universal mega-dictionary. Patient specific and inter-patient dependencies are analyzed and it is noticed that the results are not dependent on the patient.
Towards a Software Product Industry Classification
Authors: Werder Karl  , Wang Hua-Ying
Published: February 2025

The demand for and the number of software product offers are evolving rapidly. Despite the importance of the software industry, its representation within current classification systems is often obstructed. Therefore, researchers often neglect the variety of software products within their work. Using a systematic approach, we develop an industry classification for the software product industry. The empirical-to-conceptual approach allows us to establish concepts that are dominant in practice. As a result, we derive three major sectors, i.e. business function software, industrial software and consumer software. Experts from the software industry are interviewed to evaluate the results. The suggested industry classification helps practitioners to communicate clearly, identify competitors and possible innovations within the software product industry. The classification helps scholars to improve theory building.
Detecting Similarities in Mobility Patterns
Authors: Cottone Pietro  , Ortolani Marco  , Pergola Gabriele
Published: February 2025

The wide spread of low-cost personal devices equipped with GPS sensors has paved the way towards the creation of customized services based on user mobility habits and able to track and assist users in everyday activities, according to their current location.
The Impact of Coherence Analysis and Subsequences Aggregation on Representation Learning for Human Activity Recognition
Authors: Saleh Adel  , Abdel-Nasser Mohamed  , Garcia Miguel Angel  , Puig Domenec
Published: February 2025

Human activity recognition methods are used in several applications such as human-computer interaction, robot learning, and analyzing video surveillance. Although several methods have been proposed for activity recognition, most of them ignore the relation between adjacent video frames and thus they fail to recognize some actions. In this study we propose an unsupervised algorithm to segment the input video into subsequences. Each subsequence contains a part of the main action happening in the video. This algorithm analyzes the temporal coherence of the adjacent frames using several similarity measures. We show preliminary results using two state-of-the-art action recognition datasets, namely HMDM51 and Hollywood2.
Hybrid Approach for Automatic Identification of Multi-Word Expressions in Lithuanian
Authors: Mandravickaitė Justina  , Rimkutė Erika  , Krilavičius Tomas
Published: February 2025

Identification of MultiWord Expressions (MWE) is one of the most challenging problems in Computer Linguistic and Natural Language Processing. A number of techniques are used to solve this problem in different language, mostly English. However not all techniques and approaches can be directly transferred to Lithuanian. Hence, in this paper we experiment with automatic identification of bi-gram MWEs for Lithuanian, which is considered to be under-resourced in terms of lexical resources and availability or accuracy of special lexical tools (e.g., POS-taggers, parsers). We use a raw corpus and combination of lexical association measures and supervised machine learning, which was shown to perform well for English and some other languages. Using this approach we have reached 70.4% precision for identification of typical MWEs, 77.1% precision for non-typical MWEs as well as 60.0% and 81.6% precision for typical adjective + noun and noun + noun MWEs respectively.
Bidirectional LSTM for Automatic Punctuation Restoration
Authors: Salimbajevs Askars
Published: February 2025

The output of generic automatic speech recognition systems consists of raw word sequences without any punctuation symbols. When sequences are longer, it is difficult for humans to read and understand them. Also, many natural language understanding and processing tools expect that input will contain punctuation. We present a bidirectional recurrent neural network for punctuation restoration in speech utterances. The proposed model showed promising results, F1-scores of 0.732 for commas and 0.708 for periods on raw output from a speech recognizer.
Whether to English More Alike Reordered Suits Estonian Input for Statistical Machine Translation Better?
Authors: Aedmaa Eleri  , Fishel Mark
Published: February 2025

We describe experiments with syntax-based pre-reordering of Estonian for statistical translation into English. The reordering rules are designed manually to address the most prominent differences in typical constituent order between Estonian and English and are based on syntactic information obtained via parsing Estonian input sentences. In the experiments we obtained mixed results and present a discussion of the possible reasons.
The Choice of Optimal Algorithm for Frequent Itemset Mining
Authors: Busarov Vyacheslav  , Grafeeva Natalia  , Mikhailova Elena
Published: February 2025

The algorithms for mining of frequent itemsets appeared in the early 1990s. This problem has an important practical application, so there have appeared a lot of new methods of finding frequent itemsets. The number of existing algorithms complicates choosing the optimal algorithm for a certain task and dataset. Twelve most widely used algorithms for mining of frequent itemsets are analyzed and compared in this article. The authors discuss the capabilities of each algorithm and the features of classes of algorithms. The results of empirical research demonstrate different behavior of classes of algorithms according to certain characteristics of datasets.
Interactive Multi-System Machine Translation with Neural Language Models
Authors: Rikters Matīss
Published: February 2025

The tool described in this article has been designed to help machine translation (MT) researchers to combine and evaluate various MT engine outputs through a web-based graphical user interface using syntactic analysis and language modelling. The tool supports user provided translations as well as translations from popular online MT system application program interfaces (APIs). The selection of the best translation hypothesis is done by calculating the perplexity for each hypothesis. The evaluation panel provides sentence tree graphs and chunk statistics. The result is a syntax-based multi-system translation tool that shows an improvement of BLEU scores compared to the best individual baseline MT. We also present a demo server with data for combining English – Latvian translations.
Disorders of Amino Acid Metabolism
Authors: Ficicioglu Can
Published: February 2025

This chapter discusses disorders of 8 different amino acids (phenylalanine, tetrahydrobiopterin, tyrosine, methionine, glycine, tryptophan, lysine, and serine). Creatine deficiency syndromes, Sulfite oxidase and Molybdenum cofactor deficiency, defects of GABA metabolism and γ-Glutamyl Cycle have been included in this chapter as well. Although it is not an amino acid disorder, oculocerebrorenal syndrome (OCRL) (Lowe syndrome) remains in this chapter because it was included here in the first edition of the book. I summarize the biochemical abnormalities and pathology for each condition, and then discuss the clinical findings, diagnosis and treatment. For some of these disorders detected through newborn screening, early the morbidity and mortality in patients. For others, there are not yet any effective treatment options.
Peroxisome Biogenesis Disorders
Authors: Argyriou Catherine  , D'Agostino Maria Daniela  , Braverman Nancy
Published: February 2025

The peroxisome biogenesis disorders (PBD) are a heterogeneous group of autosomal recessive disorders in which peroxisome assembly is impaired, leading to deficiencies of peroxisomal enzymes, complex developmental sequelae and progressive disabilities. Mammalian peroxisome assembly involves the coordinated action of multiple PEX proteins, or peroxins, encoded by PEX genes. There are two main groups of PBD: Zellweger spectrum disorder, due to defects in any one of 13 PEX genes, and Rhizomelic Chondrodysplasia Punctata spectrum, mainly due to defects in PEX7. For most patients, there is a correlation between clinical severity and effect of the mutation on PEX protein function. Diagnosis relies on biochemical measurements of peroxisome metabolites and enzymatic functions, PEX gene sequencing and, in some cases, analysis of peroxisome morphology and more detailed studies of peroxisome biology. Recent advancements in diagnosis have expanded the phenotypes observed, indicating that the full spectrum of these disorders remains to be identified. Although there are no targeted therapies, improved knowledge of peroxin functions, continued characterization of disease models, and systematic clinical studies are expected to impact treatment in the near future.
Hesitant Bipolar Fuzzy Set and Its Application in Decision Making
Authors: Han Ying  , Luo Qi  , Chen Sheng
Published: February 2025

In this paper, by combining hesitant fuzzy set with bipolar-valued fuzzy set, the concept of hesitant bipolar value fuzzy set is introduced, and the hesitant bipolar fuzzy group decision making method based on TOPSIS is proposed. Our study firstly integrates fuzziness, hesitation and incompatible bipolarity in multiple criteria decision making method. An illustrative case of chemical project evaluation also demonstrates the feasibility, validity, and necessity of our proposed method.
Interval-Valued Hesitant Fuzzy Geometric Bonferroni Mean Aggregation Operator
Authors: He Xiao-Rong  , Wu Ying-Yu  , Yu De-Jian  , Zhou Wei  , Meng Sun
Published: February 2025

Hesitant fuzzy set (HFS) is one of the most common used techniques for expressing the decision maker's subjective evaluation information. Interval-valued hesitant fuzzy set (IVHFS) is the extension of HFS and can reflect our intuition more objectively. In this paper we focus on the IVHF information aggregation methods based on Bonferroni mean (BM). We proposed the IVHF geometric BM operator (IVHFGBM) and weighted IVHFGBM operators. Some numerical examples for the operators are designed for showing their effectiveness. The desirable properties of weighted IVHFGBM operator are also discussed in detail. These operators can be applied in many areas especially in decision making problems.
Quality Prediction in Manufacturing Process Using a PCA-BPNN Model
Authors: Zhou Hong  , Yu Kun-Ming
Published: February 2025

A PCA-BPNN model is proposed and simulated which aims to resolve the difficulties faced by the product quality prediction in the modern industry which is brought by the high dimension of production parameters generated in the complex and nonlinear production process. The PCA algorithm is introduced into the BPNN model to realize the dimension reduction without vital information loss which can simplify the architecture of the neural network. The PCA-BPNN model is illustrated and simulated. Experimental results show that this model is superior over the BPNN which achieves a stable prediction performance with a rapid convergence and can overcome the problem, oscillation of MSE, occurred in the BPNN.
ARII-eL: An Adaptive, Informal and Interactive eLearning Ontology Network
Authors: Burgos Daniel
Published: February 2025

User models – the representation of user properties, preferences, goals, etc. – are crucial in modern educational applications. Several related processes, such as recommendations and user adaptations, depend on a user model. The increasing demand for new and more complex system features has resulted in an increasing complexity of user models. Ontologies are a tool to deal with this growing complexity. Well-defined and flexible ontologies can help developers cope with the inherent difficulties. In this paper, the authors present ARII-eL, an Ontology Network for Adaptive and Informal eLearning. ARII-eL defines a user model which is flexible, adaptable and scalable. ARII-eL has been developed for use in a range of educational settings including informal and interactive learning, for which there are not so many user models available for. In addition, we show the ARII-eL ontology network and user model and one application, practical case, and the user results. The massive use of social networks, wikis, collective repositories, instant messaging services, and other social applications is moving the learning process out of classrooms into informal environments, at least as a complement to the regular lessons; and learning applications should take advantage of this tendency. This work is a step forward in that direction.
Explaining Bayesian Belief Revision for Legal Applications
Authors: Keppens Jeroen
Published: February 2025

Bayesian models are a useful tool to propagate the rational implications of human beliefs expressed as probabilities. They can yield surprising, counterintuitive and, if based on valid models, useful results. However, human users can be reluctant to accept their results if they are unable to find explanations providing clear reasons for how and why they were arrived at, which existing explanation methods struggle with. This is particularly important in the legal domain where explanatory justifications are as important as the result and where the use of Bayesian models is controversial. This paper presents a novel approach to explain how the outcome of a query of Bayesian network was arrived at. In the process, it augments the recently developed support graph methodology and shows how support graphs can be integrated with qualitative probabilistic reasoning approaches. The usefulness of the approach is illustrated by means of a small case study, demonstrating how a seemingly counterintuitive Bayesian query result can be explained with qualitative arguments.
Open Science and Accelerating Discovery in Rare and Neglected Diseases
Authors: Harding Rachel J.
Published: February 2025

New medicines for many diseases, in particular neurodegenerative disorders, are not forthcoming, despite patient demands and billions of dollars spent on biomedical research globally. Traditional publishing methods in biomedical sciences are generally slow and disseminate manuscripts, sometimes without the inclusion of primary data, to a privileged audience affiliated to institutions which can afford publication subscription costs. To overcome this barrier to progressive scientific endeavors, many researchers are championing the use of preprints, transparent subject-relevant data repositories, open access journals and open lab notebooks in an effort to more effectively and efficiently communicate their research to a wider audience. In this talk I shall discuss these options and the decisions I have made as an early career researcher, to share my research output on Huntington's disease in real-time through an open lab notebook. Included will be a discussion of the motivations, methods and assessment of open online publishing, including an evaluation of my own open notebook endeavors.This paper is the text of the keynote delivered at the ELPUB2017 conference.
Measurement of Open Access as an Infrastructural Challenge: The Case of Finland
Authors: Olsbo Pekka
Published: February 2025

Finland has set numeric goals for the development of open access. However, at the moment, no system is available by which this development could be monitored. Poor quality in the metadata records in universities' research information databases prevents metadata-based analysis of open access publishing progress. This paper shows how the quality problems of Finnish publication data can be resolved through centralizing the services and processes of metadata creation and by improving the interoperability of systems involved in the processes. As a result, this study describes an environment where reliable measurement of open access is possible and presents suggested actions for improving the Finnish publication data collection.
The University Cheikh Anta Diop of Dakar (UCAD) Science Shop “Xam-Xamu Niep Ngir Niep” (Knowledge of All for All)
Authors: Diouf Dieyi
Published: February 2025

Traditionally, universities in the North as well as in the Global South concentrated their activities on two main missions: Teaching and Research. A “third mission” of universities called “service to the community”, defined as its social responsibility to contribute to development, is now promoted to researchers [1] [2] [3]. Several studies have shown that scientific and local knowledge play an important role in the process of sustainable development by creating an operational interface between researchers, students and non-profit organizations [4] [5] [6]. In order to fully accomplish this mission for the benefit of local communities, researchers are getting involved in Science shops, which were established in the Netherlands in the 1970's. Glen Millot [15] speaks of “third sectors” in reference to the role Science Shop plays. Indeed, Science shops are dynamic mediators of cooperation between communities, NGOs, citizens and researchers. Science Shos teams receive demands from civil society or organizations and helps translate them into research programs or scientific issues that students and researchers treat and make the results available to communities. This presentation will firstly focus on a definition of some useful concepts. Then, the second part will deal with the origin of Science Shops and their evolution before analyzing the process of setting up the UCAD Science Shop “Xam-xamu niep ngir niep” (Knowledge of all for all).
Tracing Cascading Data Corruption in CPS with the Information Flow Monitor
Authors: Gries Stefan  , Hesenius Marc  , Gruhn Volker
Published: February 2025

Cyber-physical systems are context-aware networked systems containing sensors, aggregators and actuators. Raw sensor data and aggregated information are spread among the network and processed in multiple nodes to result in an action, which is possibly executed at a different physical location in the network. Due to the flexible topology and the emergent features of CPS, decisions within the network that trigger actions are not always trivial to understand. These decisions are based on raw sensor data which are not clearly visible at the location of their execution. This can be problematic if decisions have to be justifiable. It becomes even more difficult if decisions has been flawed, and it is not ascertainable why they were made. In order to determine the reasons for incorrect decisions in the network, the dependencies and input values of a decision must be known. Without this information, debugging is quite difficult. In this paper, we present the Information Flow Monitor, which can capture and visualize dependencies between nodes and information in CPS.
Unified User-Interface and Protocol for Managing Heterogeneous Deep Learning Services
Authors: Shu Absalom  , Markov Konstantin  , Vazhenin Alexander  , Cortez Ruth  , Bhalla Subhash  , Shrestha Shashank
Published: February 2025

In the last decade, cheaper and more powerful computations have favored a sufficient surge in research and development of applications in the fields of machine and deep learning. Though often varying in approach, these activities aim mostly at solving similar tasks such as speech synthesis, emotion detection, image recognition, mathematical computations etc. Usually, the typical scenario of using designed algorithms/applications includes inputting data represented in some predefined formats and launching a corresponding program tool that produces expected output data as well as analyzing obtained results. These applications are numerous, and are created by different research teams and laboratories using different techniques and environments including locations, interfaces and procedures to access, query and use them. In a collaborative working environment, developing independent user interfaces for each deep learning application could entail a lot of additional development efforts. In the presented paper, we propose a standardized and flexible interface to reduce design efforts, based on integration of various Deep Learning services (DL services). We also demonstrate a protocol for communication between the user interface and the heterogeneous services. This platform will enable developers of deep learning models to be concerned solely with developing and tuning their models, which can be easily plugged into the central user interface, conveniently exposing their services to users who will have homogeneous central access to a wide-range of DL services, from a unified interface.
Flat Finishing Skill Training Software System Based on Personal Peculiarity Classification
Authors: Teranishi Masaru  , Matsumoto Shinpei  , Takeno Hidetoshi
Published: February 2025

The paper proposes a new education software system for flat finishing skill with an iron file based on classification of personal peculiarities. The software measures leaner's flat finishing motion by using a 3D stylus, and displays classified personal peculiarities effectively to correct learner's finishing motions. A torus type Self-Organizing Maps is used to classify such unknown number of classes of peculiarity patterns.
Robust Technical Trading with Fuzzy Knowledge-Based Systems
Authors: Nakano Masafumi  , Takahashi Akihiko  , Takahashi Soichiro
Published: February 2025

This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e., (i) a module for preparing candidate investment proposals and (ii) a module for their evaluation to construct a well-performed portfolio.
A Transformational Creativity Tool to Support Musical Composition
Authors: Raya Cristóbal  , Ruiz Francisco J.  , Angulo Cecilio  , Samà Albert  , Agell Núria
Published: February 2025

In this paper we use the idea of conceptual space introduced by Boden and redefine some properties such appropriateness and relevance that facilitate the computational implementation of the transformational creativity mechanism. While appropriateness can only be evaluated by an expert, relevance can be objectively measured for any spectator. Computational creativity is based on the relationship between appropriateness and relevance of a concept, and therefore a computational system can be used to support this task. The paper analyses this relationship in the field of music in order to obtain a computer tool to support the musical composition task.
Decision Evolution Set: A New Method of Rough Set in Time Series
Authors: Hu Yuwen  , Xu Jiucheng  , Yi Xiaoyu
Published: February 2025

Rough set theory is conceived as an effective mathematical tool to deal with uncertain information. However, rough set is not suitable to deal with dynamic data, some methods are proposed to solve this question. Evolution model of granular decision is an extension for time series sets based on classical rough set model. Its purpose is to research the rules of decision on time series. Evolution model of granular changes the focus from static decision information system to dynamic time series. It researches the regulations of evolution in decision information system which will be changed in time series. It is a new method to research decision information system. Evolution model of granular decision has the unique advantage in application, but it does not have the clear concept for classification of attributes. This paper gives a concept for decision evolution set based on evolution model of granular decision, and researches its effects in application.
Application of Big Data on Formative Assessment System for Foreign Language Learning
Authors: Huang Wei-bo  , Liu Jiang-hui  , Shen Rong-hong  , Zeng Yi-ting  , Li Xiao-dan
Published: February 2025

This article tries to analyze the value behind the data, students' learning styles, learning behaviors and learning strategies through a systematic study of the technology required in the Big data and formative assessment. Besides, track and record the whole process of the students' learning activities by Cloud Computing and Big data technology. It aims at establishing a new learner-center model of college foreign language learning and provides theoretical support for cultivating college students' ability in autonomous learning and lifelong learning. It is of theoretical significance for the intensive study of autonomous learning. Study results can be applied to the teaching practice of college foreign language and find support for data-driven accurate teaching. Study results can also diversify the foreign language teaching process, improve the college students' comprehensive application abilities and meet the needs of teachers and students to some extent.
An Improved Spinal Neural System Based Method for Mobile Robot Navigation
Authors: Ni Jianjun  , Yang Liu  , Mo Zhengpei  , Fan Xinnan  , Luo Chengming
Published: February 2025

In this paper, a kind of behaviour based navigation is studied, which is inspired from the spinal neural system. To deal with the deficiency of the general spinal neural algorithm, the virtual force field (VFF) algorithm is used. In the proposed method, the local obstacle avoidance is realized by the general spinal neural algorithm and the VFF algorithm is used to achieve global navigation by constructing the force environment of the robot. Finally, the simulation experiments are conducted and the results show that the real-time performance and the path length of the robot can be optimized by the proposed method.
Underwater Image Enhancement Based on Dark Channel Theory
Authors: Fan Xinnan  , Chen Jianyue  , Li Min  , Shi Pengfei  , Zhang Xuewu
Published: February 2025

The inaccurate estimation of underwater background light and distortion of scene color results in the lower quality of recovered underwater images. To address this problem, the paper proposes an enhancement algorithm for underwater images based on dark channel theory, which is expected to improve the clarity, contrast and color fidelity of underwater images. Different from the works of others, the paper uses underwater 3-D transmittance map to achieve the effect of deblurring and contrast enhancement of underwater images. The experimental results show that our algorithm can significantly improve the quality of underwater images.
Harnessing Fuzziness of the Pragmatic Rule-Design Without IF-THEN Rules
Authors: Inamoto Tsutomu  , Higami Yoshinobu
Published: February 2025

In this study, we display preliminary results for harnessing fuzziness of yet-another fuzzy rule-bases. They are based on the pragmatic rule-design (PRD), which has been proposed by the authors. The PRD is novel since a pragmatic rule is not an “IF-THEN” rule nor an artificial neural network, and does not represent a stimulus-response relation. A pragmatic rule is a vector of relative characteristics of effective responses in itself. In the original PRD, the fuzziness in discretizing a system state is too surplus. Restricting such fuzziness may improve the performance of the rule-base, therefore a modification of the original PRD is proposed. Some PRD variants based on that modification are developed and evaluated through their applications to elevator operation problems.
An Investigation on the Effects of Various Flow Parameters on the Underwater Flow Noise
Authors: Bulut Sertac  , Ergin Selma
Published: February 2025

The prediction and reduction of underwater noise is commercially, militarily and ecologically a very critical issue for maritime industry. Machine noise, propeller noise and flow noise are the main components of underwater noise for submerged bodies. Especially at high flow velocities, flow noise becomes dominant source of underwater noise radiated from these bodies. In this paper, the effects of the fluid temperature and salinity of the fluid on the underwater flow noise are investigated, numerically. A circular cylinder is selected for the validation studies of the noise model used in the acoustic analyses. The flow characteristics are obtained by solving governing equations of fluid using Computational Fluid Dynamics (CFD). The turbulence is modelled by using SST k-ω turbulence model. The Ffowcs Williams and Hawkings (FW-H) noise model is applied to predict the sound pressure levels at the receiver points defined various locations, numerically. The monopole, dipole and quadrupole sound sources are taken into consideration for acoustic analyses.
Ship Survivability Study Using High Fidelity CFD
Authors: Caldas Alejandro  , Zegos Constantinos  , Skoupas Sotiris  , Jenkins Jane
Published: February 2025

Safety is of paramount concern to passenger ship owners and integral to their reputation. For this reason, passenger ship owners are aiming for safety standards well beyond the statutory requirements. The current methods for assessing ship survivability following hull breach and subsequent flooding, adopt a simplified application to define a complex issue leading to uncertainty and over-design. This study uses high fidelity deterministic Computational Fluid Dynamics (CFD) analysis in order to explore the shortfalls of the current design guidance such as SOLAS. A number of flooding scenarios are modelled on a cruise ship at full scale for calm and rough seas.
Local Strain Approaches for LCF Life Prediction of Ship Welded Joints
Authors: Corigliano Pasqualino  , Crupi Vincenzo  , Dong Pingsha  , Fricke Wolfgang  , Guglielmino Eugenio
Published: February 2025

The aim of this research activity is to predict the low-cycle fatigue life of welded joints used for ship structures, applying two methods: the effective notch strain approach and the structural strain approach. The first method was applied performing elastic-plastic finite element analyses (FEA), using different cyclic stress-strain curves for base material, heat affected zone and weld metal, while the second method is mesh insensitive for applications in high cycle fatigue regime and has recently been adapted for applications in low cycle fatigue regime. The geometry used for the first method was acquired by means of a 3D scanner. Effective notch strain results and structural strain results were used to predict the low cycle fatigue life.
Buckling Analysis of Explosive Welded Joints for Ship Structures
Authors: Corigliano Pasqualino  , Crupi Vincenzo  , Guglielmino Eugenio
Published: February 2025

Aluminum superstructures and steel hull connections are of fundamental importance in ships. This study regards the buckling analysis of explosion welded joints, made of three layers (ASTM A516 low carbon steel, pure aluminium, A5086 aluminium alloy) and used in ship structures. Experimental tests were carried out on explosion welded specimens. The Digital Image Correlation technique was applied during the tests for the detection of displacement and strain fields. A theoretical analysis, considering the different materials was also performed. Furthermore, a non-linear finite element analysis, considering the different mechanical properties of the explosive welded joint, was performed and was validated by means of the experimental results.
Distributed Energy Resources On-Board Cruise Ships: Integration into the Ship Design Process
Authors: Flore Giordano  , Neroni Diego  , Lamberti Thomas  , Gualeni Paola  , Magistri Loredana  , Silvestro Federico  , Pietra Andrea
Published: February 2025

An intense innovation activity is characterizing the energy system solutions on board ships, especially in the case of large passenger ships due to the significant total amount of installed power and the variegate typology of electrical loads. In the paper the solution of a distributed energy system will be considered for a 140.000+ GT cruise ship, in the perspective of a superior performance in terms of safety and energy efficiency. The target is to overcome the traditional concept of power generation based on large diesel Gensets located in few compartments. The innovative proposal is to integrate it, for the hotel needs, with a greater number of power generation units (but of a smaller size), properly distributed on board. For the application, a suitable reference cruise ship vessel will be considered, already characterized by LNG propulsion solution. Number, typology, size and integration on board of the generation units will be defined in relation with aspects of zonal independence, electrical load, weights, volumes, fuel tanks, supply systems, auxiliaries, with the minimum possible impact on commercially valuable space. In this perspective, fuel cells technology will be particularly taken into account. The critical issues in relation with the present safety rules and the whole ship design process will be addressed as a fundamental aspect.
Effect of Propeller Modelling on Station-Keeping Thruster Allocation Strategy
Authors: Mauro Francesco  , Duranti Eugenia
Published: February 2025

In preliminary Dynamic Positioning calculations, quasi-steady prediction approach is commonly used to figure out the system capability. Thrust allocation solver is usually modelling the thruster devices as pure force generators, considering empirical general formulations to correlate delivered thrust with absorbed power. In such a way,a rough estimate of the power demand during station-keeping operations can be made. Once allocation algorithm is using minimum power demand as objective function, then a more precise modelling of the thrusters can be helpful since preliminary design stages. In the specific, by using an allocation algorithm capable to manage non-linear objective function and constraints, several types of propellers can be modelled, considering differences between tunnel thrusters and azimuthal thrusters, or the differences between fixed pitch and controllable pitch propellers. An example of the effect of propeller modelling is given, considering an Offshore Supply Vessel mounting steerable thrusters equipped with controllable pitch propellers, highlighting the differences with a standard allocation approach.
Experimental and Numerical Prediction of the Hydrodynamic Performances of a 65 ft Planing Hull in Calm Water
Authors: Pigazzini Riccardo  , Puzzer Thomas  , Martini Simone  , Morgut Mitja  , Contento Giorgio  , Gatin Inno  , Vukčević Vuko  , Jasak Hrvoje  , Begovich Ermina  , Caldarella Sebastiano  , De Santis Marco  , Migali Amedeo
Published: February 2025

An extensive campaign of model and full scale experimental tests and numerical computations is being undertaken within the framework of the Project “SOPHYA - Seakeeping Of Planing Hull YAchts”, co-financed by Friuli-Venezia Giulia Region in the field of joint industrial and academic research. The project is aimed at the prediction and optimization of the hydrodynamic performances of a 65 ft motoryacht by MonteCarlo Yachts SpA in calm water and in waves. In this paper, selected results of the numerical computations for the calm water case, conducted jointly by HyMOLab-University of Trieste and by the University of Zagreb, are presented. RANS, Volume-of-Fluid and Level Set simulations are carried out in combination with an automatic mesh-generation tool, developed in this project within the OpenFOAM/foam–extend frameworks. The numerical results are compared with experimental data obtained within the project at the towing tank of the University of Naples.