bluuebunny/crossref_metadata_2025 huggingface dataset:
Cruise Cabin as a Home: Smart Approaches to Improve Cabin Comfort
Authors: Buqi Raol  , Carciotti Sara  , Cipriano Margherita  , Ferrari Paolo  , Nolich Massimiliano  , Rinaldi Alessandro  , Ukovich Walter  , Celotti Daniel  , Guglia Paolo
Published: February 2025

Information and Communications Technology (ICT) has an impact on nearly every aspect of people lives – from working or learning to socializing – and makes people lives easier and more comfortable. In particular, smart home devices can handle environmental characteristics (lighting, temperature, etc.) that can increase people comfort and can also help in reducing costs. In order to increase passenger comfort in the cruise ship cabins, this study proposes the design of an App that provides high quality products and services. Smart Home, Ambient Intelligence and IoT are the starting knowledge to create a smart space without re-designing all the cabin environments. The App brings real added value compared with existing domotic systems, because it does not consider only environmental characteristics but also personal features of the passengers and their activities. The aim of the proposed system is to improve passenger comfort in order to increase perceived quality, acting as a real time Decision Making System. The application receives information from various sensors and actuators and decides how to create a suitable environment and healthy space, according to the passenger preference and activity. The App follows both the necessity of the ship owner to increase passenger satisfaction and to reduce costs.
SRtP 2.0 — The Evolution of the Safe Return to Port Concept
Authors: Cangelosi Dario  , Bonvicini Alessandro  , Nardo Marco  , Mola Andrea  , Marchese Andrea  , Tezzele Marco  , Rozza Gianluigi
Published: February 2025

In 2010 IMO (International Maritime Organisation) introduced new rules in SOLAS with the aim of intrinsically increase the safety of passenger ships. This requirement is achieved by providing safe areas for passengers and essential services for allowing ship to Safely Return to Port (SRtP). The entry into force of these rules has changed the way to design passenger ships. In this respect big effort in the research has been done by industry to address design issues related to the impact on failure analysis of the complex interactions among systems. Today the research activity is working to bring operational matters in the design stage. This change of research focus was necessary because human factor and the way to operate the ship itself after a casualty on board may have a big impact in the design of the ship/systems. Also the management of the passengers after a casualty is becoming a major topic for safety. This paper presents the state of the art of Italian knowledge in the field of system engineering applied to passenger ship address to safety improvement and design reliability. An overview of present tools and methodologies will be offered together with future focuses in the research activity.
The Application of Goal Based Design for Passenger Ship Safety Improvement
Authors: Dreossi Matteo  , Bonvicini Alessandro
Published: February 2025

SOLAS (International Convention for Safety Of Life At Sea) Chapter II-2/Regulation 17 and Chapter III/Regulation 38 allow for the adoption of “Alternative designs and arrangements” that deviate from the ones permitted by prescriptive regulations. The process to be used for the Alternative Design engineering analysis is documented by SOLAS by means of guidelines and requires a holistic and consistent risk assessment to demonstrate that the risk introduced by the novel design is less or at least equal to the one guaranteed by the prescriptive reference design. This activity de facto introduces the Goal-Based Design into the traditional design process by evaluating safety as a main goal in the ship design. This approach is possible thanks to the research activity aimed at improving the application of the simulation tools that are used to quantify the risk level of a specific design solution and its variants by evaluating the human element in the design. In this paper examples of shipbuilding application of goal based design are presented together with an overview of the new frontier in the adoption of these approaches for the improvement of safety and the maximisation of the ship payload.
Analysis of the Influence of Pressure Field on Accuracy for Onboard Stability Codes
Authors: Braidotti Luca  , Trincas Giorgio  , Bucci Vittorio
Published: February 2025

Over last decades, due attention was paid to development of new stability criteria, especially probabilistic rules for damage stability, strongly influenced by the loss of many ro-ro ships in last decades of past century. In the last years, also a revision of intact stability code started and proposals have been implemented introducing stability in waves. These proposals deal with the equilibrium of a ship in regular waves to evaluate initial stability (GM) as well as righting arm curve in waves (GZ). This paper is not intended to critically review the present and proposed initial stability code, but is limited to assess how expected behavior of intact ships in waves is affected by accuracy in computer programs for assessing actual hydrostatic properties. An updated computer code, designed for onboard application and based on 3D pressure integral, has been developed and tested for a ro-ro ship. Then a comparison between hydrostatic and Airy effective waves has been carried out to analyze the relevance of differences between the two correspondent pressure fields affecting the equilibrium position and hydrostatic properties of the ship. It is demonstrated that these differences appear relevant beginning from sea state 4.
Robot Utilization for Nursing Technical Support Emphasizing Nurse–Patient Synchronization
Authors: Tamai Takahito  , Majima Yukie  , Ishigame Atsushi  , Nakayama Masaya
Published: February 2025

Handing down nursing skills of experienced nurses to newcomer nurses is difficult. Kawano's earlier studies have specifically examined brain wave synchronization between a nurse and patient during intravenous injection. Presumably, a good technique can be provided by inducing their synchronization. Preliminarily, we verified the use of a robot to generate mutual synchronization phenomena.
Home Care Admission Information: What Nurses Need and What Nurses Have. A Mixed Methods Study
Authors: Sockolow Paulina  , Wojciechowicz Christine  , Holmberg Annika  , Bass Ellen J.  , Potashnik Sheryl  , Yang Yushi  , Bowles Kathryn H.
Published: February 2025

Effective communication between clinicians across care settings is fundamental for continuity of care and decreased risk of errors. The home care admission often starts without important information needed for formulation of the plan of care. We conducted a mixed methods analysis to investigate home care admission information from two perspectives: qualitative information regarding information nurses reported they needed during an admission, and quantitative information regarding information actually available. We mapped both data sets to an international specification for transitions in care information, the Continuity of Care Document (CCD). The information that homecare nurses said they needed mapped sufficiently (90%) to the CCD. Regarding available information: no observation had all the CCD information present; CCD information was missing in varying amounts across the admission documents. Nurses searching among pages of documentation for information which may not be present is inefficient and introduces patient safety concerns of increased risk for errors.
Predictive Tools in the Care of Blood Donors: Prevention of Vasovagal Syndrome
Authors: Cobo González Diego  , Fernández Batalla Marta  , Gasco González Sara  , Martínez Botija Sergio  , Jiménez Rodríguez Ma Lourdes  , Santamaría García José Ma  , Ramírez Sánchez Sylvia Claudine  , Vialart Vidal Niurka  , Condor Camara Daniel Flavio
Published: February 2025

Prediction in healthcare is essential in order to promote safe and quality care. Taking adequate care of blood donors, who perform an altruistic act towards society, is paramount. Therefore, the use of tools which allow to predict the risk of Vasovagal Syndrome during the act of blood donation is necessary. The objective of this study is to design a predictive engine of an expert system to determine the risk of Vasovagal Syndrome through the use of deductive methodology. Five clusters of predictors of this syndrome were obtained by applying grouping tables of the variables established by logical formulation in such a way that after combinatorial variables, 5 values were obtained for the determination of risk using a Lickert scale. With these results we could design the predictive engine that will allow the development of a computational tool to improve the quality of care of blood donors.
Leveraging Aging in Place Through Sensor-Enhanced In-Home Monitoring
Authors: Wang Ju  , Wang Jing  , Miao Hongyu  , Marschollek Michael  , Wolf Klaus-Hendrik  , Lynch Kerry A.  , Gong Yang
Published: February 2025

Seniors expect to age in place, which means living in their own homes as long as possible with familiar facilities and environments. Due to the capability of continuous and unobtrusive monitoring, the sensor-enhanced in-ho monitoring is regarded as a promising solution to support aging in place. In this paper, by reviewing three influential projects in this field of in-home monitoring for aging in place, we present our opinions and suggestions on the development of informatics-supported aging in place for its practical application in healthcare such as diagnosis and nursing in the era of data science. To promote the practical usage of in-home monitoring in aging, we highlight the gap between demands and available approaches. We conclude that in the next stage we should design demand-oriented system, conduct evidence-based research and accelerate interdisciplinary collaboration.
Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch
Authors: Choi Jeungok  , Choi Jeeyae  , Choi Woo Jung
Published: February 2025

The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subjects and 261 variables) and built the models using a logistic regression with and without L1 regularization. Depression prediction rates ranged 58.33% to 90.48% and 83.33% to 90.44% in the model with and without L1 regularization, respectively. The moderate to high prediction rates imply that the machine learning algorithms built the prediction models successfully.
Virtual Teaching Materials Developed for Elementary School Students to Foster Support Capabilities for Elderly People with Dementia
Authors: Masuda Seiko  , Murashima Kotoka  , Majima Yukie
Published: February 2025

We created teaching materials for elementary school students to foster their ability to understand and support elderly people correctly, including those with dementia, without prejudice. Pediatric awareness education by DVDs can positively capture the elderly with dementia, but it was difficult to learn in practice. There, Slide materials designed to encourage positive understanding of elderly people were used together with a Pepper to increase participants' interest material. Results suggest that simultaneous use of the slide materials and a Pepper maintains participants' interest and promotes understanding of elderly people, including those with dementia.
Classifying Provider-EHR Screen Interactions During ICU Pre-Rounds
Authors: Khairat Saif  , Coleman Cameron  , Rand Victoria  , James Elaine  , Bice Thomas
Published: February 2025

Electronic health records (EHR) usability is paramount for high quality of care delivery, clinician productivity and effectiveness, and patient outcomes. This paper investigates clinicians EHR pathways during pre-rounds by characterizing the top EHR screens, duration per screen, and the path taken to complete a task. Structured observations were conducted of ICU providers interacting with the EHR in a real-time, real-world setting to better characterize the information retrieval process. Based on preliminary results of the observations, key areas of information needs have been identified and a preliminary model of EHR workflow has been established. The study highlights that there is a clear discrepancy in usage in EHR screens among ICU residents suggesting that there is a perceived clinician's pathology to finding patient information.
Improving Clinical Named-Entity Recognition with Transfer Learning
Authors: Zhang Edmond  , Thurier Quentin  , Boyle Luke
Published: February 2025

Transfer learning is a powerful machine learning technique that enables the internalizing and reuse of prior knowledge to new tasks. Transfer learning is currently the starting point for recognition tasks such as computer vision. However, in natural language processing (NLP), the application of this technique is less prevalent. Our research investigates how, through the application of transfer learning, existing knowledge can be used to build more accurate NLP models. We subsequently applied these models to a named-entity recognition (NER) task. Our experimental results show significantly better recognition performance can be obtained through leveraging knowledge from a base model, trained using poorly annotated data.
Enabling Pedigree Visualization and Analysis in tranSMART
Authors: Lodahl Robert  , Bauer Christian R.  , Baum Benjamin  , Bender Theresa  , Parciak Marcel  , Krawczak Michael  , Sax Ulrich
Published: February 2025

This paper examines the relevance of genetic pedigree data in the context of medical research platforms. By surveying currently available tools for visualization and analysis of this data type and by considering possible use cases that could make usage of the combination of singular patient data and pedigree data, the advantages of integrating the data type into a medical research platform were shown. In a practical work step, an integration procedure of pedigree data into tranSMART was created. Furthermore, a tool to analyze and visualize pedigree data in combination with other patient data was implemented into SmartR, a dynamic analysis tool inside of tranSMART. Finally, we address limitations and future development strategies of the tool.
Privacy Preservation and Drone Authentication Using ID-Based Signcryption
Authors: Benzarti Sana  , Triki Bayrem  , Korbaa Ouajdi
Published: February 2025

By the recent years, the use of UAV (Unmanned Aerial Vehicle) also known as drone have witnessed a remarkable increase. Drones are no longer limited for military services as in the past. They are able to carry out civilian missions like survivor search operations after natural disasters, accidents, especially in the most difficult conditions, such as meteorological conditions and inaccessible or dangerous geographical locations. Moreover with the invasion of a new era, which is the IoT (Internet of Things), carrying a big wave of connected objects, we can talk about drone based IoT. The problem is how can we control such a connected object by preserving the privacy and security of users. The principal goal is to build a new secured architecture to provide a high security level. This architecture will control drones and promote them to an upper level connected with the IoT and the big data paradigms. We propose in this paper an architecture that relies on Id-Based Signcryption and RFID tags.
Using Information Extracted from Microblogs in Order to Palliate the Cold Start Problem in Recommender Systems
Authors: Herce-Zelaya Julio  , Porcel Carlos  , Tejeda-Lorente Álvaro  , Bernabé-Moreno Juan  , Herrera-Viedma Enrique
Published: February 2025

Recommender systems predict and recommend items that could be from the user's interest. One of the main problems from recommender systems is the cold start problem, which happens when new users or items are registered into the system. In order to deal with this problem in the literature we can find many proposal. In many cases the solutions require users to provide explicit information which requires effort on their part. For that reason and given the great boom of social networks, we focus on systems that make use of external data sources. Then, in this paper will be presented an approach in which using social media data will classify the users and create some predictions by using classification trees. Therefore the users will not need to explicitly provide any data other than the source of their social media, helping in this way to alleviate the cold start problem, because the system always has information available to establish user profiles.
60 Years of Business Intelligence: A Bibliometric Review from 1958 to 2017
Authors: López-Robles J.R.  , Otegi-Olaso J.R.  , Gamboa-Rosales N.K.  , Gamboa-Rosales H.  , Cobo M.J.
Published: February 2025

Business Intelligence (BI) is being seen as a core activity in business, science, education or any field in which the use of intelligent software are vital to achieve their goals. The BI concept is also multidimensional, and it can be defined as a technology-driven process for analyzing data and presenting actionable information to support the organizational decisions. In this respect, the professionals involved in this area of knowledge are seeking to develop a culture of result orientation, focused on an effective decision-making and collaboration. Thus, the main aim of this contribution is to develop a bibliometric analysis to evaluate the performance and conceptual evolution of the authors and publications that are directly related to Business Intelligence.
Feature Vector-Based Artificial Neural Network Classification Model for Handwritten Character Recognition
Authors: Mohamad Muhammad Arif  , Haron Habibollah  , Hasan Haswadi
Published: February 2025

This paper presents a recognition system for handwritten character that utilized the chain code and the image properties features as image representation. Thinning algorithm was applied first to the original image to produced thinned binary image before extracting the chain code. Then, chain code feature was extracted by metaheuristic feature extraction algorithm. Once the chain code has been extracted, a feature vector was derived based on the chain code and the image properties. The derivation of feature vector is based on the formation rule in terms of Local Value Formation Rule (LVFR) and Global Value Formation Rule (GVFR). After that, a back propagation neural network was used as a classification model to classify the image character based on the generated feature vectors. National Institute of Standards and Technology (NIST) handwritten character database were applied in the experiment. Finally, the performance of the classification models was measured in terms of precision, sensitivity, specificity, F-score and accuracy.
Four-Scene Comic Story Dataset for Softwares on Creative Process
Authors: Ueno Miki
Published: February 2025

Computational methods to analyze and build creative contents are important issues to enable collaboration between creators and softwares with artificial intelligence. Despite current advances of machine learning, there is a few data of common creative contents with the process of creating stories. In order to solve the problem,
Managing Multi-Criteria Group Decision Making Environments with High Number of Alternatives Using Fuzzy Ontologies
Authors: Morente-Molinera Juan Antonio  , Kou Gang  , González-Crespo Rubén  , Corchado Juan Manuel  , Herrera-Viedma Enrique
Published: February 2025

The high amount of information that modern multi-criteria group decision making environments must handle requires the development of novel methods. These methods should be able to work with high amounts of alternatives while providing the experts a comfortable framework that they can use to carry out this complex decisions. In this paper, a novel method that tries to solve this issue is presented. Our method makes use of fuzzy ontologies in order to allow the experts to focus on what is more important: the weight that should be given to each criteria. This way, they do deal directly with the high amount of alternatives. Experts decide the importance of each criteria and the alternatives ranking is calculated automatically using the fuzzy ontology.
An Ontology of Specification Patterns for Verification of Concurrent Systems
Authors: Garanina Natalia  , Zubin Vladimir  , Lyakh Tatiana  , Gorlatch Sergei
Published: February 2025

Verifying the reliability of software systems formally is difficult due to the complexity of system correctness requirements. Specification patterns allow us to describe typical requirements in a natural language, while their formal semantics enable expressing these requirements in the input language of some verification tool. In this paper, we propose an ontology of specification patterns that combines patterns from existing requirement classifications with new patterns. Our ontology can be used to express combinations of requirements of the following types: qualitative, real and branching time, with combined events, quantitative characteristics of events, and simple statements about data. The advantage of our approach is the ability to formally verify the compatibility of multiple requirements. As an illustrating use case, we describe the requirements for the real-world vacuum control system of the Large Solar Vacuum Telescope.
Experiment and Comparision on Classification of Chinese Car Reviews
Authors: Liu Xuedan  , Wang Yinglin
Published: February 2025

With the rapid development of e-commerce and online review platforms, the number of reviews of product has been multiplied, which makes it significant to mine valuable information from them for both businesses and consumers. Usually text classification methods are the main approaches to deal with this kind of problems. There are several steps in the process of text classification, and many different choices of methods or components can be selected in each step, so there are many possible combinations of schemas. However, there was lack of comparison of those different combinations in the past. In this paper, different combinations of components of text classification are constructed and evaluated. In the feature selection and weighting step, mutual information, information gain, chi-square test and TF-IDF methods are used as the alternatives. In the text classification step, four frequently used machine learning methods are selected as the components. The experiments are conducted on an annotated Chinese car reviews corpus. Results show that the combination of using chi-square test and Support Vector Machine algorithm obtain the best performance. The relationship between the performance and the number of the features is also studied, and empirical size of the corpus in this kind of task is given.
Rela-Ops Model: A Method for Knowledge Representation and Application
Authors: Nguyen Hien D.  , Do Nhon V.  , Pham Vuong T.
Published: February 2025

Knowledge base plays an important role in intelligent systems. In this paper, we propose a knowledge model of relations and operations, called Rela-Ops model. This model is built based on object-oriented approach, and it has the foundation which are concepts, relations, operators and inference rules. Each concept is a class of objects. It has the structure and behavior to solve problems on its own. Besides that, we also propose the model of problems on Rela-Ops model. Algorithms for solving them are also designed. These algorithms use the combining knowledge of relations and operators in their reasoning processing. Rela-Ops model is applied to build the knowledge base of the intelligent problems solving system in linear algebra. This system satisfies requirements of an intelligent problem solver in STEM education. It can solve exercises about matrices, linear equations systems and vector spaces automatically. Its solutions are step-by-step and human-alike solving methods.
Knowledge of Language and Knowledge Science
Authors: Di Sciullo Anna Maria
Published: February 2025

Knowledge of language and principles of naturalistic learning are rarely used in information extraction systems. The main challenge for these systems is to provide conceptually relevant information while minimizing computational costs. This paper argues that knowledge of language and principles of naturalistic learning may enhance the capacity of intelligent systems to process linguistic expressions and simulate humans' capacity for language. We contrast humans' genetic endowment to genetic programming, as well as naturalistic learning to Reinforcement Learning used to extract information form texts. We point to limits of machine learning methodology and we draw consequences for knowledge science.
Modelling Negation of the Afrikaans Declarative Sentence in GF
Authors: Pretorius Laurette  , Marais Laurette
Published: February 2025

The correct modelling of negation in a computational grammar is essential in order for the grammar to be useful in natural language processing, and controlled language development and applications. An important and unique feature of Afrikaans is how it deals with negation. We present an exposition of a substantial fragment of negation in Afrikaans and discuss our implementation thereof in GF. Examples are given as illustration of the relevant issues. The paper is concluded with a discussion of results and plans for future work.
Biparty Decision Theory for Dialogical Argumentation
Authors: Hadoux Emmanuel  , Hunter Anthony  , Polberg Sylwia
Published: February 2025

Proposals for strategies for dialogical argumentation often focus on situations where one of the agents wins the dialogue and the other agent loses. Yet in real-world argumentation, it is common for agents to not involve such zero-sum situations. Rather, the agents may enter into a dialogue with divergent but not necessarily opposing views on what is important in the outcomes from the argumentation. In order to model this kind of situation, we investigate a decision-theoretic approach that allows different participants to have different utility evaluations of a dialogue, and for the proponent to model the opponent's utility evaluation in order to optimize the choice of move in the dialogue.
Mediation of Debates with Dynamic Argumentative Behaviors
Authors: Hadoux Emmanuel  , Beynier Aurélie  , Maudet Nicolas  , Weng Paul
Published: February 2025

Mediation is a process for resolving conflicts among several entities. In argumentation debates, conflicting agents that may be organized as teams exchange arguments to persuade each other. In this paper, we consider an automated mediator, which assigns the speaking slots to agents so as to optimize some objectives and ensure the fairness of the debate. We propose a general setting where the argumentation strategies of the agents are probabilistically known and may evolve over time. We show that the problem can be solved as a semi-Markov decision problem with hidden modes.
Machine Arguing: From Data and Rules to Argumentation Frameworks
Authors: Toni Francesca
Published: February 2025

Argumentation frameworks have been widely studied both in terms of formal properties they exhibit under different semantics and in terms of applications they can support. But where are argumentation frameworks coming from, and how can argumentation, a model-based approach to AI, beneficially integrate with the nowadays-much-widespread data-centric AI perspective? In this talk I will overview applications empowered by a variety of (extension-based and gradual) semantics for abstract and bipolar argumentation frameworks automatically obtained from data (including but not limited to text) and from logical rules. Some of these applications require the integration of argumentation and machine learning, and result in a mixed model-based and data-centric pipeline. For some applications, the semantics informs the definition of the frameworks rather than, as is conventionally the case, being enforced on frameworks a posteriori.
Choosing Appropriate Arguments from Trustworthy Sources
Authors: Panisson Alison R.  , Parsons Simon  , McBurney Peter  , Bordini Rafael H.
Published: February 2025

Recently, argumentation frameworks have been extended in order to consider trust when defining preferences between arguments, given that arguments (or information that supports the arguments) from more trustworthy sources may be preferred to arguments from less trustworthy sources. Although such literature presents interesting results on argumentation-based reasoning and how agents define preferences between arguments, there is little work taking into account agent strategies for argumentation-based dialogues using such information. In this work, we propose an argumentation framework in which agents consider how much the recipient of an argument trusts others in order to choose the most suitable argument for that particular recipient, i.e., arguments constructed using information from those sources that the recipient trusts. Our approach aims to allow agents to construct more effective arguments, depending on the recipients and on their views on the trustworthiness of potential sources.
Epistemic Attack Semantics
Authors: Thimm Matthias  , Polberg Sylwia  , Hunter Anthony
Published: February 2025

We present a probabilistic interpretation of the plausibility of attacks in abstract argumentation frameworks by extending the epistemic approach to probabilistic argumentation with probabilities on attacks. By doing so we also generalise the previously proposed attack semantics by Villata et al. to the probabilistic setting and provide a fine-grained assessment of the plausibility of attacks. We also consider the setting where partial probabilistic information on arguments and/or attacks is given and missing probabilities have to be derived.
Amnestic Forgery: An Ontology of Conceptual Metaphors
Authors: Gangemi Aldo  , Alam Mehwish  , Presutti Valentina
Published: February 2025

This paper presents Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet, which is inspired by the theory of Conceptual Metaphor. Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology design framework to deal with both semiotic and referential aspects of frame and role mappings. The description of the resource is supplied by a discussion of its applications, with examples taken from metaphor generation, and the referential problems of metaphoric mappings. Both schema and data are available from the Framester SPARQL endpoint.
SAREF4health: IoT Standard-Based Ontology-Driven Healthcare Systems
Authors: Moreira João  , Ferreira Pires Luís  , van Sinderen Marten  , Daniele Laura
Published: February 2025

Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies, which offer opportunities to improve patient monitoring and abnormal situation detection with support of medical wearables and cloud infrastructure. Usually, these systems rely on IoT ontologies to represent sensor data observations. The ETSI Smart Appliances REFerence (SAREF) IoT ontology is an extensible industry-oriented standard. In this paper, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context according to quality, message size (payload), IoT-orientation and standardization. We also introduce a SAREF4health extension to tackle the verbosity problem. In the SAREF4health development we followed ontology-driven conceptual modelling, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO) plays the role of a reference model. The methodology was enhanced by a standardization procedure and considers the RDF serialization of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the use cases of an early warning system that uses ECG data to detect accidents with truck drivers in a port area. A prototype that integrates an existing ECG wearable with cloud infrastructure demonstrates the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health is adequate to enable semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed.
Exploring Key Elements for Successful Telehealth Implementation Within Severely Under-Resourced Communities: A Case Study in the Pacific Islands
Authors: Higa Christina
Published: February 2025

The intent of this paper is to identify and provide insight on key enablers for successful implementation of telehealth services in severely under-resourced and rural populations. The case study presented is in the context of the U.S. Affiliated Pacific Islands that represent resilient and innovative communities who face many challenges of isolation, tiny populations, and developing economies. Long-standing telecommunication hindrances and lack of supporting resources are fundamental barriers to telehealth advancement. New undersea fiber optic developments present opportunities for reliable connectivity needed for telehealth applications. This paper reviews the emergence of telehealth champions in the region and reviews key elements that contribute to rapid and successful implementation of telehealth applications and services.
Unsupervised Systems-Level Learning Mechanism Based on Bayesian-Inference
Authors: Puigbò Jordi-Ysard  , Arsiwalla Xerxes D.  , Verschure Paul
Published: February 2025

Is the brain a predictive machine? Is prediction its only function or one among many? Predictive coding theories argue that all the brain is doing is inference, and the way it is doing it is through Bayesian inference (i.e. predicting what will be the input to the brain in a Bayes-optimal way) and active inference (i.e. the brain acts through a body with the intention of ensuring the predictability of the environment or the same, reduce the free energy [2]). Nonetheless, Bayesian inference is a computationally expensive process for which there's no explanation on how a brain built with neurons and neuromodulators could perform such process. Previous attempts at defining a neuronal mechanism for performing Bayesian inference failed at defining how a model of the world was originally formed [1]. We reformulate the previous attempts to provide a neuronal model that performs Bayesian inference without previous assumptions about the world except for normality. This model becomes a biologically plausible learning mechanism that uses unsupervised local learning rules to learn the statistics of an input and stop learning when the prediction error can't be further reduced.
Cognitive Architecture: The Key to AI-Based Digital Transformation in Organizations
Authors: Olmos-Sánchez Karla  , Rodas-Osollo Jorge
Published: February 2025

The objetive of this paper is to present a framework of work for organizations to became cognitive systems through the KMoS-RE systematic knowledge management process. A summary of the application of the framework in a freight company is presented. Promising results provide evidence of the usefulness of the framework in the era of AI-based digital transformation.
Towards Detecting Structures in Computational Astrophysics Plasma Simulations: Using Machine Learning for Shock Front Classification
Authors: Aloy Torás Carmen  , Mimica Petar  , Martinez Sober Marcelino
Published: February 2025

In this paper, we assess the suitability of a number of different machine learning (ML) methods for detecting shock fronts in Computational Fluid Mechanics (CFD) simulations. Detection and handling of shock fronts is important in a wide variety of fluid mechanics problems. We focus on computational astrophysics, where a successful algorithm must be able to classify the simulated fluid elements as belonging to a no-shock, ahead-of-the-shock-front and behind-the-shock-front class. We implement and test several supervised multi-class classification ML methods for highly imbalanced classes. The training data set is generated by an exact solver for a Riemann Problem [1] (one of the most straightforward non-trivial CFD tests). The most suited algorithm(s) are chosen according to their accuracy, speed, and ease of training. Our preliminary results show that the random forest algorithm (with class balancing) is the best method for classification.
Physical and Sedentary Activity Among Facebook Users in Croatia
Authors: Crnković Ivana  , Babić Janko  , Racz Aleksandar
Published: February 2025

The aim of the research was to examine the level of physical activity of users of the social network in Croatia, the relation of the sociodemographic characteristics and the level of physical activity compared to the time spent on the following activity. The study involved 333 respondents who are active users of Facebook (FB). The short version of the IPAQ questionnaire and BMI Calculator were used to assess the physical activity of respondents. This research has determined that the BMI index (BMI) ranges from 16.04 to 42.25, average 24.00. The total level of physical activity of the participants, expressed through the Metabolic equivalent task (MET) is high, and according to the MET category the participants are in the high level of involvement category or HEPA active. There is a statistically significant correlation between the overall results obtained with the IPAQ compared to the MET category, BMI, quality of life and self-assessment of health in FB users. Participants spend on average 5 hours per day sitting during a week, and there is statistically significant correlation between the overall results of the IPAQ compared to the MET category and the time spent sitting. In conclusion: that involvement in social networks for participants in this study does not disturb the level of physical activity.
Research-Based Educational Support of Undergraduate Students with Autism Spectrum Disorders
Authors: Ribu Kirsten
Published: February 2025

The numbers of college and university students with autism spectrum disorders (ASD) are steadily rising, but research on their academic performance reports mixed results. The diversity of the population makes it necessary to target each student individually. This paper describes an ongoing study and experience with pedagogical intervention for ten undergraduate IT students at Oslo Metropolitan University (OsloMet) over several years. The intervention design is based on knowledge of research in the field and evidence-based practices, as well as professional skills. Data about student challenges and needs are collected from informal, open-ended interviews with students, in addition to conversations and observation. The goal is to ensure that the students achieve academical success. Plans are currently being made to develop a formal program that will target all students with autism spectrum disorders at OsloMet.
Simulating Disability in Universal Design Teaching: A Critique
Authors: Ryhl Camilla
Published: February 2025

Blindfolding people and providing them with white cane or placing them in a manual wheelchair for 20–60 minutes is a much used and classic exercise when teaching architects and other professionals the basics of user needs related to disability. This method, called try-it-yourself, is the most prevalent method where universal design in taught in the Nordic region.
Cultural Artefacts with Virtual Capabilities Enhance Self-Expression Possibilities for Children with Special Needs
Authors: Cappelen Birgitta  , Andersson Anders-Petter
Published: February 2025

In this paper we discuss how new combinations of technology, art and culture enable children with special needs new ways to express themselves. UN declaration (UDHR) states “All human beings have the right to participate in cultural life, enjoy art…”, later including children (CRC) and persons with disabilities (CRPD). To meet these UN demands, several countries have created cultural programs to offer children art and culture activities in school. In the Norwegian Cultural Schoolbag-program they emphasize that art and culture can provide experiences that may be decisive in order to develop the individual's personal identity, life quality and alternative worldviews. Since 2015 one county offers a unique “Accessible-Program” for children with special needs. From many years of research, we know the health-promoting value of artistic and musical activities. New technology opens up completely new ways to make art and culture accessible for all. In this paper, we show how we used music and cultural artefacts with musical and visual capabilities (RFID, AI), giving diverse users new musical, artistic, sensorial and creative experiences. We do this in relation to Ruud's concepts of Health Affordance and Cultural Immunogen. As a travelling interactive art installation, being part of the Norwegian “Accessible-Program”, we made participatory observations in six schools. We experienced how the children expressed themselves, co-created, showed self-efficacy and made different cultural experiences, depending on disability and relations. With this paper, we emphasize that art and culture, in combination with technology, offers valuable new health-promoting potentialities, important for the UD community.
Applying a Universal Design Approach to Empower Children with Multiple Impairments in Assistive Technology Assessment
Authors: MacKeogh Trish  , Dillenburg Karola  , Donovan John
Published: February 2025

Rapid developments in technology, coinciding with the shift in educational ideology towards the inclusion of children with multiple disabilities, has led to increased calls for a Universal Design (UD) approach to promoting, user-friendly, proactive, transparent, and accessible environments and to the importance of Assistive Technology (AT) applications. For children with disabilities, technologies provide opportunities for greater and more flexible access to activities then traditional interfaces if applied and matched successfully. Relatively high abandonment rates of assistive technology have been documented, often due to a lack of user involvement in the assessment process. The field of Universal Design is showing that collaborative approaches enable users to be active participants and enhance their engagement in activities and social inclusion. This paper provides a background to AT service provision in Ireland and outlines the adaptation of the Irish Matching Person with Technology (IMPT) tool from text based to a multimodal digital format for children with disabilities through a universal design approach.
Improved Wolf Pack Algorithm for Solving Traveling Salesman Problem
Authors: Zhang Lanyong  , Chen Huihuang  , Wang Wugui  , Liu Sheng
Published: February 2025

To achieve an optimal solution to the traveling sales problem, School of Automation (TSP), an improved wolf pack algorithm (IWPA) that addresses this problem through migration, summoning, and besiegement behavior is proposed. This proposal is based on a basic WPA concept. In this algorithm, it retains the division-work cooperative search characteristics of WPA, adds negative feedback to the algorithm, and sets adaptive parameters, which solves the disadvantages of slow convergence speed and low-dimensional search efficiency of WPA. Finally, the path planning simulation of the IWPA was performed and the proposed approach was compared with ant colony optimization and particle swarm optimization. The simulation results indicate that IWPA has specific advantages over similar algorithms regarding feasibility, convergence, and stability.
Elucidation of the Backbone Structure of Cross-Market Dependency Network of World Market Indices: A Global Threshold Filtering Approach
Authors: Bhattacharjee Biplab  , Ramanan T. Radha  , Shafi Muhammad  , Acharjee Animesh
Published: February 2025

The multi-tiered cross-market dependency structures among capital market worldwide have a highly complex architecture which makes international portfolio management a challenging exercise. The challenge in recent times has been magnified given that the strengths of linkage structures have increased over the past decades. Understanding the complex interdependencies in a temporal scale and identifying the backbone structure in this interdependent system will aid in unearthing the avenues from where diversification benefits may possibly arise. With this objective in mind, the current study attempts to mathematically formulate the cross-market connectivity into weighted network models and elucidate the backbone structures by deploying a global threshold filtering approach. The present work investigates the dependency structure based on weekly-data series belonging to forty-three global markets. The weighted networks depicting cross-market relationships are filtered and visually inspected to decipher the significant connectivity structures. The study identifies that average cross-market linkage strengths increased during market stress conditions. The study also identified a disjointed set of markets wherein one can direct the investments to cushion oneself from systemic risk impacts.
An Uncertain Mean-Chance Model for International Portfolio Selection
Authors: Huang Xiaoxia  , Wang Xuting  , Zhou Xiaoguang
Published: February 2025

In real international financial markets, the past data can't always effectively reflect the future and people must estimate the future values. In this paper the future security prices and foreign exchange rates are given by experts' evaluations rather than historical data. Regarding the future security prices and foreign exchange rates as uncertain variables we discusses an international portfolio selection. In the paper, an uncertain mean-chance model for international portfolio selection is proposed. The equivalent model is given. Finally, a numerical example is provided.
A Data Collection and Prediction Methodology for Manufacturing Process: A Case Study in Food Industry
Authors: Lan Yang  , Konur Savas  , Sutcliffe Peter
Published: February 2025

This paper presents a case study, which describes a novel data collection and monitoring mechanism integrated into a food manufacturing company for the first time since they started their production over a century. Our results demonstrate this methodology provides a reliable approach to achieve a more consistent production facility. It also helps the factory understand the process and the parameters (e.g. oven temperature and environmental conditions) that significantly affect the product quality and consistency between production lines. We believe our results are make a useful contribution to the methodologies enabling intelligent manufacturing.
An Application of Stochastic Time Strength RBF Neural Network to Forecasting Spot Prices of Crude Oil
Authors: Niu Hongli
Published: February 2025

In this paper we introduce a stochastic time strength RBF neural network (ST-RBF) to prices forecasting of crude oil indices in which the network is set up by taking into consideration the impact of occurrence time for the historic data. A stochastic time-effective function is utilized to depict this, and a weight is assigned to each historic data, in which the time strength behavior is expressed through a combination of a Brownian function and a drift function. In empirical experiment, the data adopted is Chinese Daqing crude oil and Brent crude oil which is marked as one of the main global crude oil benchmarks. It is shown that the improved ST-RBF outperforms the traditional RBF neural network and increases the precision in predicting of crude oil prices.