bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Compund Codes for Automatic Classification of Clinical Diagnoses.
Authors: Pakhomov Serguei V.  , Buntrock James D.  , Chute Christopher G.
Published: February 2025

Classification of diagnoses (a.k.a. coding) is the central part of current concept based medical IR systems. Some classification systems contain over 30, 000 distinct codes which makes classifying clinical documents a time consuming labor intensive and error prone process. This paper presents a simple methodology for cleaning up and reusing existing manually coded diagnostic statements mainly extracted from clinical notes to build predictive models using a sparse feature implementation of a Naïve Bayes classifier. One of the problems addressed is that diagnostic statements often contain several diagnoses and are assigned several codes resulting in a “many-to-many” mapping problem. We investigate one possible way of solving this problem by introducing compound (multiple code) categories. We present experimental results of classifying >16,000 randomly selected diagnostic strings into 19 top level categories. A small improvement (3%) with using compound categories over simple categories indicates that using multiple code categories is a promising solution, although clearly in need of further research and refinement.
Mapping Various Information Sources to A Semantic Network
Authors: Ruan Wen  , Buerkle Thomas  , Dudeck Joachim W.
Published: February 2025

Giessen Data Dictionary Server (GDDS) provides context sensitive information services to disparate clinical applications by automatically navigating a semantic network that stores medical knowledge. Mapping multiple information sources to single clinical application becomes a challenge due to different organization of semi-structured information sources. Linking huge unstructured information sources such as medical literature is even more challenging because we need to develop a mechanism to organize unstructured information and take into account the scalability issue. In this paper, two drug information sources have been mapped by developing an independent subnet for each source and interlinking them at proper nodes. For medical literature, we have demonstrated that the semantic network of the Unified Medical Language Systems and human assigned topics to each document can be used to organize the large amount of medical literature into the framework of the GDDS service.
A Tool for Abstracting Relevant Classes of Concepts: the Common Ancestry Summarizer
Authors: Tao Ying  , Mendonça Eneida A.  , Lussier Yves A.
Published: February 2025

Controlled Medical Terminologies (CMTs) are indispensable tools for present medical information systems and medical informatics researches. Concept-oriented architecture and multiplehierarchy have been accepted as two of the desiderata of contemporary CMTs. A common problem for informaticians working with class-based methods for terminologies is to find the Most Relevant Common Ancestor (MRCA) for a group of concepts, the common ancestor which has the closest semantic distance to all the given concepts. However, manually exploring the hierarchical relationships and determining the MRCA are daunting tasks due to the massive size, the multiple hierarchies and in some case the cycles of the hierarchical networks of terminologies. In our study, we developed a web-based visualization tool, the Common Ancestry Summarizer (CAS), that graphically displays the hierarchical relations of multiple concepts within CMTs, and also assist users to identify the MRCA of these concepts by identifying the Lowest Common Ancestors (LCAs). The CAS has been shown useful in organizing classes for the controlled terms used in clinical guidelines (e.g. “the bottom-up method”).
‘Knowledge Management’ for health: What ‘tools’ can improve the performance of workgroups, clinicians and patients?
Authors: Purves Ian  , Robinson Paul
Published: February 2025

This paper is a vision paper presenting a framework that describes a set of integrated ‘tools’ for supporting ‘knowledge management’. These ‘tools’ span technical, organizational and human story telling. The framework is based on the authors’ involvement in many research projects, the literature and conversations sounding the issues.
Comparison of Proportional Hazard Model and Neural Network Models in a real data set of intensive care unit patients
Authors: Suka Machi  , Oeda Shinichi  , Ichimura Takumi  , Yoshida Katsumi  , Takezawa Jun
Published: February 2025

There has been increased interest in using neural network model (NNM) for prognosis tasks. However, the performance of NNM has seldom been compared with that of traditional statistical models such as proportional hazard model (PHM) in real data sets. We conducted a comparative study of PHM and two types of NNM, that is, aggregate single point model (ASPM) and multiple point model (MPM), using a real data set of intensive care unit patients. The three models were developed using the 70% training subset and their predictive accuracy were assessed using the 30% testing subset according to classification accuracy, area under receiver operating curve, and concordance index. Overall, the highest predictive accuracy was found in MPM, followed by PHM and ASPM. MPM is likely to have the potential ability to provide more accurate estimation of prognosis than PHM and ASPM.
The BioMediator System as a Data Integration Tool to Answer Diverse Biologic Queries
Authors: Donelson Loren  , Tarczy-Hornoch Peter  , Mork Peter  , Dolan Cindy  , Mitchell Joyce A.  , Barrier M.  , Mei Hao
Published: February 2025

We present the BiobMediator (www.biomedtator.org) system and the process ofexecuting queries on it. The system was designed as a tool for posing queries across semantically and syntactically heterogeneous data particularly in the biological arena. We use examples from researchers at the University of Washington, and the University of Missouri-Columbia, to discuss the BioMediator system architecture, query execution, modifications to the system to support the queries, and summarize our findings and ourfuture directions. Finally, we discuss the system's flexibility and generalized approach and give examples of how the system can be extended for a variety of objectives.
Data Mart based Research in Heart Surgery: Challenges and Benefit
Authors: Arnrich Bert  , Walter Jörg  , Albert Alexander  , Ennker Jürgen  , Ritter Helge
Published: February 2025

For many new medical research questions in heart surgery comprehensive and large data bases are essential. We discuss typical challenges for the integration of real-time and legacy data stored in multiple unconnected hospital information systems (HIS). Furthermore the HIS are often operated by autonomous departments whose data base structures are subject to occasional modifications.
Distilling Conceptual Connections from MeSH Co-Occurrences
Authors: Srinivasan Padmini  , Hristovski Dimitar
Published: February 2025

Our aim is to contribute to biomedical text extraction and mining research. In this paper we present exploratory research on the MeSH terms assigned to MEDLINE citations. We analyze MeSH based co-occurrences and identify the interesting ones, i.e., those that are likely to be semantically meaningful. For each selected co-occurring pair we derive a weighted vector representation that emphasizes the verb based functional aspects of the underlying semantics. Preliminary experiments exploring the potential value of these vectors gave us very good results. The larger goal of this project is to contribute to knowledge discovery research by mining the knowledge that is latent within the biomedical literature. It is also to provide a method capable of suggesting cross-disciplinary connections via the pairs derived from all of MEDLINE.
A Log Likelihood Predictor for Genomic Classification of Oral Cancer using Principle Component Analysis for Feature Selection
Authors: Whipple Mark E.  , Mendez Eduardo  , Farwell D. Gregory  , Agoff S. Nicholas  , Chen Chu
Published: February 2025

DNA microarrays are powerful tools for exploring gene expression and predicting disease state. However, since the number of variables (genes) typically exceeds the number of samples (tissue specimens), many potentially spurious genes may be selected for a predictor function. Principle component analysis (PCA) can greatly reduce the high-dimensional microarray data space while retaining most of the inherent variability. We propose a methodology that uses PCA to identify a predictor vector between two mutually exclusive and collectively exhaustive classes. By projecting the training set upon this vector a distribution of projections can be computedfor each class. A log-likelihood ratio is then calculatedfor class membership. We used this methodology to classify 48 biopsy specimens as either oral squamous cell carcinoma or normal oral mucosa using oligonucleotide microarrays. The system was trained using a set of halfthe samples, and correctly predicted the membership of the other half. The three most highly positively and three most highly negative predictive genes were all keratins that are known markers of squamous cell carcinoma.
Improving Governance and Reducing Risk in Electronic Patient Record Systems: Ensuring Appropriate Competencies for Support and End-User Staff
Authors: Rigby Michael
Published: February 2025

As clinical health information systems, particularly Electronic Patient Records, become widely used, so it is important to address the Governance and Risk Reduction policies needed to ensure their safe and effective use. Greater emphasis needs to be placed on ensuring that all staff involved have relevant, recognized competencies. This paper describes two initiatives – one for health informatics staff, and one for end-user health professionals - to ensure and assess such competencies.
The Medical Appointment Scheduler
Authors: Lacson Ronilda
Published: February 2025

In order to enhance our understanding of how to best improve patient care, it was necessary to initially identify key questions that patients ask mostfrequently by recording actual calls made by home hemodialysis patients to a dialysis clinic over a threemonth period. From an initial analysis of the recorded conversations, one of the mostfrequent reasonsfor patient calls was for scheduling concerns. Since the use of automated systems often enable a quicker response to patients’ needs and alleviate time demands on the personnel that have to answer these calls, an automated scheduling system was designed and implemented to address this concern.
Innate and Learned Emotion Network
Authors: Novianto Rony  , Williams Mary-Anne
Published: February 2025

Autonomous agents sometimes can only rely on the subjective information in terms of emotions to make decision due to the inavailability of the non-subjective knowledge. However, current emotion models lack of integrating innate emotion and learned emotion and tend to focus on a specific aspect. This paper describes the underlying new computational emotion model in ASMO which integrates both innate and learned emotions as well as reasoning based on probabilistic causal network. ASMO's emotion model is compared with other models and related works and shows its practical capabilities to utilize subjective knowledge in decision making.
Contrasting Infant Perception Data with a Reinforcement Learning Visual Search Model
Authors: Pennington Nicole  , Goodrich Benjamin  , Arel Itamar
Published: February 2025

Selective visual attention is the mechanism by which we rapidly fixate on objects of interest in our visual environment. The study of visual attention is relevant to any situation in which actions are based on visual information. In this work, we propose modeling the dynamics of infant perception data with respect to the visual search process. Observation-action trajectories collected from different infant subjects are mapped to a Markov model. This data is then compared to results obtained from a reinforcement learning based visual search system. The long-term goal of this research effort is to devise a near-optimal, biologically-inspired visual attention mechanism that can be applied to large visual fields while offering speed and accuracy for diverse scene conditions.
Analytic General Intelligence: Constructing Space+Time for the Lowen Model
Authors: Surowitz Eugene J.
Published: February 2025

The Lowen Model presents a graph of cycles among macroscopic processes of the mind. Developing of that model by adopting the feedback loop as the fundamental element, we can assemble various basic structures via space+time diagrams which can be used to form large, extended systems. The introduction of diagrammatic methods drastically eases the development and expression of structural concepts. Multilayer systems are formed from the basis elements by concatenating or gluing along common links to make them of arbitrary scale. Different types of multilayered systems are generated by the choice of which sections of the basis elements are glued together. A focus-bilens multilayer contains a middle layer of nodes that are not directly connected to each other. Generating graphs within this layer is hypothesized to provide a sufficient mechanism to implement the Lowen model's capacities; their dynamics are not addressed here.
Recursive Subgoals Discovery Based on the Functional Systems Theory
Authors: Vityaev Evgenii E.  , Demin Alexander V.
Published: February 2025

The paper presents a model of adaptive behavior of an aminat based on the physiological functional system theory and Probabilistic Dynamic Logic. The main distinction of this model is the possibility for automatic generation of new sub-goals, which allows us to solve more complex multi-level tasks. Animat has been created on the basis of this model, and an experiment has been carried out in order to train it and to compare it with the existing approaches based on reinforcement learning. The results of comparison have shown that the proposed model learns and acts efficiently.
Learning categories with invariances in a neural network model of prefrontal cortex
Authors: Chelian Suhas E.  , Bhattacharyya Rajan  , O'Reilly Randall
Published: February 2025

Prefrontal cortex (PFC) is implicated in a number of functions including working memory and categorization. Here the Prefrontal cortex Basal Ganglia Working Memory (PBWM) model (O'Reilly and Frank, 2006) is applied to learning categories with invariances. In particular, motivated by a problem in scene recognition, objects in different locations are sequentially presented to the network for categorization. The model learns to recognize these classes without explicit programming, thus modeling human categorization along with characteristics such as generalization to novel sequences and frequency dependent effects. Future extensions to the current work including applications to other domains and modeling functionally distinct segregations of PFC and neuromodulatory systems are also described.
A Novel Approach for Geometric Clustering based on Tensor Voting Framework
Authors: Lombardi Gabriele  , Rozza Alessandro  , Casiraghi Elena  , Campadelli Paola
Published: February 2025

In this work we propose a novel geometric clustering algorithm based on the Tensor Voting Framework (TVF). More precisely, we propose the construction of a weighted graph by means of the information diffused by TVF during the vote casting step. This graph, which summarizes informations related to the manifold geometric structure, was used for clustering purposes. To this aim, we applied the well known Dijkstra and Ford Fulkerson algorithms to recursively separate weakly connected graph components.
A Collaborative Approach to Time-Series Prediction
Authors: Scarpiniti Michele  , Comminiello Danilo  , Parisi Raffaele  , Uncini Aurelio
Published: February 2025

In this paper a collaborative filter combination for time-series prediction is considered. The basic idea is based on a convex combination of two kernel adaptive filters with different parameters. While the convergence of one filter is fast but not accurate, the convergence of the second one is much more accurate, even if slower. The convex combination of both filters allows to reach good performances in terms of convergence and speed. Some experimental results on the prediction of the Mackey-Glass time-series demonstrate the effectiveness of the proposed approach.
Time Series Classification by Imprecise Hidden Markov Models
Authors: Antonucci Alessandro  , De Rosa Rocco
Published: February 2025

Hidden Markov models (HMMs) are powerful tools for modelling the generative and observational processes behind time series. For short sequences, the small amount of data can make unreliable the estimates returned by the EM algorithm, which is generally used to learn HMMs. To gain robustness in these cases, an imprecise version of the EM algorithm, achieving an interval-valued quantification of the model parameters can be considered instead. The bounds of the likelihood assigned to a particular sequence with respect to these intervals can be efficiently computed. Overall, this provides a time series classification algorithm. To classify a new sequence, the bounds of the likelihood associated to the HMMs learned from the supervised sequences are evaluated, and the returned class label is that of the highest-likelihood interval. If two or more of these intervals overlap and they are associated to different labels, the classifier returns multiple classes, this corresponding to a condition of partial indecision for the class of a particular sequence. An application to human action recognition shows the effectiveness of this approach in discriminating the hard-to-classify instances (those for which the classifier returns many classes) from the “easy” ones (those for which a single class, which mostly is the correct one, is returned). This suggests the opportunity of an application of the proposed approach as an useful preprocessing tool for other time series classifiers.
A unified learning space for a network of mobile neurons
Authors: Apolloni Bruno  , Bassis Simone  , Valerio Lorenzo
Published: February 2025

We add a mobility functionality to the neurons of an artificial neural network as a key expression of their intentionality of cooperating in the overall computational task of the network. We draw the main features of this functionality from mobility models developed in some macroscale biological frameworks. The goal is to improve the learning capability of the network. The main tools are a mutual positioning strategy that may be shared with other kinds of social communities and a unifying framework where the physical and cognitive aspects of the mobility are dealt with synergistically. The new connectionist paradigm we introduce is susceptible of overriding many of the current methodologies and drawbacks affecting the training of neural networks on heavy problems, including those concerning the exploitation of deep architectures. We test our network of mobile neurons on a pair of well-known benchmarks, showing interesting performances and opening further problems.
Eunomos, a Web 3.0 Legal Knowledge Management System that Helps People Make Sense of the Law
Authors: Boella Guido  , Humphreys Llio  , Violato Andrea  , Rossi Piercarlo  , Van Der Torre Leendert
Published: February 2025

The idea of making the law concrete, principled and accessible to citizens has a long and distinguished history. With the growth of the Internet, laws can now be easily accessed by most citizens, but is the law truly accessible? Or is there a ‘crisis of the law’ as citizens and organizations try to make sense of the increasing number of legislation coming from European, national and regional governments? This paper argues that as the law gets more complex, conflicting, and ever-changing, more advanced tools to make the law truly accessible are needed. The paper presents Eunomos, an integrated Knowledge Management System that enables customers to find and understand the law in their domain of interest. Eunomos has generated much interest in the financial sector. The paper discusses how Eunomos is also suitable for public organizations and citizens, particularly in view of the challenges and opportunities presented by Web 3.0 and the Internet of things.
Proper Treatment of Gaps in Legal Data and Commercial Electronic Legal Search Services
Authors: Karakullukçu Melih  , Eribol Yeşim  , Karaer Tuğba
Published: February 2025

The paper presents the technical and legal issues that an electronic legal search services has to face while dealing with the problem of missing, unreliable or quasi-official legal data. Technically the problem requires the sifting of acquired legal texts in terms of importance and reliability, and coming up with user friendly matrices and expressions to present information to the researcher that conveys texts with their accompanying degrees of ambiguities, their varying reliability and their connections to other texts in the “corpus.” Ultimately the aim is to formulate and delineate (for the user) the limits of reliability of any given legal electronic information. Moreover the initiatives carried on by the authors' on line legal service (Kanunum) to make more legal texts available electronically in Turkey from the official hand that produced these texts and actions are presented.
Composition of Services on Hierarchical Service Models
Authors: Maigre Riina  , Tyugu Enn
Published: February 2025

In this paper we define a concept of service model, and introduce hierarchical service models suitable for large service parks. We describe a method for handling higher-order workflows that we use for automatic composition of services. We give a description of a specification language suitable for representing hierarchical service models. We introduce two kinds of ontologies: service ontologies and the user ontologies, and demonstrate their usage in our experiments on Estonian e-government information system.
Augmented Reality Visualization for Guidance in Neurovascular Surgery
Authors: Kersten-Oertel Marta  , Chen Sean S.J.  , Drouin Simon  , Sinclair David S.  , Collins D. Louis
Published: February 2025

In neurovascular surgery, and in particular surgery for arteriovenous malformations (AVMs), the surgeon maps pre-operative images to the patient on the operating table to aid in vessel localization and resection. This type of spatial mapping is not trivial, is time consuming, and may be prone to error. Using augmented reality (AR) we can register the microscope/camera image of the patient to pre-operative data in order to help the surgeon better understand the topology and locations of vessels that lie below the visible surface of the cortex. In this work we describe a prototype system, developed using open source software and built with off-the-shelf hardware, for AR visualization for AVM neurosurgery. Furthermore, we consider two visualization techniques, colour-coding and chromadepth, to enhance the depth perception of vessels.
Development of a VR-Based Injection Training System Using a Standardized Patient
Authors: Kikuchi Ayano  , Nakaguchi Toshiya  , Tanabe Masahiro  , Haneishi Hideaki
Published: February 2025

We propose a VR based injection training system using Standardized Patient (SP) with an original haptic needle which can represent a haptic expression. SP is trained to realistically portray a real patient. In the proposed system, trainee can virtually puncture the SP using the haptic needle. In addition, the haptic needle can represent a haptic expression of needle insertion of the virtual anatomical model. By using the proposed system, trainee can feel virtual puncture as well as operating for real patient.
Abnormal Red Blood Cells Detection Using Adaptive Neuro-fuzzy System
Authors: Babazadeh Khameneh Nahid  , Arabalibeik Hossein  , Salehian Piruz  , Setayeshi Saeed
Published: February 2025

Features like size, shape, and volume of red blood cells are important factors in diagnosing related blood disorders such as iron deficiency and anemia. This paper proposes a method to detect abnormality in red blood cells using cell microscopic images. Adaptive local thresholding and bounding box methods are used to extract inner and outer diameters of red cells. An adaptive network-based fuzzy inference system (ANFIS) is used to classify blood samples to normal and abnormal. Accuracy of the proposed method and area under ROC curve are 96.6% and 0.9950 respectively.
Step-Based Cognitive Virtual Surgery Simulation: An Innovative Approach to Surgical Education
Authors: Oliker Aaron  , Napier Zachary  , Deluccia Nicolette  , Qualter John  , Sculli Frank  , Smith Brandon  , Stern Carrie  , Flores Roberto  , Hazen Alexes  , McCarthy Joseph
Published: February 2025

BioDigital Systems, LLC in collaboration with New York University Langone Medical Center Department of Reconstructive Plastic Surgery has created a complex, real-time, step-based simulation platform for plastic surgery education. These simulators combine live surgical footage, interactive 3D visualization, text labels, and voiceover as well as a high-yield, expert-approved testing mode to create a comprehensive virtual educational environment for the plastic surgery resident or physician.
Personal Low-Cost Ultrasound Training System
Authors: Pedersen Peder C.  , Skehan Daniel
Published: February 2025

To meet the challenge of making realistic training opportunities in medical ultrasound readily available, a PC based low cost personal ultrasound training system has been developed. The training experience is provided by scanning a generic, curved and compliant scan surface with a sham transducer, containing position and orientation sensors, while the PC displays both a virtual subject and a virtual transducer, along with an ultrasound image.
Detail-on-Demand Visualization for Lean Understanding of Lung Abnormalities
Authors: Raghunath Sushravya  , Rajagopalan Srinivasan  , Karwoski Ronald A.  , Larson Alan G.  , Bartholmai Brian J.  , Robb Richard A.
Published: February 2025

In some respects, the lung is an anatomical bog - having limited referential landmarks. Nonetheless, precise understanding of the abnormalities that inflict this organ is crucial to effective clinical diagnosis and treatment. However, wading interactively through a three-dimensional scan of the lung poses a visual quagmire to the radiologist, resulting in significant interpretive differences due to inter and intra observer variation. Despite the continuing progress in quantitative imaging, lack of unambiguous visualization with accurately, relevant cues severely hinders the clinical adoption of many computational tools. We address this unmet need through a lean visualization paradigm wherein information is presented hierarchically to provide an interactive macro-to-micro view of lung pathologies. At the macro level, the structural and functional information is summarized into a synoptic glyph that is readily interpreted and correlated to a priori known disease states. The glyphs are “patho-spatio-temporally” tagged to facilitate navigation through the level-of-detail scales, down to the micro level values in the image voxels, providing quantitative interpretation of tissue type and the confidence level in the quantitation. A novel volume compositing scheme is proposed to specify and guide to the optimal site for surgical lung biopsy. This intuitive, interactive interface for rapid and unambiguous navigation towards the clinical endpoint harnesses the power of bio-informatics technology to provide an efficient, clinically relevant and comprehensive summary of pulmonary disease, including precise location, spatial extent and intrinsic character.
Three-Dimensional Micro-Imaging (μCT) Based Physical Anatomic Teaching Models: Implementation of a New Learning Aid for Routine Use in Anatomy Lectures
Authors: Wulf Joerg  , Rohde Isabelle  , Koppe Thomas  , Winder Robert John
Published: February 2025

The aim of our project is the development and implementation of μCT based plastic anatomical models to make them commercially available as an anatomy learning tool for students at all levels. High resolution μCT of human ossicles and trabecular bone was performed. After image processing, application of rapid prototyping and rapid manufacturing technologies, enlarged physical teaching models, magnified by a factor of 20, were built and provided an accurate representation of the human anatomy.
Hepatocellular carcinoma of the liver
Authors: Block Timothy  , Mehta Anand S.  , London W. Thomas
Published: February 2025

Primary cancer of the liver, hepatocellular carcinoma (HCC), is an extremely deadly cancer, with very poor 5 year survivals, following diagnosis. The poor outcomes are believed to be due, in part, to the late times in which the cancers are usually first detected. Improved methods for early detection have thus become a top priority in the management of liver cancer. This Chapter reviews current methods of detection as well as leading new methods. Possible explanations as to why there are so many markers that are being discovered, but so few that make it to validation are discussed.
Modeling Price Volatility on Agricultural Markets: The Momagri Modular Approach
Authors: Munier Bertrand  , Briand Anne
Published: February 2025

This paper argues, on the basis of statistical evidence of recent sharp volatility increases in agricultural commodity markets, that such financialized markets should be modeled differently than simple markets for physical goods. The Momagri 2 Model has been designed as a multi-regional modular model. The originality of it lies in the fact that it connects a microeconomic out of equilibrium market module, characterizing agricultural price behavior on the basis of their specific sources of uncertainty, to a macroeconomic computable constrained general equilibrium model. The emphasis is laid on market microstructure, heterogeneity of boundedly rational agents, and the specifics of agricultural markets. Simulations show that under conservative parametric specifications, the integrated model produces strong price volatility of the magnitude and scope experienced in the first decade of the 21st Century. A scenario of markets liberalization increases, rather than decreases, the volatility of commodity prices. We argue that producer's boundedly rational expectations as well as the ones of short term investors, together with market's structures and risk attitude account for the largest part of this volatility. It is suggested that the modular approach used in this paper be applied to all “financialized” markets. Some characteristics of it should indeed also apply to general economic modeling.
Analysis of Metrics for the Usability Evaluation of Electronic Health Record Systems
Authors: Kopanitsa Georgy  , Tsvetkova Zhanna  , Veseli Hasan
Published: February 2025

Electronic health records are gradually replacing conventional paperbased health records. For a doctor, it is a working instrument, which can significantly reduce the time spent on paper work. At the same time, patients can benefit from accessing the electronic health records even though they usually do not have a medical background. Therefore, when specifying a graphical user interface (GUI) it is necessary to take into account the requirements of the different users: e.g. the functionality for the doctors and the presentation of data in an understandable manner for the patients. The study aims to review and analyze metrics used to evaluate the usability of user interfaces in health information systems. A literature review was performed to identify existing metrics. The scope of the search included the analysis of existing usability evaluation metrics that are applied both in healthcare and other domains, where standards for storage and presentation of information are applied. The analysis focused on metrics that are applicable for evaluating GUIs of health information systems. Several approaches and standards have been studied. Finally, a set of metrics and evaluation methods that provide holistic evaluation facilities for graphical user interfaces has been identified.
A System Architecture for Sharing De-Identified, Research-Ready Brain Scans and Health Information Across Clinical Imaging Centers
Authors: Chervenak Ann L.  , van Erp Theo G.M.  , Kesselman Carl  , D'Arcy Mike  , Sobell Janet  , Keator David  , Dahm Lisa  , Murry Jim  , Law Meng  , Hasso Anton  , Ames Joseph  , Macciardi Fabio  , Potkin Steven G.
Published: February 2025

Progress in our understanding of brain disorders increasingly relies on the costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, the clinical interpretation of brain scans benefits from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. This paper describes the design, deployment, and operation of a federated imaging system that captures and shares standardized, de-identified clinical brain images in a federation across multiple institutions. In addition to describing innovative aspects of the system architecture and our initial testing of the deployed infrastructure, we also describe the Standardized Imaging Protocol (SIP) developed for the project and our interactions with the Institutional Review Board (IRB) regarding handling patient data in the federated environment.
A Flexible, Open, Decentralized System for Digital Pathology Networks
Authors: Schuler Robert  , Smith David E.  , Kumaraguruparan Gowri  , Chervenak Ann  , Lewis Anne D.  , Hyde Dallas M.  , Kesselman Carl
Published: February 2025

High-resolution digital imaging is enabling digital archiving and sharing of digitized microscopy slides and new methods for digital pathology. Collaborative research centers, outsourced medical services, and multi-site organizations stand to benefit from sharing pathology data in a digital pathology network. Yet significant technological challenges remain due to the large size and volume of digitized whole slide images. While information systems do exist for managing local pathology laboratories, they tend to be oriented toward narrow clinical use cases or offer closed ecosystems around proprietary formats. Few solutions exist for networking digital pathology operations. Here we present a system architecture and implementation of a digital pathology network and share results from a production system that federates major research centers.
Technical support for Life Sciences communities on a production grid infrastructure
Authors: Michel Franck  , Montagnat Johan  , Glatard Tristan
Published: February 2025

Production operation of large distributed computing infrastructures (DCI) still requires a lot of human intervention to reach acceptable quality of service. This may be achievable for scientific communities with solid IT support, but it remains a show-stopper for others. Some application execution environments are used to hide runtime technical issues from end users. But they mostly aim at fault-tolerance rather than incident resolution, and their operation still requires substantial manpower. A longer-term support activity is thus needed to ensure sustained quality of service for Virtual Organisations (VO). This paper describes how the biomed VO has addressed this challenge by setting up a technical support team. Its organisation, tooling, daily tasks, and procedures are described. Results are shown in terms of resource usage by end users, amount of reported incidents, and developed software tools. Based on our experience, we suggest ways to measure the impact of the technical support, perspectives to decrease its human cost and make it more community-specific.
Trust, Self-organisation and Complexity in Digital Space
Authors: Bus Jacques
Published: February 2025

The concepts of trust and security are deeply embedded in our society and are therefore strongly affected by the societal transformation caused by the digitisation. Societal and technical change is strongly influenced by the growing complexity of society related to the emergence of easy worldwide communication, the Web and mass data collection. In this paper I discuss security and trust as fundamental drivers for self-organisation in our society. I highlight the concepts of trustworthy technology and trust in the societal context, as well as the difference between accepting technology and trusting technology. An important observation is that a complex system cannot be fully understood by reductionism alone. The discussion leads to some cautious conclusions on future actions.
Trust in the Cloud through Openness and Standards
Authors: Paulus Sachar
Published: February 2025

Two important ingredients to develop trust are, besides of course required security technologies, openness and standards. The article gives an overview on how these two concepts influence the trust development, and on their current status in the Cloud.
Defend the Web
Authors: Berners-Lee Tim  , Halpin Harry
Published: February 2025

As the power of the Internet and Web increases, the motivation of some governmental and corporate actors to control and limit the Internet and Web becomes stronger. We must ask whether the ability to participate in this empowered, connected society should be considered worthy of protection, and how we can conceptualize this protection. As access to the Internet provides new capabilities that become constitutive of human thought and social life, we conclude that urgent action is needed in promotion and defense of the Internet and the Web.
Influence of Various Factors on Mechanical Properties of Adhesive Joint in Glass Structures
Authors: Machalická Klára  , Eliášová Martina
Published: February 2025

Ability of adhesive connection to distribute stress in uniform manner due to relative large bonding area is a significant advantage in brittle and stress concentration sensitive glass. For the glued joint design there are many aspects which have an essential influence on shear load carrying capacity and behaviour of joint under increasing load. The shear strength of adhesive connection is affected by correct choice of glue for specific joint in the first place, but there are many other important factors which have to be taken into the consideration during adhesive joint design. Research is focused on effect of thickness of adhesive layer, different joining materials including their surface preparation and surface roughness and exposure to environment conditions for several chosen adhesives with different mechanical properties.
Contact Damage Near the Supporting Pillars in Vacuum Glazing Units
Authors: Bao Minxi  , Yang Jian  , Liu Xiaogen  , Bao Yiwang
Published: February 2025

The contact damage caused by the supporting pillar on the glass surface in vacuum glazing units is investigated. XFEM numerical modeling is employed to simulate the cone crack initiation and propagation. The critical indentation loads in localized uniform pressure and that for a cylindrical indenter are calculated and compared. The stress distributions due to the indenters of different geometries are investigated. In light of the numerical results, an improved design of supporting pillar is recommended, which is able to mitigate the severe concentration at the contact rim.
Glazing Under Long Term Loads Induced by Water
Authors: Pistora Arthur  , Siebert Barbara
Published: February 2025

In modern architecture, swimming pools are often equipped with underwater windows. The swimmer gets the possibility to have a look outside or inside the house, while he's enjoying his time in the water. Additionally the light can enter the building through the water. For some projects our office, Ingenieurbüro Dr. Siebert, made the engineering design and the static calculation. This paper shows by presenting these projects, how to add value to a pool through clever use of glass and what has to be considered from a static and an engineering point of view. For the two projects in Germany a special building permit from the building authorities was needed.
REACT4C – Climate Optimised Flight Planning
Authors: Matthes Sigrun
Published: February 2025

The European Project REACT4C, coordinated by the Institute of Atmospheric Physics, German Aerospace Agency (DLR), addresses inefficiencies which exist in the aviation system with respect to fuel consumption and emissions by investigating the potential of climate-optimised flight routing for reducing the atmospheric impact of aviation.
Inauguration Speech by Antonio Vázquez
Authors: Vázquez Antonio
Published: February 2025

The need for efficiency will lead to the coordination of different means of transport; environment is one of the major challenges affecting the growth of aviation industry; Air Transport will only be able to meet its emission reduction targets if sustainable alternative fuels are deployed.
Behavourial Science Modelling of Security in Airports: BEMOSA
Authors: Kirschenbaum Alan
Published: February 2025

The European R&TD project called BEMOSA – Behavioural Modelling for Security in Airports – aims at developing a dynamic and realistic model of social behaviour and security decision making to threats in airports. These objectives will be accompanied by advancing the state-of-the-art in behavioural modelling. By examining airports as complex organisations throughout Europe and focusing on key decision making groups such as control tower operators, security employees, service vendors and passengers, BEMOSA will deliver the basis for a comprehensive and practical training programme that considers all the airport security stakeholders. Presently a mock-up model is being evaluated and refined as a generic format for the training modules.
SOFIA: Flight Automation as a Safe Countermeasure for Potentially Hostile Aircraft
Authors: Herrería García Juan-Alberto  , Bueno Gómez Jorge
Published: February 2025

The SOFIA project (Safe Automatic Flight Back and Landing of Aircraft) is a response to the challenge of developing techniques enabling the safe and automatic return of an airplane to ground in the event of hostile actions. SOFIA validates the Flight Reconfiguration Function (FRF), which takes the control of the aircraft and manages to safely return it to ground under a security emergency (e.g. hijacking), disabling the control and command of the aircraft from the cockpit. This means to create and execute a new flight plan towards a secure airport and landing the aircraft at it. The flight plan can be generated on ground or in a military airplane and transmitted to the aircraft, or created autonomously at the own FRF system. For the validation of the FRF, SOFIA uses flight and Air Traffic Control simulators and General Aviation aircraft. SOFIA designs architectures for integrating the FRF system into several typologies of avionics; develops one of these architectures; and finally validates, following the European Operational Concept Validation Methodology (E-OCVM), the FRF concept and the means to integrate it in the current Air Traffic Management (ATM) system.
SUPRA – Simulation of Upset Recovery in Aviation
Authors: Groen Eric  , Fucke Lars
Published: February 2025

Loss of control in flight is the leading cause of fatal accidents in commercial aviation today. Due to infrequent nature of upset events in operations, many commercial pilots have never experienced such a situation. So, aircrews may not be proficient in dealing with such events, a fact which calls for specific upset recovery training. For many reasons, ground-based simulation of these extreme conditions might be the only viable option for effective and cost-efficient pilot instruction. Due to the lack of validated aircraft models outside the normal operating envelope current flight simulators do not adequately represent aircraft behavior at the edge of the envelope. The European FP7 project called “SUPRA” – Simulation of UPset Recovery in Aviation – aims at filling this gap by extending dynamic simulation models beyond the current state-of-the-art.
The effect of Frontpacks, Shoulder bags and Handheld bags on 3D back shape and posture in young university students: An ISIS2 study
Authors: Bettany-Saltikov J.  , Cole L.
Published: February 2025

Background: Students at school and university settings have been shown to carry heavy loads in a variety of pack systems. Both type and mode of load carriage have been shown to cause significant postural adaptations that can lead to injuries in the shoulder, arms, hands and back. Whilst backpacks have been well researched, there is a paucity of literature on the effects of frontpacks, shoulder bags and hand-held bags on 3D posture and back shape.
Variations in bioelectric activity during symmetric loading and asymmetric stretching of paraspinal extensors in young adult women with mild single curve scoliosis
Authors: Chwała Wiesław  , Płaszewski Maciej  , Kowalski Paweł
Published: February 2025

Introduction Symmetrical loading and asymmetrical stretching of trunk extensors are applied in scoliosis physiotherapy management. Conflicting evidence is available on trunk erectors' bioelectrical activity during such static exercise, especially in adult subjects. Our purpose was to identify the profile of bioelectrical activity of trunk extensors during static contractions against symmetrical loading with body mass in young adult females with single curve scoliosis, who participated in adolescence in a scoliosis-specific physiotherapy program.
Present day explanation of the clinical signs in the biomechanical aetiology of the so-called idiopathic scoliosis (1995–2011). The relationship between the “model of hips movement” and the character of scoliosis; three groups and four t
Authors: Karski Tomasz
Published: February 2025

The observations from 1985-1995 and till 2012 clarify that the development of so-called idiopathic scoliosis is connected with “gait” and habitual permanent “standing at ease” on the right leg. The scoliosis is “a result” of asymmetry of “function” – “changed” loading during gait and asymmetry in time during ‘at ease’ standing, more prevalent on the right leg. Every types of scoliosis is connected with the adequate “model of hips movements” [MHM] (Karski et al., 2006 [1]). This new classification clarifies the therapeutic approach to each types of scoliosis and provides the possibility to introduce causative prophylaxis.
Photodynamic Impact on the Epiphyseal Plates
Authors: Kurchenko S.  , Shashko A.  , Dudin M.  , Mikhailov V.  , Netylko G.  , Ashmarov V.
Published: February 2025

This study was carried out to prove the possibility of inhibition of long bones epiphyseal plates activity with photodynamic impact. Comparative analysis of the Chlorin E6 accumulation with transcutaneous and intraperitoneal administration mode, carried out on 175 laboratory mice showed the drug accumulates selectively in the epiphyseal plates of long bones, regardless of the mode of administration. 15 mice (males and females) at the age of active grownig were subjected to the single laser radiation impact on the knee joints area: 5 ones with transcutaneous Chlorine E6 administration, another 5 ones with intraperitoneal administration and the rest 5 without the drug. Histological samples of 15 experimental mice epiphyseal plates were examined by light microscopy, compared with 10 intact control mice. Influence of the laser radiation without administration of Chlorin E6 leads to intracellular swelling of epiphyseal plates chondrocytes. Influence of the laser radiation after transcutaneous or intraperitoneal injection of Chlorine E6 reduces significantly the total number of epiphyseal plates chondrocytes, without reducing the proportion of terminally-differentiated chondrocytes. Thus, the photodynamic impact inhibits the activity of epiphyseal plates of the mice.