Adaptive Software Research Articles

Process evaluation of a cross-sectoral, coordinated follow-up care of stroke patients: a qualitative study

BackgroundImplementation of interventions to improve follow-up stroke care is complex due to the involvement of various stakeholders and challenges of health care coordination. The aim of this study was to evaluate the process of implementing a cross-sectoral, coordinated follow-up care for stroke patients (the StroCare intervention).MethodsAs part of a multicenter interventional trial, this qualitative study was performed in a pre-post design with semi-structured interviews conducted with patients and health care employees. The multicomponent intervention was implemented in eight participating acute care and rehabilitation clinics. The interviews were analyzed using qualitative content analysis. Contents were coded using eight a priori defined categories (acceptability, adoption, appropriateness, feasibility, fidelity, sustainability, patient-centeredness, satisfaction with treatment, and pandemic-related effects) with the possibility of inductively developed categories.ResultsInterviews with 21 patients and 34 interviews with 23 employees were conducted. In addition to the deductive categories, three inductive categories (psychosocial implications, interconnectedness, and potential for improvement) emerged. Acceptability, adoption, and appropriateness were assessed positively before the intervention. However, poor feasibility had a negative impact on adoption and appropriateness. In contrast, outcomes related to patient care (patient-centeredness and psychosocial implications) were independent from this effect.ConclusionsSimilar to other implementation studies of stroke care interventions, implementation of eHealth Services in the StroCare project met barriers in usability and adaptability of new software. However, high adoption and appropriateness in regard to patient-centeredness, psychosocial implications, and an overall benefit for the patients supported continuation of the remaining intervention components.Trial registration The trial is registered at ClinicalTrials.gov (NCT04159324), registration date 12/11/19.

Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and monitor the tumor's response. Recent AI advances in neuroimaging have promising applications in neuro-oncology, including guiding clinical decisions and improving patient management. However, the lack of clarity on how AI arrives at predictions has hindered its clinical translation. Explainable AI (XAI) methods aim to improve trustworthiness and informativeness, but their success depends on considering end-users' (clinicians') specific context and preferences. User-Centered Design (UCD) prioritizes user needs in an iterative design process, involving users throughout, providing an opportunity to design XAI systems tailored to clinical neuro-oncology. This review focuses on the intersection of MR imaging interpretation for neuro-oncology patient management, explainable AI for clinical decision support, and user-centered design. We provide a resource that organizes the necessary concepts, including design and evaluation, clinical translation, user experience and efficiency enhancement, and AI for improved clinical outcomes in neuro-oncology patient management. We discuss the importance of multi-disciplinary skills and user-centered design in creating successful neuro-oncology AI systems. We also discuss how explainable AI tools, embedded in a human-centered decision-making process and different from fully automated solutions, can potentially enhance clinician performance. Following UCD principles to build trust, minimize errors and bias, and create adaptable software has the promise of meeting the needs and expectations of healthcare professionals.

Cognitive Agents Powered by Large Language Models for Agile Software Project Management

This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and their transformative impact on decision-making, problem-solving, and collaboration dynamics. The research leverages the CogniSim ecosystem, a platform designed to simulate real-world software engineering challenges, such as aligning technical capabilities with business objectives, managing interdependencies, and maintaining project agility. Through iterative simulations, cognitive agents demonstrate advanced capabilities in task delegation, inter-agent communication, and project lifecycle management. By employing natural language processing to facilitate meaningful dialogues, these agents emulate human roles and improve the efficiency and precision of Agile practices. Key findings from this investigation highlight the ability of LLM-powered cognitive agents to deliver measurable improvements in various metrics, including task completion times, quality of deliverables, and communication coherence. These agents exhibit scalability and adaptability, ensuring their applicability across diverse and complex project environments. This study underscores the potential of integrating LLM-powered agents into Agile project management frameworks as a means of advancing software engineering practices. This integration not only refines the execution of project management tasks but also sets the stage for a paradigm shift in how teams collaborate and address emerging challenges. By integrating the capabilities of artificial intelligence with the principles of Agile, the CogniSim framework establishes a foundation for more intelligent, efficient, and adaptable software development methodologies.

Adaptive Software Component for Predicting Energy Consumption of an Electric Vehicle on a Planned Route

This paper presents the development of an adaptive software component for forecasting the energy consumption of electric vehicles along planned routes. It includes a conceptual framework, architectural design, and modular software composition, introducing mathematical models for managing energy consumption with a model-based design approach for precise predictions and optimization. The importance of data sources such as route information, vehicle condition, and driver behavior is emphasized to create a comprehensive state vector for energy optimization. Following ISO 26262 and A-SPICE 3.1 standards, the implementation uses a model-based approach with Simulink and aligns with the V-Model for rigorous validation. The methodology details segmenting routes and optimizing energy consumption for each segment, considering driving style and environmental conditions. The gradient search method adjusts energy consumption to minimize usage while maximizing comfort and ensuring route completion. This research lays the groundwork for future advancements in predictive energy management systems for electric vehicles, with potential real-world applications. Future work will focus on refining predictive models, exploring machine learning for improved accuracy, and integrating real-time data from connected vehicle technologies for dynamic optimization.

Analysis of Soil-Structure Interaction in Raft Foundation Using Grid Analogy

Anticipating the behavior of structural elements to be designed is an inherent necessity in Civil Engineering. Within the realm of foundation structures, this need is no exception. In this context, it is pertinent to state that structural analysis softwares represents a significant advancement in this field. Its utilization enables the attainment of precise and often complex results through a variety of analysis methods. Consequently, the Grid Analogy method, through its numerical application, offers a simplified and effective approach to analyzing two-dimensional plate elements by treating them as grid elements. This study focuses on analyzing the soil-structure interaction in raft foundations, using an approach based on the Grid Analogy Method. This is achieved through an adaptation of the LAGI software, developed in Python, aimed at the structural analysis of slab floors, which now includes support for analyzing rafts supported on soil, serving as a flexible base for the structure. The Grid Analogy is employed as a technique that facilitates modeling for soil-structure interaction analysis, providing relatively accurate results and enabling a deeper understanding of raft behavior under different loading conditions and soil characteristics. Practical applications are utilized to validate the implementation of the modeling, along with comparison of results obtained from other structural analysis software. These results, which include internal force diagrams emphasizing maximum bending moments and displacements with their values, underscore the effectiveness of the adopted approach and present an innovative alternative for analyzing a crucial area within Structural Engineering.

Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images

Neuroendocrine neoplasms (NENs) arise from diffuse neuroendocrine cells and are categorized as either well-differentiated and less proliferative Neuroendocrine Tumors (NETs), divided into low (G1), middle (G2), and high grades (G3), or poorly differentiated, and more proliferative Neuroendocrine Carcinomas (NECs). Low-grade NENs typically necessitate surgical intervention, whereas high-grade ones often require chemotherapy. However, low-grade NENs may exhibit aggressive behavior. Therefore, it is crucial to precisely refine the diagnosis of NENs. This refinement is achievable when differentiation/non-differentiation is evident or when the Ki-67 or mitosis index is low. The challenge arises in cases of morphologically undifferentiated instances with a high Ki-67 percentage and/or high mitotic index. To address this challenge, we developed a Deep Learning (DL) system named NEToC, designed to differentiate between NETs and NECs using exclusively morphological information from immunohistochemistry images, without relying on Ki-67 or mitosis assessments. NEToC was developed using 95 NEN cases from the period 2015 to 2018 at Parc Tauli Hospital in Spain, comprising 588 images. Implemented as a Graphical User Interface (GUI) system, NEToC is intended for deployment in pathological departments of hospitals to perform federated supervision. We tested the performance of NEToC with 119 images that were not used during the Artificial Neural Network (ANN) training phase, and evaluated its robustness across various resolutions: 64 × 64, 128 × 128, 256 × 256, and 512 × 512 pixels. The achieved accuracies for these resolutions were 74 %, 98 %, 98 %, and 100 %, respectively, for an underrepresented NET G3 experiment, and 66 %, 89 %, 95 % and 94 % for a represented NET G3 experiment. Based on several measured performance metrics, the optimal resolution appears to be between 128 × 128 and 256 × 256 pixels, considering computational resources and accuracy requirements. However, we found that the 256 × 256-pixel resolution is more robust to classify underrepresented classes in the learning phase. These results imply that the information to discriminate between NECs and Grade 3 NETs needs to be resolved in regions with a pixel resolution of no more than 4 μm/pixel. Most of the misclassifications were false negatives, where NET G1-type images were erroneously classified as NEC-type. Our results demonstrate that a DL-based diagnostic algorithm provides a more accurate diagnosis in NEN cases where physicians face challenges. NEToC has been initially trained with and used to classify gastrointestinal NENs. Since the NEN morphology does not change among the different organs, the use of NEToC can be extrapolated to NENs from different organs. NEToC facilitates federated supervision, allowing pathologists to collect interchangeable files based on NEToC classification predictions. NEToC is an easy-to-use, adaptable software that integrates multiple ANNs to improve standardization and accuracy NEN diagnosis, opening up possibilities for combining DL and histological diagnosis in federated supervision systems. A future goal is to classify not only NETs, but also the three-tier system (NET G1, NET G2, and NET G3) based solely on tissue differentiation information.

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond

The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this work, we report an AI-based software, named Chrompredict 1.0, which based on chromatographic data of a novel mesogenic crown ether stationary phase (CESP). Its molecular design represents a significant advancement due to the unique combination of properties and binding capabilities, including the formation of a cavity, mesogenic behavior via mobile chains, and a range of polar and non-polar interactions (aromatic rings, N=N and C=O double bonds, alkyl chains, π–π interactions, and hydrogen bonding). The mesogenic phase is effective in both normal and reversed-phase chromatography, enhancing the software's adaptability across diverse datasets.Here we introduce for the first time an unprecedented scientific approach, integrating deep learning techniques with the novel CESP, which demonstrates exceptional thermal and analytical performance in both liquid chromatography modes, especially in the separation of complex hydrocarbon isomers. This ability enables the results obtained with CESP to extend across various types of stationary phases.Leveraging these insights, a comprehensive chromatographic dataset on a series of aromatic and polyaromatic molecules interacting with our CESP was used to train a Deep Learning Model (DLM). This model is embedded within a user-friendly software, Chrompredict 1.0, designed for predicting chromatographic parameters (MAE = 0.042, R² = 0.95) by selecting chemical descriptors directly from SMILES notation. It offers a deeper understanding of molecular structure and interactions through exploratory data analysis, identifying key factors affecting model accuracy and chromatographic behavior.Users can configure hyperparameters, choose from six machine learning models, and compare their performance with DLM. Chrompredict 1.0 excels in retention behavior prediction for compounds with known structures, and it accurately predicts chromatographic retention and thermal characteristics for different temperatures in HPLC and GC. The model has been successfully tested with METLIN database of 1,023 small molecules of diverse structures and polarities (R² > 0.75, error range ±7.8 s). Overall, the CESP, combined with Chrompredict 1.0, offers a robust tool for intelligent chromatographic analysis, encompassing chemo-informatics, statistical analysis, and graphical capabilities across a broad range of compounds and stationary phases.

The Software Artifacts as Knowledge Products: A Systematic Review

Software industry organizations use different knowledge that can be consolidated, as in the case of a software artifact. A software artifact represents all organizational knowledge, as well as a knowledge product, once it has the same purpose as a software artifact in those organizations. However, these organizations face difficulties to update both software artifact and knowledge products continuously. So, this article aims to identify and understand the state of the art of adaptable software artifacts as knowledge products within software industry organizations. Therefore, we carried out a systematic literature review using searches in scientific digital databases. Our results point out 51 articles grouped into six categories according to their respective goals: explicit knowledge, in the most diverse forms; overview of the software industry; knowledge products; adaptive software artifacts; explicit knowledge within organizations of the software industry; and knowledge products in organizations of the software industry. Thus, these categories sought to group related issues; however, none of them presented a clear relationship on adaptive software artifacts as knowledge products within software industry organizations. However, the main finding of this research concerns the support of adaptable software artifacts backing the registration, storage and updating of organizational knowledge. That occurs since such organizations are inserted in the context of several technological changes, in which such adaptable software artifacts would be a support to keep the knowledge available and updated.

ANALYSIS OF APPROACHES TO DESIGN ONTOLOGICAL MODELS OF AN ADAPTIVE SOFTWARE SYSTEM

The paper analyzes approaches to the design of ontological models of adaptive software systems. To evaluate the quality of the projected ontologies, specialized groups of metrics are used, which allow us to determine the shortcomings in the structure and functioning of the model. An analysis of the use of ontological models in the process of designing and developing adaptive software systems has been carried out. It was established that the ontological approach allows for a more effective presentation of the subject area, which improves the software configuration process. The use of ontological models allows flexible responses to changes in user requirements without the need to directly update the source code of the software. The process of designing ontologies based on classical and abstract approaches is presented. It was determined that the use of a classical approach to the design of ontological models could complicate the adaptation process due to the need to change the structure and reconfiguration of the system. To solve this problem, an abstract approach is proposed that ensures the dynamism of the adaptation process, without the need for a complete reconfiguration of the system. A set of specialized metrics is defined that allows for the analysis of the ontological model of the software system, the evaluation of its syntactic and semantic quality, compliance with rules and interoperability, and the possibility of expansion. A comparative analysis of abstract and classical approaches to the design of ontological models was carried out based on defined structural metrics, as well as metrics of the scheme and filling of the knowledge base. The obtained values of structural and hierarchical metrics confirm the quality and effectiveness of the proposed approaches. According to the defined metrics, the absence of cycles and entanglement in the ontological model indicates the correctness of the construction of the structure from the point of view of ergonomics and the possibility of further changing the structure and filling of the ontological model. The analyzed values of the schema metrics and the filling of the knowledge base demonstrate the better effectiveness of the abstract approach in the process of determining the software configuration, which provides faster and more correct processing of ontological SWRL rules during adaptation.

The Integration of Technology-assisted Assessment in esl (English as a second Language) Instruction

This study explores the incorporation of technology-assisted evaluation in English as a Second Language (ESL) teaching and its influence on educational achievements. The study investigates the effectiveness of using several technology tools, such as online platforms, adaptive software, and multimedia resources, to evaluate the language ability of ESL learners. Through the utilization of these tools, instructors strive to deliver interactive, captivating, and customized assessments that provide significant insights into learners' advancement and areas requiring enhancement. The study highlights the significance of accuracy in the design and execution of assessments to guarantee their validity and reliability. The text examines the role of technology in enhancing the gathering and examination of assessment data, empowering educators to make well-informed choices to improve instructional methods and to cater to the unique needs of each student. Moreover, the study explores the capacity of technology-supported evaluation to tackle issues encountered in conventional assessment approaches, such as the ability to handle large-scale assessments and ensure impartiality. Additionally, it emphasizes the importance of addressing implementation factors, such as guaranteeing availability of devices and dependable internet connectivity. In summary, this article argues in favour of incorporating technology-assisted assessment into ESL training to improve learning outcomes and promote better language proficiency among learners.

Rehab-AMD: co-design of an application for visual rehabilitation and monitoring of Age-related Macular Degeneration

BackgroundThe increasing demand for remote medical care, driven by digital healthcare advancements and the COVID-19 pandemic, necessitates effective solutions tailored to patients and healthcare practitioners. Co-design, involving collaboration between software developers, patients, and healthcare practitioners, prioritizes end-user needs. Research indicates that integrating patient perspectives enhances user experience and usability. However, its application in healthcare has been limited to small projects. This work focuses on co-designing a technological solution to enhance the monitoring and visual rehabilitation of individuals with Age-Related Macular Degeneration (AMD), a condition that significantly impacts the quality of life in people over 60. Current vision rehabilitation systems lack personalization, motivation, and effective progress monitoring. Involving patients and healthcare practitioners in the design process aims to ensure the final product meets their needs.MethodsThe project employs iterative and collaborative principles, involving a vision rehabilitation expert and two AMD patients as active users in the application’s development and validation. The process begins by establishing requirements for user accounts and rehabilitation exercises. It continues with an initial approach extended through user validation. Co-design is facilitated by specific workshops marking each project iteration, totaling four workshops, along with continuous communication sessions between experts and developers to validate design decisions. Initial requirements gathering and constant feedback from end-users, the visual rehabilitator, and patients are crucial for refining the product effectively.ResultsThe workshops produced a prototype featuring a test to monitor changes and progression and 15 visual rehabilitation exercises. Numerous patient and vision rehabilitation-driven software modifications led to a final design that is responsive and adaptive to end-user needs.ConclusionsThe Rehab-AMD pilot project aims to develop a collaborative and adaptive software solution for AMD rehabilitation by actively involving stakeholders and applying iterative design principles. Co-design in the Rehab-AMD solution proves to be a methodology that identifies usability issues and needs from the initial design stages. This approach ensures that software developers create a final product that is genuinely useful and manageable for people with AMD and the targeted vision rehabilitators.

Biophysical essentials – A full stack open-source software framework for conserved and advanced analysis of patch-clamp recordings

Background and ObjectivesPatch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. MethodsBiophysical Essentials (BPE) was developed as an open-source full stack python software for transparent and reproducible analysis of electrophysiological recordings. For validation, BPE results were compared with manually analyzed single-cell patch-clamp data acquired from a human in vitro nociceptor-model and mouse dorsal root ganglia neurons. ResultsWhile initially designed to improve time consuming and repetitive analysis steps, BPE was further optimized as a technical software solution for entire workflow processing including data acquisition, data preprocessing, normalization and visualization and of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps reproducible review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers' needs in projects involving electrophysiology techniques. ConclusionsBPE extends beyond available patch-clamp specific, open source – and commercial analysis tools in particular because of reproducible and sharable analysis workflows. BPE enables full analysis from raw data acquisition to publication ready result visualizations – all within one single program. Thereby, BPE significantly enhances transparency in the analytical process of patch-clamp data analysis. BPEs function scope is completely accessible through an easy-to-use graphical user interface eliminating the need for programing language proficiency as required by many community patch-clamp analysis frameworks and algorithms.

Incoming Inspection of Lithium‐Ion Batteries Based on Multi‐cell Testing

Incoming inspections of battery cells prior to module assembly help to ensure the quality of the battery system and prevent the installation of anomalous cells. Depending on the area of application, identifying deviations in the electrical behavior of the battery cells under test can be essential for downstream assembly processes like cell matching and algorithm adaptations of the battery management software. In this work, the use of a multi‐cell testing procedure involving differential voltage analysis, incremental capacity analysis, direct current internal resistance tests, and electrochemical impedance spectroscopy is investigated to reveal differences in cell properties and identify anomalous cells while economizing on the required cell test channels. The results obtained from 20 model‐identical 21700 cylindrical cells from four different batches demonstrate that this methodology can detect material variations, such as differing silicon and graphite content, which are not disclosed by the supplier or indicated in the data sheet. A teardown with elemental analysis of two cells from different batches is carried out as verification. Finally, prospects for potential application scenarios and raw measurement data are provided.

3D Echocardiographic Assesment of Left Ventricle Size and Function with Automated Adaptive Software in Patients with Right Heart Dominant

Introduction : Echocardiography is a fundamental tool for assessing cardiac chamber size and function, with evolving technologies like real-time three-dimensional echocardiography (3DE) and automated quantification software offering new possibilities. This study aims to validate left ventricular (LV) measurements obtained through automated quantification software against manual 3DE and 2D measurements, explore the relationship between these measurement techniques, and compare their reproducibility and analysis time. Methods : A total of 60 patients with uncorrected severe mitral stenosis (MS) and atrial septal defect (ASD) underwent echocardiography. Automated and manual 3DE measurements were compared, with subgroup analysis for software performance. Analysis times were also assessed. Results : The automated quantification software did not work in 16 patients, with notable differences in tricuspid regurgitation parameters. Automated 3DE measurements yielded larger LV volumes than manual 3DE measurements, while LV ejection fraction (LVEF) measurements were smaller. Good correlations were observed, but agreement analysis revealed significant differences between the two methods. The use of automated software significantly reduced analysis time. Conclusions : Automated quantification software can effectively measure LV parameters in patients with MS and ASD, significantly reducing analysis time. However, manual methods may still be necessary in cases with structural changes or unique anatomical conditions, emphasizing the importance of selecting the appropriate measurement technique for specific patient populations. Keywords: 3D Echocardiography, Automated Adaptive Software, Cardiac Imaging

An Intelligent Human–Machine Interface Architecture for Long-Term Remote Robot Handling in Fusion Reactor Environments

This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware interfaces, a critical requirement emerges for a flexible and adaptive software architecture based on changing situations and past experiences. The paper explores the inherent challenges associated with sustaining and upgrading RH equipment within an extended operational context. In response to this challenge, a groundbreaking, flexible, and maintainable human–machine interface (HMI) architecture named MAMIC is designed, guaranteeing seamless integration with a diverse range of RH equipment developed over the years. Embracing a modular and extensible design, the MAMIC architecture facilitates the effortless incorporation of new equipment without compromising system integrity. Moreover, by adopting this approach, nuclear facilities can proactively steer the evolution of RH equipment, guaranteeing sustained performance and compliance throughout the extended operational lifecycle. The proposed adaptive architecture provides a scalable and future-proof solution, addressing the dynamic landscape of remote handling technology for decades.

PENGEMBANGAN MEDIA PEMBELAJARAN BILANGAN CACAH BERBASIS ANDORID UNTUK SISWA SD/MI DENGAN METODE ADAPTIVE SOFTWARE DEVELOPMENT

Development of Andorid-Based Learning Media on Whole Numbers for Elementary/MI Students Using the Adaptive Software Development Method, The purpose of this study is to develop an Android-based whole number learning media application for SD/MI using the Adaptive Software Development method. The development of this application is divided into 2 iterations. Functional testing method using black-box testing technique. From the results of black-box testing, it show that all features/functions run well and smoothly. The usability method is used to see user ratings of this learning media application. There are 3 aspects which are Learnability, Flexibility, and Effectiveness. The overall value of the usability test results is 78.82%. Based on the eligibility category, the value is included in the feasible category. So that this android-based whole number learning media application is feasible to implement.

INTERNATIONALIZATION OF SOFTWARE AND ITS LOCALIZATION INTO UKRAINIAN

The article explores the effects of the internationalization process on adapting software for the Ukrainian locale, emphasizing the linguistic, communicative, and socio-cultural challenges encountered by localizers and translators. It also discusses the strategies employed to address these challenges, considering the unique characteristics of the product and the production processes involved in adapting software for various markets. The study relies on the localized versions of software from Microsoft and Google, along with an examination of their style guides. The goal of the research is to assess the impact of internationalization on the adaptation of software for the Ukrainian audience by delineating strategies and identifying key localization issues. Comparative and contextual analysis methods are used to examine the original and localized versions of the software. The findings from the analysis of supporting documentation and Ukrainian localized products demonstrate that localization is a multifaceted process designed to adapt a product based on the interaction between differenlocales. The interaction properties humans and software to meet the specific requirements and characteristics of influence the architecture and textual elements of the software, as well as the sequence of operations necessary for adaptation to specific locales. Throughout the internationalization of software and the preparation of materials for further localization, certain elements are identified as vulnerable due to linguistic, communicative, and socio-cultural factors, which can present challenges during adaptation to local needs. The linguistic issues associated with Ukrainian localization stem from the specific alphabet system and grammatical features of the Ukrainian language. Communicative aspects determine the level of formality in the software's interaction with Ukrainian users. The socio-cultural characteristics of the Ukrainian locale are the most dynamic and, therefore, may not be fully encapsulated in localization guidelines. The analysis of the auxiliary documents provided to localizers illustrates the developers' commitment to delivering high-quality localized software to Ukrainian users. This commitment is achieved by identifying and addressing the linguistic, communicative, and socio-cultural elements that play a crucial role in successful localization.

From enterprise models to low-code applications: mapping DEMO to Mendix; illustrated in the social housing domain

Due to hyper-competition, technological advancements, regulatory changes, etc, the conditions under which enterprises need to thrive become increasingly turbulent. Consequently, enterprise agility increasingly determines an enterprise’s chances for success. As software development often is a limiting factor in achieving enterprise agility, enterprise agility and software adaptability become increasingly intertwined. As a consequence, decisions that regard flexibility should not be left to software developers alone. By taking a Model-driven Software Development (MDSD) approach, starting from DEMO ontological enterprise models and explicit (enterprise) implementation design decisions, the aim of this research is to bridge the gap from enterprise agility to software adaptability, in such a way that software development is no longer a limiting factor in achieving enterprise agility. Low-code technology is a growing market trend that builds on MDSD concepts and claims to offer a high degree of software adaptability. Therefore, as a first step to show the potential benefits to use DEMO ontological enterprise models as a base for MDSD, this research shows the design of a mapping from DEMO models to Mendix for the (automated) creation of a low-code application that also intrinsically accommodates run-time implementation design decisions.

Mobile Applications for Self-Handle of Pornography Addiction

Content with pornographic nuances in the form of images, sound, and videos is widely circulating on the internet, including on social media. Teenagers have great potential to become addicted to pornographic content given the widespread use of the internet among adolescents. Pornography addiction has the potential to interfere with the physical and mental development of addicts, even a wider impact can lead to criminal cases in society, such as rape. This paper discusses the development of mobile applications that aim to help pornographic content addicts get rid of pornography addiction problems. The applications developed include a system for assessing the level of exposure to pornographic content, handling and self-care of pornographic content, and a system for detecting the user's location in solitude. The rating system was adapted from the Pornography Addiction Screening Tool (PAST). Handling and self-care pornographic content use the psychological approach of Cognitive Behavioral Therapy (CBT) which has been widely researched and used as a method for mental treatment and healing. An assessment system for the level of exposure to pornographic content and self-care is presented in the application by utilizing chatbot to increase the interactive between the user and the application. The research method uses the Design Research Methodology (DRM) while the method in developing mobile applications uses Agile models as an adaptive software development method. This application is not intended to replace the role of psychologists, but as a supporting tool that can help pornography addicts to reduce their addiction level until they recover. Through black box testing, evaluation results from a functional perspective show that this application can be used as expected.

The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this paper, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging in the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models, that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by \(13.6\% \) .