Domain Model Research Articles

Barriers and enablers toward healthy eating and weight gain among pregnant women in Vietnam: A qualitative study with analysis informed by the theoretical domains framework and COM-B model

Undernutrition and insufficient gestational weight gain can negatively affect maternal and infant health short- and long-term. In Vietnam, 50% of pregnant women lack essential nutrients, and 75% do not gain enough weight. Current interventions have limited success due to a gap in understanding their determinants. This study aimed to identify barriers and facilitators to healthy eating and weight gain among pregnant Vietnamese women. This qualitative study collected data from 20 pregnant Vietnamese women via virtual focus groups. Discussions were audio-recorded and translated into English for thematic analysis. The study utilized the Capability, Opportunity, Motivation to Behaviour (COM-B) model and Theoretical Domains Framework (TDF) to map the identified themes. Fifteen themes mapped onto nine of the 14 theoretical TDF domains, providing a comprehensive understanding of barriers and enablers to healthy eating and gestational weight gain within the COM-B model. CapabilityWomen had limited knowledge about food sources and the implications of insufficient weight gain and micronutrient deficiencies, though they exhibited high self-care and digital literacy. OpportunityThey lacked reliable online sources, had limited healthcare provider communication, spousal support, and faced cultural food beliefs and taboos. MotivationThe women understood the need for healthier lifestyles during pregnancy, but often lacked confidence in managing gestational weight and misunderstood the role of prenatal supplements. Our research identified key factors to inform future interventions to promote healthy eating and recommended weight gain during pregnancy among Vietnamese women. To be effective, interventions should focus on increasing nutritional knowledge, enhancing communication with healthcare professionals, and improving husband supports. Addressing food taboos with culturally sensitive approaches is crucial. The potential of digits` al health interventions is enhanced by factors such as self-care and digital literacy among pregnant Vietnamese women.

Automated scenario generation from Operational Design Domain model for testing AI-based systems in aviation

AbstractApplications based on artificial intelligence (AI) promise benefits, ranging from improved performance to increased capabilities in many industries. In the aviation domain, one example is the new Airborne Collision Avoidance System (ACAS X). The current investigation aims at combining ACAS X and AI to maintain its performance while decreasing the memory footprint. However, the anticipation of AI being increasingly used confronts regulators with challenges in terms of safety assurance and certification. Consequently, the European Union Aviation Safety Agency (EASA) published a concept paper for machine learning applications in aviation. Both, the Concept of Operation (ConOps) in combination with an Operational Design Domain (ODD), are listed as objectives to be met for the safety analysis. From a developer’s perspective, this raises questions on how to effectively derive the ODD from ConOps and test the given system based on the ODD description. Based on an exemplary use case of a Near Mid-Air Collision avoidance between two aircraft through the advisories of ACAS X, a highly automated framework for generating and testing synthetic data is proposed. Using this framework, 1800 Near Mid-Air Collision scenario files are created and automatically executed in the simulation environment FlightGear. Scenario-based testing is used for the logging of ACAS X advisory data and evaluating it against predefined requirements. By this approach, an efficient way of verifying system requirements and conducting automated testing based on the ODD definition is demonstrated. Throughout this process, Model-Based Systems Engineering (MBSE) is used to reduce and manage complexity. The framework in this paper enables a systematic and highly automated approach for scenario generation based on the ODD.

Acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration

This paper presents the acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration, a critical factor affecting the performance of electric motors in various applications such as automotive, aerospace, and industrial systems. The study provides a multidisciplinary approach that integrates electromagnetics, structural vibration, and acoustics, solved using the fast multipole boundary element method (FMBEM). The results summarize the validation of the analytical models and numerical simulations, offering insights into effective vibration reduction methods. The conclusions indicate that the 3-D numerical analysis using FMBEM aligns well with the analytical solution for the sound pressure in the exterior acoustic domain of the cylindrical shell model. The paper contributes valuable insights for the design of low-noise motors and the control of electromagnetic vibration and noise in electric motors.

Transient Ischemic Attack and Mild Stroke Participant Perspectives on Barriers and Enablers to Participation in Cardiovascular Rehabilitation

Background Exercise-based cardiac rehabilitation reduces the risk factors and recurrence of cardiovascular disease in patients with coronary heart disease. Despite similar risk factors, people with transient ischemic attack (TIA) and mild stroke do not attend cardiac rehabilitation. Cardiovascular rehabilitation (CVR), including TIA, stroke, and coronary heart disease, could be a solution for increasing participation. Perspectives from TIA and stroke survivors on barriers and enablers to participation in CVR are limited, and implementation strategies for CVR are currently unknown. Objectives We sought to determine (1) What were the perspectives of participants with TIA and mild stroke on barriers and enablers to uptake, engagement, and participation in CVR? and (2) What implementation strategies may influence people with TIA and mild stroke to participate in CVR? Methods In-depth semistructured interviews were conducted with CVR participants. Inductive thematic analysis was completed before deductively mapping themes to implementation frameworks, the Capability, Opportunity, Motivation, and Behavior Model and Theoretical Domains Framework. Results Twenty participants aged 73 ± 11 years were interviewed. Most were men, stroke survivors, tertiary educated, and retired. Six themes were generated to explain the barriers and enablers to CVR participation. Themes and subthemes mapped to 8 Theoretical Domains Framework domains and 5 Capability, Opportunity, Motivation, and Behavior constructs. Prioritizing participants' physical and social opportunities, reflective motivation, and physical and psychological capabilities may improve their participation in CVR. Conclusions Barriers and enablers to CVR participation were similar to those in the coronary heart disease population. Increasing access and availability of CVR for people with TIA and mild stroke should be prioritized for future implementation.

Working memory multicomponent model outcomes in individuals with traumatic brain injury: Critical review and meta-analysis.

Traumatic Brain Injury (TBI) often leads to cognitive impairments, particularly regarding working memory (WM). This meta-analysis aims to examine the impact of TBI on WM, taking into account moderating factors which has received little attention in previous research, such as severity of injury, the different domains of Baddeley's multi-component model, and the interaction between these two factors, as well as the interaction with other domains of executive functions. Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a systematic review and meta-analysis searched Google Scholar, PubMed, and PsycNET for studies with objective WM measures. Multiple meta-analyses were performed to compare the effects of TBI severity on different WM components. Twenty-four English, peer-reviewed articles, mostly cross-sectional were included. TBI significantly impairs general WM and all Baddeley's model components, most notably the Central Executive (d' = 0.74). Severity categories, mild-moderate and moderate-severe, were identified. Impairment was found across severities, with "moderate-severe" demonstrating the largest effect size (d' = 0.81). Individuals with moderate-severe TBI showed greater impairments in the Central Executive and Episodic Buffer compared to those with mild-moderate injury, whereas no such differences were found for the Phonological Loop and Visuospatial Sketchpad. These findings enhance our understanding of WM deficits in varying severities of TBI, highlighting the importance of assessing and treating WM in clinical practice and intervention planning.

A Cross-Sectional Study Investigating the Role of Meaning and Purpose in Life Among Older Individuals (≥50-Years Old) in Recovery from Alcohol Use Disorder/Problem Drinking

ABSTRACT Meaning and purpose in life (MPL) is an important domain of the recovery capital model. Yet, this important recovery support mechanism has received scant attention, particularly in relation to older individuals recovering from alcohol use disorder (AUD)/problem drinking. Framed within Viktor Frankl’s theory of meaning, the study used two measures, the Purpose in Life Test and the Meaning in Life Questionnaire, to collect the data of 381 older individuals (≥50-years old) in recovery. The data was analyzed against four independent variables: onset age of problem drinking (early- and late-onset), gender, continuous years in recovery and recovery pathways. The study found a significant relationship between MPL and continuous years in recovery, suggesting that as the length of time in recovery increases, so too, do levels of MPL.

PAYOFF-BASED PROBABILISTIC INTERACTION MODEL ON THE EVOLUTION OF COOPERATION IN SPATIAL PUBLIC GOODS GAME

In the spatial public goods game (SPGG), punishment effectively promotes cooperation but often reduces the collective benefits of cooperators and punishers. In order to increase revenue, we propose a probabilistic interaction domain model considering both strategy type and payoff level. In this model, players are divided into two types, successful players with payoffs higher than the payoff threshold and failed players with payoffs lower than the payoff threshold. A successful player is less likely to change the interaction range than a failed player. Through extensive simulations, it is shown to verify that a higher payoff threshold leads to a more pronounced promotion effect on cooperation and corresponds to a higher cooperation return. Moreover, introducing dynamic interaction domain can rapidly remove defectors from the vicinity of cooperative players on regular lattices. Reducing the payoff gap between punishers and cooperators helps mitigate the system’s second-order free-riding problem. Additionally, through analysis of the critical parameters, it is revised that incorporating diversity in interaction structures substantively enhances cooperation level.

A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States

Within the geo-simulation research domain, micro-simulation and agent-based modeling often require the creation of synthetic populations. Creating such data is a time-consuming task and often lacks social networks, which are crucial for studying human interactions (e.g., disease spread, disaster response) while at the same time impacting decision-making. We address these challenges by introducing a Python based method that uses the open data including that from 2020 U.S. Census data to generate a large-scale realistic geographically explicit synthetic population for America’s 50 states and Washington D.C. along with the stylized social networks (e.g., home, work and schools). The resulting synthetic population can be utilized within various geo-simulation approaches (e.g., agent-based modeling), exploring the emergence of complex phenomena through human interactions and further fostering the study of urban digital twins.

Polygenic risk for psychotic disorders in relation to cardiac autonomic dysfunction in unmedicated patients with schizophrenia.

Cardiac autonomic dysfunction (CADF), mainly characterized by increased heart rate, decreased heart rate variability, and loss of vagal modulation, has been extensively described in patients with schizophrenia (SCZ) and their healthy first-degree relatives. As such, it represents an apparent physiological link that contributes to the increased cardiovascular mortality in these patients. Common genetic variation is a putative underlying mechanism, along with lifestyle factors and antipsychotic medications. However, the extent to which CADF is associated with genetic factors for SCZ is unknown. A sample of 83 drug-naive SCZ patients and 96 healthy controls, all of European origin, underwent a 30-minute autonomic assessment under resting conditions. We incorporated parameters from several domains into our model, including time and frequency domains (mean heart rate, low/high frequency ratio) and compression entropy, each of which provides different insights into the dynamics of cardiac autonomic function. These parameters were used as outcome variables in linear regression models with polygenic risk scores (PRS) for SCZ as predictors and age, sex, BMI, smoking status, principal components of ancestry and diagnosis as covariates. Of the three CADF parameters, SCZ PRS was significantly associated with mean heart rate in the combined case/control sample. However, this association was was no longer significant after including diagnosis as a covariate (p = 0.29). In contrast, diagnostic status is statistically significant for all three CADF parameters, accounting for a significantly greater proportion of the variance in mean heart rate compared to SCZ PRS (approximately 16% vs. 4%). Despite evidence for a common genetic basis of CADF and SCZ, we were unable to provide further support for an association between the polygenic burden of SCZ and cardiac autonomic function beyond the diagnostic state. This suggests that there are other important characteristics associated with SCZ that lead to CADF that are not captured by SCZ PRS.

A spatially explicit drift foraging model quantifies salmon rearing habitat enhancement

Salmonid rearing habitat restoration is often intended to increase juvenile prey abundance, predator refugia, and physical conditions that enhance growth, development, and survival. A common restoration evaluation approach is to quantify habitat quality via physical metrics such as depth and velocity. However, such an approach does not account for the bioenergetic implications of habitat occupancy. We developed a spatially explicit predictive framework to assess net rate of energy intake (NREI) for juvenile Chinook salmon (Oncorhynchus tshawytscha) experiencing pre-existing and post-project conditions in a restored side channel complex. Energetic submodels from existing juvenile salmonid drift foraging literature are used in combination with site-specific macroinvertebrate drift data and a 2D hydraulic model. We address the following question: does our model predict increased net available energy for foraging juveniles in a restored channel condition compared to pre-existing conditions under varying discharges, thermal conditions, and seasonality? Observed macroinvertebrate drift data was extrapolated to the model domain via random forest models. Modeled post-project conditions showed statistically significant improvements in foraging opportunity compared to existing conditions, with depth ranked as the most important NREI predictor. The novel framework presented here improves upon previous applications that model drift from a single fish perspective and generalize prey density.

CycleGAN-based Data Augmentation for Enhanced Remaining Useful Life Prediction Under Unsupervised Domain Adaptation

Predictive maintenance is crucial for enhancing operational efficiency and reducing costs in Prognostics and Health Management (PHM). One of the key tasks in predictive maintenance is the estimation of Remaining Useful Life (RUL) of machinery. In practice, the data for different machines is not always accessible in sufficient quantity or quality, therefore the machine learning models trained on machines in one domain often perform poorly when applied to other domains due to covariate shifts. As a solution, Domain Adaptation (DA) aims to tackle domain shifts by extracting domain-invariant features. However, traditional methods often fail to adequately address the complexity and variability of real-world data. We propose to address this challenge, using a Wasserstein CycleGAN with Gradient Penalty (W-CycleGAN-GP) to learn mappings between domains and generate augmented data in the target domain from data in the source domain. We use our approach to generate realistic augmented data that bridge domain gap coupled with recent work on adversarial-based and correlation alignment-based DA models to improve the performance of RUL prediction models in target domains without having access to labeled data. The experimental results on the C-MAPSS dataset demonstrate a significant improvement in the RUL prediction score and accuracy within the target domain.

The molecular consequences of FOXF1 missense mutations associated with alveolar capillary dysplasia with misalignment of pulmonary veins

BackgroundAlveolar capillary dysplasia with misalignment of pulmonary veins (ACD/MPV) is a fatal congenital lung disorder strongly associated with genomic alterations in the Forkhead box F1 (FOXF1) gene and its regulatory region. However, little is known about how FOXF1 genomic alterations cause ACD/MPV and what molecular mechanisms are affected by these mutations. Therefore, the effect of ACD/MPV patient-specific mutations in the FOXF1 gene on the molecular function of FOXF1 was studied.MethodsEpitope-tagged FOXF1 constructs containing one of the ACD/MPV-associated mutations were expressed in mammalian cell lines to study the effect of FOXF1 mutations on protein function. EMSA binding assays and luciferase assays were performed to study the effect on target gene binding and activation. Immunoprecipitation followed by SDS‒PAGE and western blotting were used to study protein‒protein interactions. Protein phosphorylation was studied using phos-tag western blotting.ResultsAn overview of the localization of ACD/MPV-associated FOXF1 mutations revealed that the G91-S101 region was frequently mutated. A three-dimensional model of the forkhead DNA-binding domain of FOXF1 showed that the G91-S101 region consists of an α-helix and is predicted to be important for DNA binding. We showed that FOXF1 missense mutations in this region differentially affect the DNA binding of the FOXF1 protein and influence the transcriptional regulation of target genes depending on the location of the mutation. Furthermore, we showed that some of these mutations can affect the FOXF1 protein at the posttranscriptional level, as shown by altered phosphorylation by MST1 and MST2 kinases.ConclusionMissense mutations in the coding region of the FOXF1 gene alter the molecular function of the FOXF1 protein at multiple levels, such as phosphorylation, DNA binding and target gene activation. These results indicate that FOXF1 molecular pathways may be differentially affected in ACD/MPV patients carrying missense mutations in the DNA-binding domain and may explain the phenotypic heterogeneity of ACD/MPV.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

A hydraulic motor fault diagnosis method based on weighted multi-channel information fusion

Abstract In response to the limitations of the existing single-sensor hydraulic motor fault diagnosis model, which includes significant fluctuations in fault identification accuracy, low data utilization, poor reliability, and insufficient generalization ability under variable working conditions, a novel hydraulic motor fault diagnosis method based on weighted fusion of multi-channel data and migration learning is proposed. Firstly, in order to fully extract the information content in the multi-channel data set of the hydraulic motor, a multi channel-fusion method based on information entropy weighting is proposed. The information entropy method is employed to calculate the fusion weight of each channel of data, and the sampled data of each channel is weighted and fused. Subsequently, the fusion data from the source domain is employed to pre-train the depth transmission model, with the model parameters obtained from this pre-training serving as the initialization parameters for the target domain model. Subsequently, the parameters of the target domain model's feature extractor are fixed, and the parameters of its classifier are fine-tuned using the target domain's fusion data. The distance between the source and target domains is reduced by incorporating an attention mechanism and constructing a loss function. The migration from the source domain to the target domain is achieved, which enables the classifier to adapt to the novel target sample recognition task. Ultimately, the experimental results of hydraulic motor migration diagnosis under variable operating conditions demonstrate that the proposed method is an efficacious approach for hydraulic motor fault diagnosis. In comparison to conventional models such as
CNN and GRU, the proposed method exhibits superior migration diagnosis accuracy and enhanced generalization and robustness under variable operating conditions.

The First Standardized Scoring System to Assess the Welfare of Free‐Ranging Indo‐Pacific Humpback Dolphins (Sousa chinensis)

ABSTRACTThe assessment of wild animal welfare has recently gained interest and represents a novel, complementary approach to monitoring free‐ranging populations for conservation purposes. However, few attempts have been made to develop standardized welfare assessment tools for free‐ranging animals. In the present study, a preliminary scoring system for the welfare assessment of Indo‐Pacific humpback dolphins (IPHDs) was created using the Five Domains Model. During a series of online meetings, the panel established the scoring unit, the contribution of each parameter to the four physical/functional domains, the inferred mental states and impact intensity associated with each of these parameters, and the method of aggregating parameter scores within and among domains. The resulting framework includes a total of 53 parameter scores. A total of 20 welfare‐status and 33 welfare‐alerting parameters were identified; these are aggregated into two overall welfare grades for enhancement (positive experiences) and compromise (negative experiences). The panel attributed confidence scores to the intensity impact reflected by each parameter and associated mental states. As expected, these scores reflect higher confidence in welfare‐status than welfare‐alerting parameters; welfare compromise was also attributed higher confidence than welfare enhancement and Domain 1 (Nutrition) globally received higher confidence scores than other domains. As with any expert elicitation study, subjective biases likely exist; these can be reduced through further research on the suggested parameters. The framework uses current knowledge and has been developed to enable continual improvement as further evidence becomes available on various parameters. This study represents an important step in the development of a welfare assessment tool (WAT) for IPHDs. Next steps include defining measurement methods and thresholds for scoring parameter intensities, followed by reliability testing. This WAT can then be used to systematically monitor populations of IPHDs in a way that will better inform conservation measures and ensure their effectiveness.

A unified Personalized Federated Learning framework ensuring Domain Generalization

Personalized Federated Learning (pFL) allows for the development of customized models for personalized information from multiple distributed domains. In real-world scenarios, some testing data may originate from new target domains (unseen domains) outside of the federated network, resulting in another learning task called Federated Domain Generalization (FedDG). In this paper, we aim to tackle the new problem, named Personalized Federated Domain Generalization (pFedDG), which not only protects the personalization but also obtains a general model for unseen target domains. We observe that pFL and FedDG objectives can conflict, posing challenges in addressing both objectives simultaneously. To sufficiently moderate the conflict, we develop a unified framework, named Personalized Federated Decoupled Representation (pFedDR), which decouples the two objectives using two separate loss functions simultaneously and uses an integrated predictor to serve both two learning tasks. Specifically, the framework decouples domain-sensitive layers linked to different representations and design an entropy increase loss to encourage the separation of two representations to achieve the pFedDG. Extensive experiments show that our pFedDR method achieves state-of-the-art performance for both tasks while incurring almost no increase in communication cost. Code is available at https://github.com/CSU-YL/pFedDR.

ETHOS.ActivityAssure—an open-source validation framework for synthetic European activity profiles

The simulation of human behavior is an essential component in the domain of energy demand modeling. However, due to its diverse nature, it is often unclear whether a simulated behavior pattern is fitting to certain contexts. Combined with the poor availability of appropriate activity data, this makes proper validation of behavior models difficult. Existing validation approaches are limited and specialized to the respective use case, and are therefore not reusable or comparable. To address this issue, the new open-source framework ETHOS.ActivityAssure is presented for evaluation of generated activity profiles, supporting the validation of behavior models. For this purpose, an aggregated activity dataset is created from restricted European time use data and published to ensure reusability. Validation is conducted through a set of indicators and comparative plots, taking into account activity duration, frequency, and time. A categorization of person types and activities enables mapping to result categories commonly used in behavior models. The framework's capabilities are demonstrated on the residential demand model LoadProfileGenerator. The developed framework allows for consistent and reproducible validation of synthetic activity profiles targeting Europe, without requiring access to confidential data. This offers the opportunity to enhance both new and existing behavior models by identifying flaws, compare multiple modeling approaches, and thoroughly evaluate model quality for diverse target purposes.

Ready-to-use Models Built Using a Diverse Set of 266 Aroma Compounds for the Estimation of Gas Chromatographic Retention Indices for the 50%-Cyanopropylphenyl-50%-Dimethylpolysiloxane Stationary Phase.

Retention index prediction based on the molecule structure is not often used in practice due to low accuracy, the need to use paid software to calculate molecular descriptors (MD), and the narrow applicability domain of many models. In recent years, relatively accurate and versatile deep learning (DL)-based models have emerged. These models are now used in practice as an additional criterion in gas chromatography-mass spectrometry identification. The DB-225ms stationary phase (usually described as 50%-cyanopropylphenyl-50%-dimethylpolysiloxane in available sources) is widely used, but ready-to-use retention index estimation models are not available for it. This study presents such models. The models are linear and use simple constitutional MD and retention indices predicted by DL for the DB-WAX and DB-624 stationary phases as MD (we show that it is their use that allows us to achieve satisfactory accuracy). The accuracy obtained for a completely unseen hold-out test set: root mean square error 73.2; mean absolute error 45.7; median absolute error 22.0. The models were trained using a retention data set of 266 volatile compounds. All calculations can be performed using the convenient open-source software CHERESHNYA. The final equations are implemented as a spreadsheet and a code snippet and are available online: https://doi.org/10.6084/m9.figshare.26800789.

An improved frequency domain model for floating offshore wind turbines

Abstract A cost- and time-efficient design evaluation tool can assist the FOWT industry in evaluating many different concepts. An open source frequency domain model for FOWT applications shows promising results for the integral, coupled response, but clear differences to higher fidelity time-domain studies are observed. In this work, the hydrodynamic modelling is improved by using a potential flow diffraction code, and the implementation of the floating feedback mechanism has been improved. Attempts have been made to improve the mooring system modelling by implementing a lumped mass approach, and to include the excitation of a turbulent wind spectrum. A good match in the system’s motion responses is obtained in the wave frequent range when compared to time-domain results. At lower frequencies, the excitation from the turbulent wind spectrum remains to be improved on. At higher frequencies, the mooring line modelling is to be improved for reliable results. For both effects, a clear outline is set up to improve in future work. The current model is capable of accurately modelling fully coupled wave-frequent motions in combined wind-wave environments in an efficient manner. When the suggested improvements are incorporated in future work, it is expected that a good match in the low- and high-frequency ranges can be obtained as well, allowing to study the full motion- and mooring tension response. Finally the tool will give developers and designers the opportunity to evaluate a large amount of designs in a cost and time efficient manner.

From Generative AI to Objective-Driven Systems: A Paradigm Shift in Artificial Intelligence

Marwan Omar 1Illinois Institute of Technology Email: momar3@iit.edu Abstract The rapid advancement of Artificial Intelligence (AI), particularly in the domain of generative models, has led to impressive achievements in content creation and natural language processing. However, these models are inherently limited by their reliance on pattern recognition and lack of true understanding. In contrast, Objective-Driven AI offers a promising alternative by focusing on goal-oriented behavior, causal reasoning, and the development of world models. This paper explores the limitations of generative AI, highlighting its inability to grasp context, causality, and ethical considerations. It then presents the concept of Objective-Driven AI, emphasizing its potential to operate effectively in complex, real-world environments where understanding and reasoning are critical. The paper concludes with a discussion of future research directions, including advanced world modeling techniques, ethical AI, and robustness against adversarial attacks, which are essential for the further development of Objective-Driven AI systems. Keywords Objective-Driven AI, Generative AI, Causal Reasoning, World Modeling, Ethical AI, Artificial Intelligence, Adversarial Attacks, Machine Learning, Autonomous Systems