Search Results (35)

Ports serve as key nodes in eco-friendly and digital logistics networks, and the volume of cargo handled continues to increase in response to growing international trade. However, the increased workload within limited spaces heightens the risk of safety accidents, and the number of casualties in port stevedoring operations has continued to rise. As the era of transition toward eco-friendly and digital paradigms unfolds, the adoption of new technologies in ports presents a strategic opportunity to enhance safety management. As of 13 May 2025, the study conducted a text-mining analysis based on research abstracts related to the keyword “New technology and port safety,” in the context of internal and external environmental changes. Specifically, a total of 639 research abstracts were collected, but 138 abstracts, which were unrelated to port safety, were excluded, and 501 abstracts from the Clarivate Web of Science database were analyzed, focusing on 2676 words that appeared at least twice. The study applied Term Frequency (TF) analysis, TF–Inverse Document Frequency analysis, Semantic Network Analysis, and Topic Modeling. The results indicate that Internet of Things emerged as a core solution for strengthening port safety management. However, challenges remain, including the prevention of security breaches, high infrastructure implementation costs, and limitations in battery life. Full article

This study examines al-Mufaḍḍaliyyāt anthology as a foundational corpus wherein pre-Islamic and early Islamic Arabic poetry emerged not only as a cultural artifact but as a generative locus for theological reflection. Through a close reading of selected poems and nuanced engagement with the figurative language specifically metaphor, personification, and symbolic narrative, the research situates poetry as a mode of epistemic inquiry that articulates religious meaning alongside Qurʾānic revelation. Drawing on ʿAbd al-Qāhir al-Jurjānī’s theory of semantic structure and metaphor, in dialogue with Paul Ricoeur’s conception of metaphor as imaginative cognition, the study proposes that poetic discourse operates as a site of “imaginative theology”, i.e., a space wherein the abstract is rendered sensorially legible and metaphysical concepts are dramatized in affective and embodied terms. The analysis reveals how key Qurʾānic themes including divine will, mortality, ethical restraint are anticipated, echoed, and reconfigured through poetic imagery. Thus, al-Mufaḍḍaliyyāt is not merely a literary corpus vis-à-vis Islamic scripture but also functions as an active interlocutor in the formation of early Islamic moral and theological imagination. This interdisciplinary inquiry contributes to broader discussions on the interpenetration of poetics and theology as well as on the cognitive capacities of literature to shape religious consciousness. Full article

Immersive technologies are increasingly acknowledged as powerful tools in sustainability education, capable of fostering deeper engagement and emotional resonance. This study investigates the potential of 360° VR storytelling to enhance learning through embodied knowledge, attitudinal change, and emotional awareness. Conducted within the EMOTIONAL project, the research explores a first-person narrative told from the perspective of a ceramic object rooted in Italian cultural heritage, designed to facilitate meaningful, affective learning. The present study addresses the following research questions: RQ1 Can 360° VR story-living narrations effectively promote embodied learning and semantic and attitudinal shifts in the context of sustainability education? RQ2 What added insights can be gained from integrating subjective assessments with physiological measures? To this end, a hybrid assessment framework was developed and validated, combining subjective self-report tools (including attitudinal scales, semantic differential analysis, and engagement metrics) with objective physiological measures, specifically Electrodermal Activity (EDA). Sixty participants, including students and entrepreneurs, experienced the immersive narrative, and a subset underwent physiological tracking to evaluate the effectiveness of the experience. The findings show that immersive storytelling can enhance emotional and cognitive engagement, producing shifts in semantic interpretation, self-perceived knowledge, and attitudes toward material culture. A convergence of high emotional engagement, embodied learning, and technology acceptance was observed, although individual differences emerged based on prior experience and disciplinary background. EDA data offered complementary insights, identifying specific moments of heightened arousal during the narrative. The study demonstrates that emotionally driven immersive narratives (supported by integrated assessment methods) can make abstract sustainability values more tangible and personally resonant, thereby fostering more reflective and relational approaches to sustainable consumption and production. Full article

The accelerated development of Artificial Intelligence (AI) capabilities and systems is driving a paradigm shift in productivity, innovation and growth. Despite this generational opportunity, AI is fraught with significant challenges and risks. To address these challenges, responsible AI has emerged as a modus operandi that ensures protections while not stifling innovations. Responsible AI minimizes risks to people, society, and the environment. However, responsible AI principles and practice are impacted by ‘principle proliferation’ as they are diverse and distributed across the applications, stakeholders, risks, and downstream impact of AI systems. This article presents a systematic review of responsible AI principles and practice with the objectives of discovering the current state, the foundations and the need for responsible AI, followed by the principles of responsible AI, and translation of these principles into the responsible practice of AI. Starting with 22,711 relevant peer-reviewed articles from comprehensive bibliographic databases, the review filters through to 9700 at de-duplication, 5205 at abstract screening, 1230 at semantic screening and 553 at final full-text screening. The analysis of this final corpus is presented as six findings that contribute towards the increased understanding and informed implementation of responsible AI. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article

This paper introduces a meta-logical framework—based on the theory of institutions (a categorical version of abstract model theory)—to be used as a tool for the formalization of the two main views regarding the structure of scientific theories, namely the syntactic and the semantic views, as they have emerged from the relevant contemporary discussion. The formalization leads to a proof of the equivalence of the two views, which supports the claim that the two approaches are not really in tension. The proof is based on the Galois connection between classes of sentences and classes of models defined over some institution. First, the history of the syntactic–semantic debate is recalled and the theory of institutions formally introduced. Secondly, the notions of syntactic and semantic theories are formalized within the institution and their equivalence proved. Finally, the novelty of the proposed framework is highlighted with respect to existing formalizations. Full article

Explainable artificial intelligence (XAI) has emerged as a crucial field for understanding and interpreting the decisions of complex machine learning models, particularly deep neural networks. This review presents a structured overview of XAI methodologies, encompassing a diverse range of techniques designed to provide explainability at different levels of abstraction. We cover pixel-level explanation strategies such as saliency maps, perturbation-based methods and gradient-based visualisations, as well as concept-based approaches that align model behaviour with human-understandable semantics. Additionally, we touch upon the relevance of XAI in the context of weakly supervised semantic segmentation. By synthesising recent developments, this paper aims to clarify the landscape of XAI methods and offer insights into their comparative utility and role in fostering trustworthy AI systems. Full article

Background: The rapid integration of generative AIs, such as ChatGPT, into industrial, process, and construction management introduces both operational advantages and emerging cognitive risks. While these models support task automation and safety analysis, their internal architecture differs fundamentally from human cognition, posing interpretability and trust challenges in high-risk contexts. Methods: This study investigates whether architectural design elements in Transformer-based generative models contribute to a measurable divergence from human reasoning. A methodological framework is developed to examine core AI mechanisms—vectorization, positional encoding, attention scoring, and optimization functions—focusing on how these introduce quantifiable “distances” from human semantic understanding. Results: Through theoretical analysis and a case study involving fall prevention advice in construction, six types of architectural distances are identified and evaluated using cosine similarity and attention mapping. The results reveal misalignments in focus, semantics, and response stability, which may hinder effective human–AI collaboration in safety-critical decisions. Conclusions: These findings suggest that such distances represent not only algorithmic abstraction but also potential safety risks when generative AI is deployed in practice. The study advocates for the development of AI architectures that better reflect human cognitive structures to reduce these risks and improve reliability in safety applications. Full article

Religion is one of the most important factors in architectural shaping. The concepts or sub-concepts that make up religion have a different language that each designer wants to explain. This language is presented semantically and syntactically through the architect and the user interprets this fiction mostly with its syntactic dimension. The findings of this study provide valuable insights into modern mosque design by establishing a connection between belief systems and architectural expressions. Moreover, the study contributes to heritage preservation efforts by proposing a framework that links historical values to contemporary practices. In this study, it is aimed to analyze the effects of the belief concepts in the Islamic religion by analyzing the semantic and syntactic dimensions in rural wooden mosque architecture. Starting from the assumption that abstract values have a language in shaping, the principle of semiotics was utilized to reach concrete results. How the concepts and principles are determined in the semantic and syntactic dimensions of semiotics are explained. In the examination of the semantic dimension, 5 concepts from the concepts of belief in the Islamic religion, namely wahdaniyet, survival, knowledge, powerand hereafter, were discussed. The syntactic dimension was analyzed under basic design principles. The semantic and syntactic dimensions of the sample wooden mosques were analyzed, interpretedand analyzed through architectural drawings, interiorand exterior visuals. These analyses provide practical strategies for translating abstract religious principles into tangible design elements, enhancing their applicability in both educational and professional contexts. As a result, the concepts that emerged in the analyzed examples and the indicators of the sub-concepts belonging to these concepts were presented. It is suggested that the determined analysis model can contribute to design education in design departments and provide convenience to designers and researchers. The model also serves as a tool for creating mosque designs that respect cultural identity while addressing contemporary needs. This research is important in terms of being a reference for the concrete expression of the concepts that we cannot see in architectural formations but we can feel that they exist. Full article

Coastal land use represents the combination of various land cover forms in a coastal area, which helps us understand the historical events, current conditions, and future progress of a coastal area. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land cover recognition, and deep learning models provide a significant possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land covers. However, recognition systems for labeling are always defined differently for specific departments, organizations, and institutes. Moreover, considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. To improve the transferability of high-level features generated by deep learning to reduce the burden of creating a massive amount of labeled data, this paper proposes an integrated framework to support semantically enriched coastal land use recognition, including foundation model-powered multi-label coastal land cover classification and conversion from coastal land cover mapping into coastal land use semantics with a vector space model (VSM). The experimental results prove that the proposed method outperformed the state-of-the-art deep learning approaches in complex coastal land use recognition. Full article

Taxonomic studies of dreams that continue to influence the dreamer’s thoughts and feelings after awakening have distinguished three types of impactful dreams: nightmares, existential dreams, and transcendent dreams. Of these, existential dreams and transcendent dreams are characterized by recurrent metacognitive appraisal of the epistemic tension between complementary (a) metaphoric (A “is” B) assertions and (b) literal (A “is not” B) assertions. Metacognitive appraisal of such complementary metaphoric and literal assertions is detectable as the felt sense of inexpressible realizations. The poesy of such inexpressible realizations depends upon the juxtaposition of a metaphoric topic and vehicle that are both “semantically dense” but at an abstract level “distant” from each other. The result is “emergence” of attributes of the metaphoric vehicle that are sufficiently abstract to be attributes also of the metaphoric topic. The cumulative effect of successive metaphoric/literal categorical transformations produces a higher-level form of metacognition that is consistent with a neo-Kantian account of sublime feeling. Sublime feeling occurs as either sublime disquietude (existential dreams) or as sublime enthrallment (transcendent dreams). The aftereffects of these two dream types are thematically iterative “living metaphors” that have abstract (but not “totalizing”) ontological import. Full article

Advances in anti-retroviral therapy (ART) have decreased mortality rates and subsequently led to a rise in the number of HIV-positive people living longer. The housing experiences of this new population of interest—older adults (50 years and older) living with HIV—are under-researched. Understanding the housing experiences and unmet needs of older people with HIV can better provide comprehensive care services for them. This study’s systematic review evaluated the peer-reviewed literature reporting housing access/insecurity/assistance/options, housing impact, and unmet needs of older individuals living with HIV in North America from 2012 to 2023. Furthermore, Latent Semantic Analysis (LSA), a text-mining technique, and Singular Value Decomposition (SVD) for text clustering were utilized to examine unstructured data from the abstracts selected from the review. The goal was to allow for a better understanding of the relationships between terms in the articles and the identification of emerging public health key themes affecting older adults living with HIV. The results of text clustering yielded two clusters focusing on (1) improvements to housing and healthcare services access and policies and (2) unmet needs—social support, mental health, finance, food, and sexuality insecurities. Topic modeling demonstrated four topics, which we themed to represent (1) a holistic care approach; (2) insecurities—food, financial, sexuality, and other basic needs; (3) access to housing and treatment/care; and (4) homelessness and HIV-related health outcomes. Stable housing, food, and healthcare services access and availability are critical elements to incorporating comprehensive, holistic healthcare for older adults living with HIV. The aging population requires high-priority policies for accessible and equitable healthcare. Clinicians and policymakers should address individual barriers, adopt a patient-centered approach, increase doctor visits, provide competency training, ensure long-term follow-up, involve families, and improve patient education in care management, contributing to HIV/AIDS geriatric care models. Full article

In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased. Full article

Diffusion models are widely recognized in image generation for their ability to produce high-quality images from text prompts. As the demand for customized models grows, various methods have emerged to capture appearance features. However, the exploration of relations between entities, another crucial aspect of images, has been limited. This study focuses on enabling models to capture and generate high-level semantic images with specific relation concepts, which is a challenging task. To this end, we introduce the Inv-ReVersion framework, which uses inverse relations text expansion to separate the feature fusion of multiple entities in images. Additionally, we employ a weighted contrastive loss to emphasize part of speech, helping the model learn more abstract relation concepts. We also propose a high-frequency suppressor to reduce the time spent on learning low-frequency details, enhancing the model’s ability to generate image relations. Compared to existing baselines, our approach can more accurately generate relation concepts between entities without additional computational costs, especially in capturing abstract relation concepts. Full article

In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions. Full article