Search Results (43,083)

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

In special relativity, particle trajectories, whether mass-bearing or not, can be traced on the Minkowski spacetime manifold in (3+1)D. Meantime, in quantum mechanics, trajectories in the phase space are not strictly outlined because coordinate and linear momentum cannot be measured simultaneously with arbitrary precision since they do not commute within the Hilbert space formalism. However, from the density matrix representing a quantum system, the extracted information still produces an imperative description of its properties and, furthermore, by appropriately reordering the matrix entries, additional information can be obtained from the same content. Adhering to this line of work, the paper investigates the definition and the meaning of velocity and speed in a typical quantum phenomenon, the disentanglement for a bipartite system when dynamical evolution is displayed in a (3+1)D pseudo-spacetime whose coordinates are constructed from combinations of entries to the density matrix. The formalism is based on the definition of a Minkowski manifold with compact support, where trajectories are defined following the same reasoning and formalism present in the Minkowski manifold of special relativity. The space-like and time-like regions acquire different significations referred to entangled-like and separable-like, respectively. The definition and the sense of speed and velocities of disentanglement follow naturally from the formalism. Depending on the dynamics of the physical state of the system, trajectories may meander between regions of entanglement and separability in the space of new coordinates defined on the Minkowski manifold. Full article

Conventional fundus (CF) and ultrawidefield fundus (UF) imaging are two primary modalities widely used in ophthalmology. Despite the complementary use of both imaging modalities in clinical practice, existing research on fundus image translation has yet to reach clinical viability and often lacks the necessary accuracy and detail required for practical medical use. Additionally, collecting paired UFI-CFI data from the same patients presents significant limitations, and unpaired learning-based generative models frequently suffer from distortion phenomena, such as hallucinations. This study introduces an enhanced modality transformation method to improve the diagnostic support capabilities of deep learning models in ophthalmology. The proposed method translates UF images (UFIs) into CF images (CFIs), potentially replacing the dual-imaging approach commonly used in clinical practice. This replacement can significantly reduce financial and temporal burdens on patients. To achieve this, this study leveraged UFI–CFI image pairs obtained from the same patient on the same day. This approach minimizes information distortion and accurately converts the two modalities. Our model employs scaled feature registration and distorted vessel correction methods to align UFI–CFI pairs effectively. The generated CFIs not only enhance image quality and better represent the retinal area compared to existing methods but also effectively preserve disease-related details from UFIs, aiding in accurate diagnosis. Furthermore, compared with existing methods, our model demonstrated a substantial 18.2% reduction in MSE, a 7.2% increase in PSNR, and a 12.7% improvement in SSIM metrics. Notably, our results show that the generated CFIs are nearly indistinguishable from the real CFIs, as confirmed by ophthalmologists. Full article

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems. Full article

The Internet of Things (IoT) has enabled a vast network of devices to communicate over the Internet. However, the fragmentation of IoT systems continues to hinder seamless data sharing and coordinated management across platforms.However, there is currently no actual search engine for IoT data. Existing IoT search engines are considered device discovery tools, providing only metadata about devices rather than enabling access to IoT application data. While efforts such as IoTCrawler have striven to support IoT application data, they have largely failed due to the fragmentation of IoT systems and the heterogeneity of IoT data.To address this, we recently introduced SensorsConnect—a unified framework designed to facilitate interoperable content and sensor data sharing among collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled shared and accessible information spaces for humans. This paper presents the IoT Agentic Search Engine (IoT-ASE), a real-time semantic search engine tailored specifically for IoT environments. IoT-ASE leverages LLMs and Retrieval-Augmented Generation (RAG) techniques to address the challenges of navigating and searching vast, heterogeneous streams of real-time IoT data. This approach enables the system to process complex natural language queries and return accurate, contextually relevant results in real time. To evaluate its effectiveness, we implemented a hypothetical deployment in the Toronto region, simulating a realistic urban environment using a dataset composed of 500 services and over 37,000 IoT-like data entries. Our evaluation shows that IoT-ASE achieved 92% accuracy in retrieving intent-aligned services and consistently generated concise, relevant, and preference-aware responses, outperforming generalized outputs produced by systems such as Gemini. These results underscore the potential of IoT-ASE to make real-time IoT data both accessible and actionable, supporting intelligent decision-making across diverse application domains. Full article

Gastro-intestinal nematodes (GINs) are still of great concern in grazing ruminants, such as camelids, ovines and caprines, affecting animal health and productivity. This is mainly due to the development of anthelmintic resistance (AR) to the compounds used long term, without much evaluation on their efficacy, including ivermectin (IVM), the most used anthelmintic drug in livestock. The aims of this study were to determine the efficacy of IVM and identify which GIN species are affecting different ruminant farms in Quebec (QC), Canada. Firstly, we collected fecal samples from six farms with different ruminant species (camelids, goats and sheep) before and after IVM treatment when applicable, analyzing them by Fecal Egg Count (FEC) and further assessments on IVM efficacy through the Fecal Egg Count Reduction Test (FECRT). In addition, molecular analyses were conducted using PCR, targeting the ITS-2 and COX-1 genes to identify GIN species. FECRT was applied only for three farms, showing that variable results with optimal efficacy (ranging from 95.5–100%) were obtained in only one farm, whereas on the other two farms, FECRT exhibited reduced efficacy, suggesting the development of IVM resistance. Among the GIN species found, Haemonchus contortus and Trichostrongylus vitrinus were identified in most of the farms, being present in sheep, goat, llama and alpaca farms, whereas Teladorsagia circumcincta was identified only in sheep and llama samples from four farms but not in alpaca samples. Trichostrongylus axei and Chabertia ovina were present in two farms (sheep and sheep and llamas). Oesophagostomum venulosum was detected in one sheep and one alpaca farm. Only one sheep farm was positive for Trichostrongylus colubriformis and Cooperia curticei. Also, Nematodirus spp. and Trichuris spp. were found in four farms, including sheep and camelids. In addition, three other species were found in camelids, including Camelostrongylus mentulatus (only in the llama samples), whereas Lamanema chavezi and Marshallagia marshalli were identified in one alpaca farm. Therefore, our work reports evidence of an uneven efficacy of IVM against GINs from ruminant farms, including the most likely emergence of IVM resistance. The diversity of GIN species found in ruminant farms in QC along with the inconsistent IVM efficacy are helpful information for veterinarians and animal producers in setting an optimal parasite management programs, including the proper use of IVM and alternative anthelmintic drugs to control these pathogens in grazing livestock. Full article

Recent studies on detecting hallucinations in summaries follow a method of decomposing summaries into atomic content units (ACUs) and then determining whether each unit logically matches the document text based on natural language inference. However, this fails to consider discourse link relations such as temporal order, causality, and purpose, leading to the inability to detect conflicts in semantic connections between individual summary ACUs, even when the conflicts are present in the document. To overcome this limitation, this study proposes a method of extracting Discourse Link-Aware Content Unit (DL-ACU) by converting the summary into an Abstract Meaning Representation (AMR) graph and structuring the discourse link relations between ACUs. Additionally, to align summary ACUs with corresponding document information in a fine-grained manner, we propose a Selective Document-Atomic Content Unit (SD-ACU). For each summary ACU, the SD-ACU retrieves only the most relevant document sentences and then decomposes them into document ACUs. Applying the DL-ACU module to existing hallucination detection systems such as FIZZ and FENICE reduces the error rate of discourse link errors on FRANK. When both modules are combined, the system improves balanced accuracy and ROC-AUC across major benchmarks. This suggests the proposed method effectively captures discourse link errors while enabling ACU-to-ACU alignment. Full article

Architectural heritage, as a highly symbolized medium of cultural expression, plays a vital role in transmitting collective memory and shaping intercultural tourism experiences. Yet, how visitors from diverse cultural backgrounds perceive and emotionally respond to Chinese architectural symbols remains insufficiently understood. This study addresses this gap by integrating architectural semiotics with cross-cultural psychology to examine perceptual differences across three visitor groups—Mainland China and Hong Kong/Macau/Taiwan (C), East and Southeast Asia (A), and Europe/North America (UA)—at eleven representative Chinese heritage sites. Drawing on 235 in-depth interviews and 1500 online reviews, a mixed-methods design was employed, combining semantic network analysis, grounded theory coding, and affective clustering. The findings reveal that memory structures and cultural contexts shape symbolic perception, that cultural dimensions and affective orientations drive divergent emotional responses, and that interpretive pathways of architectural symbols vary systematically across groups. Specifically, Group C emphasizes collective memory and identity, and Group A engages through structural analogies and regional resonance, while Group UA favors aesthetic form and immersive experiences. These insights inform culturally adaptive strategies for heritage presentation, including memory-anchored curation, comparative cross-regional interpretation, and immersive digital storytelling. By advancing a micro-level model of “architectural symbol–perceptual theme–emotional response–perceptual mechanism,” this research not only enriches theoretical debates on cross-cultural heritage perception but also offers practical guidance for inclusive and resonant heritage interpretation in a global tourism context. Full article

With the rapid advancement of artificial intelligence, the deployment of social robots has significantly broadened, extending into diverse fields such as education, medical services, and business. Despite this expansive growth, there remains a notable scarcity of empirical research addressing the underlying psychological mechanisms that influence human–robot interactions. To address this critical research gap, the present study proposes and empirically tests a theoretical model designed to elucidate how users’ multi-dimensional perceived values of social robots influence their attitudinal responses and outcomes. Based on questionnaire data from 569 social robot users, the study reveals that users’ perceived utilitarian value, emotional value, and hedonic value all exert significant positive effects on active involvement, thereby fostering their identification and reinforcing attitudinal loyalty. Among these dimensions, emotional value emerged as the strongest predictor, underscoring the pivotal role of emotional orientation in cultivating lasting human–robot relationships. Furthermore, the findings highlight the critical mediating function of active involvement in linking perceived value to users’ psychological sense of belonging, thereby elucidating the mechanism through which perceived value enhances engagement and promotes sustained long-term interaction. These findings extend the conceptual boundaries of human–machine interaction, offer a theoretical foundation for future explorations of user psychological mechanisms, and inform strategic design approaches centered on emotional interaction and user-oriented experiences, providing practical guidance for optimizing social robot design in applications. Full article

Translating knowledge into formal representation for the purpose of building an expert system is a daunting task for domain experts and requires information technology (IT) competence and software developer support. The availability of open and robust expert system shells is a way to solve this task. A new architecture of a rule-based expert system combining the digital twin paradigm and a capability-driven approach is presented in this study. The aim of the architecture is to provide a user-friendly framework for domain experts to build upon without the need to delve into technical aspects. To support this architecture, an open-source no-coding shell RecGen has been developed (Python and Django framework). RecGen was validated on a use case of an expert system for providing recommendations to reduce plate waste in schools. In addition, the article presents experiments with large language models (LLMs) by implementing a question-answering functionality in an attempt to improve the user experience while working with large expert system knowledge bases. A mean classification accuracy of 74.1% was achieved experimentally using the injection method with language prefixes. The ablation test was applied in order to investigate the effect of augmentation, injection, a linear layer size, and lowercase text on LLM accuracy. However, the analysis of the results showed that clustering algorithms would be a more suitable solution for future improvements of the expert system shell RecGen. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

Acute myocardial infarction (AMI) remains the leading cause of death worldwide, with myocardial ischemia/reperfusion injury (MIRI) emerging as a significant factor influencing patient outcomes despite timely reperfusion therapy. MIRI refers to paradoxical myocardial damage that occurs upon restoration of coronary blood flow and is driven by complex inflammatory, oxidative, and metabolic mechanisms, which can exacerbate infarct size (IS), contributing to adverse outcomes. This review explores the molecular and cellular pathophysiology of MIRI, emphasizing both its clinical and forensic relevance. The principal mechanisms discussed include oxidative stress and mitochondrial dysfunction, calcium overload and ion homeostasis imbalance, inflammatory responses, with particular focus on the NLRP3 inflammasome and cytokine pathways, and multiple forms of cell death (apoptosis, necroptosis, pyroptosis, and autophagy). Additionally, the authors present original immunohistochemical findings from autopsy cases of patients who suffered ST-segment elevation myocardial infarction (STEMI) and underwent percutaneous coronary intervention (PCI), but subsequently died. These findings underscore that successful reperfusion does not completely prevent delayed complications, like arrhythmias, ventricular fibrillation (VF), and sudden cardiac death (SCD), often caused by secondary MIRI-related mechanisms. Moreover, the case series highlight the diagnostic value of inflammatory markers for pathologists in identifying MIRI as a contributing factor in such fatalities. Finally, immunotherapeutic strategies—including IL-1 and IL-6 inhibitors such as Canakinumab and Tocilizumab—are reviewed for their potential to reduce cardiovascular events and mitigate the effects of MIRI. The review advocates for continued multidisciplinary research aimed at improving our understanding of MIRI, developing effective treatments, and informing forensic investigations of reperfusion-related deaths. Full article

Colorectal cancer (CRC) remains a leading global health challenge, with early and accurate diagnosis crucial for effective treatment. Histopathological evaluation, the current diagnostic gold standard, faces limitations including subjectivity, delayed results, and reliance on well-prepared tissue slides. Mass spectrometry imaging (MSI) offers a complementary approach by providing molecular-level information, but its high dimensionality and the scarcity of labeled data present unique challenges for traditional supervised learning. In this study, we present the first implementation of foundation models for MSI-based cancer classification using desorption electrospray ionization (DESI) data. We evaluate multiple architectures adapted from other domains, including a spectral classification model known as FACT, which leverages audio–language pretraining. Compared to conventional machine learning approaches, these foundation models achieved superior performance, with FACT achieving the highest cross-validated balanced accuracy ($93.27\%\pm 3.25\%$) and AUROC ($98.4\%\pm 0.7\%$). Ablation studies demonstrate that these models retain strong performance even under reduced data conditions, highlighting their potential for generalizable and scalable MSI-based cancer diagnostics. Future work will explore the integration of spatial and multi-modal data to enhance clinical utility. Full article

This study examines the challenges and opportunities of managing electronic waste (e-waste) in the Galapagos Islands, a globally significant yet vulnerable subnational insular jurisdiction (SNIJ). Drawing on theories of Circular Economy (CE) and Political Industrial Ecology (PIE), the research investigates the status of e-waste in the archipelago, the barriers to implementing CE practices, and the institutional dynamics shaping material flows. Using a mixed-methods approach—including archival analysis, participant observation, and semi-structured interviews with key informants from government, private, and nonprofit sectors—the findings presented here demonstrate that e-waste management is hindered by limited capital, infrastructure, public awareness, and fragmented governance. While some high-capital institutions can export e-waste to mainland Ecuador, most residents and low-capital entities lack viable disposal options, leading to accumulation and improper disposal. The PIE analysis yielded findings that highlight how institutional power and financial capacity dictate the sustainability of e-waste pathways, with CE loops remaining largely incomplete. Despite national policy support for CE, implementation in Galapagos remains aspirational without targeted financial and logistical support. This case contributes to broader discussions on waste governance in island settings and underscores the need for integrated, equity-focused strategies to address e-waste in small island developing states (SIDS) and SNIJs globally. Full article

The identification of scenes poses a notable challenge within the realm of image processing. Unlike object recognition, which typically involves relatively consistent forms, scene images exhibit a broader spectrum of variability. This research introduces an approach that combines image and text data to improve scene recognition performance. A model for tagging images is employed to extract textual descriptions of objects within scene images, providing insights into the components present. Subsequently, a pre-trained encoder converts the text into a feature set that complements the visual information derived from the scene images. These features offer a comprehensive understanding of the scene’s content, and a dynamic integration network is designed to manage and prioritize information from both text and image data. The proposed framework can effectively identify crucial elements by adjusting its focus on either text or image features depending on the scene’s context. Consequently, the framework enhances scene recognition accuracy and provides a more holistic understanding of scene composition. By leveraging image tagging, this study improves the image model’s ability to analyze and interpret intricate scene elements. Furthermore, incorporating dynamic integration increases the accuracy and functionality of the scene recognition system. Full article